👋🏽 Hey, it’s Ismail. Welcome to data nomads lab newsletter on learning data analytics, career growth, networking, building portfolios, and interview skills to break into tech role as a high-performer.

Hey friends 👋

It has been a while since the last issue.
So today I want to restart Data Nomads Lab with a reminder of why we care about data in the first place.

Because data analytics is not only about SQL queries or dashboards.
It is about understanding the world and how decisions get made behind the scenes in places we already love like streaming, sports, food, and education.

Let us take a short and fun tour together 👇

Streaming: how Netflix knows what you will watch next

Modern streaming platforms such as Netflix and YouTube track every tiny action.
Every click, every pause, every late night “just one more episode.”

All of that data helps them learn what we enjoy and what to recommend next.

In 2018, YouTube revealed that about 70% of what people watch comes from recommendations.
That is not luck. That is data in motion.

Old television networks relied on a small group of households that had Nielsen devices installed to measure ratings.
Imagine trying to guess what millions watched based on a few homes.

The more precise the data, the smarter the decision.

Sports: how the NBA turned numbers into wins

Basketball used to be ruled by instinct and tradition.
Now it is shaped by data.

In the late 1990s, most players shot from midrange, which looked good but was not the most efficient choice.
By 2019, data showed that the most valuable shots came from near the rim or beyond the three point line.

Players adapted.
A single step backward could turn a two point shot into three points with nearly the same effort.

Data did not just change how fans watch the game. It changed how athletes play it.

Product design: how HelloFresh personalizes your plate

Meal kit companies like HelloFresh use data to predict what customers will enjoy before they even order.

They gather information about:

• Customer preferences such as taste and location

• Recipe details like ingredients and preparation time

• Order patterns, browsing data, and feedback

With these layers combined, HelloFresh can recommend recipes, reduce waste, and design promotions that fit each household.

Great product design begins with great data and empathy for the person behind it.

Government and education: data for the public good

Data analytics also helps public institutions improve experiences for everyday people.

For example, the University of St Andrews used heat maps to study how visitors interact with its website.
Red spots showed where users clicked the most, while blue areas were barely touched.

They discovered that people were trying to click on non interactive text and missing important sections further down the page.
The team redesigned the site so key information appeared higher and links were easier to find.

Data can help any organization serve people better, one click at a time.

Logistics and legacy: how Coca Cola and UPS use data to stay consistent

Not every company needs to reinvent itself every year.
Some businesses like Coca Cola and UPS have built their success by perfecting a single core promise.

Coca Cola focuses on consistent flavor and emotion.
UPS focuses on getting packages where they need to go on time.

Behind both, data quietly runs the show.
They analyze routes, weather patterns, delivery times, production costs, and even driver efficiency.

Data is not only about innovation. It also keeps what already works running smoothly.

Takeaway: the world runs on data and you can too

Whether your passion is sports, fashion, technology, or government, there is space for your skills as a data analyst.

The real power of this field is not in knowing every tool or coding language.
It is in turning raw numbers into understanding.

That is what makes data analysts valuable in every industry.
We help translate confusion into clarity.

So if you are learning Excel, SQL, or Tableau, remember this:
The tools are your passport.

The real adventure begins when you apply them to something you care about.

Coming next in Data Nomads Lab

The lifeblood of analytics: understanding data itself
We will explore what data really is, where it comes from, and how analysts make sense of it all.

If you are new here, welcome.
If you have been subscribed for a while, thank you for your patience and support.

I am excited to be back, writing again and learning together with you.

💌 Support Data Nomads Lab
If you enjoyed this issue, please share it with a friend or colleague who loves learning about data.
Your support helps me keep writing and creating free content that inspires more people to explore data analytics.

Stay curious,

Share

👋🏽 Hey, it’s Ismail. Welcome to data nomads lab newsletter on learning data analytics, career growth, networking, building portfolios, and interview skills to break into tech role as a high-performer.

From dishwasher to data: A journey for the self-taught

I left Somalia in 1991 with my siblings and mother after the civil war broke out. We lived eight years in Cairo, Egypt before immigrating to Tucson, Arizona twenty six years ago. Like many immigrants, my journey started with uncertainty, language barriers, and learning everything from the beginning.

My first job in the United States was as a dishwasher at a local resort. I worked in call centers. I sold random things on eBay and Amazon to make extra money. I made a living selling engineering textbooks at a lower price at my university than the campus bookstore. I found a way to obtain a resale license as a vendor directly from the publishers and passed the savings to students. That experience taught me how to spot opportunities, negotiate, and think like a problem solver.

It was not a straight path. It was not always smooth. But it taught me how to keep going.

This newsletter is for anyone trying to break into the data and tech world, especially if you do not have a traditional college degree or if you are learning on your own. Whether you are starting with Excel or dreaming of building machine learning models, I hope this helps.

My career path: The real version

I studied Optical Science and Engineering at the University of Arizona. After graduating, I stepped away from the field to help my family run a small business. It felt like a detour, but I learned real world lessons about problem solving, negotiation, communication, and building trust.

Four years later, I returned to school for a Master’s degree in Management Information Systems and eventually earned an MBA from the University of Illinois. Along the way, I transitioned into data science, then product management. Today, I work in research and strategy at a major aerospace and defense company. I also teach business analytics and machine learning at Udacity dot com and write this newsletter, Data Nomads Lab.

It was not easy. I doubted myself. I started over more than once. But I kept moving.

Three decisions that changed everything

Looking back, there were three key decisions that shaped my career.

One. I said yes before I felt ready. When I moved into data science, I did not know all the tools. I said yes anyway and figured it out as I went.

Two. I built things on my own. I created simple projects, tested ideas, and shared them with others. Even if they were not perfect, they helped me grow.

Three. I taught others and wrote about what I was learning. Teaching helped me understand topics better. Writing made my thinking clearer. These habits gave me more growth than any job title ever could.

If you are learning data today, here is what I would tell you

You do not need permission. Just start. Even if it is a small project. Even if it is messy. Start now.

Do not only tell people what you know. Show them. Create a dashboard. Clean a dataset. Write about it. Publish it.

Reach out. Ask questions. Comment on posts. Email someone you admire. Your network grows one conversation at a time.

Pick a skill that can grow with you. It could be SQL, Excel, Tableau, or Python. Get really good at it. Let people know you for it.

Learn in public. Teach others. Share your journey. You will grow faster than you think.

Most of all, do not follow the crowd. Build your own path.

Challenges I faced but rarely talked about

I did not always feel like I belonged in tech. I had to relearn everything after switching careers. I often felt behind or out of place.

But I kept showing up. I stayed curious. I stayed useful. That helped me grow.

What skills will matter in the next five-ten years

Artificial intelligence is everywhere now. You do not have to be an expert, but you need to understand how it works and how to use it. Learn how to ask better questions and use tools that save you time.

But do not forget the basics.

• Communicate clearly.

• Think independently.

• Work well with others.

These skills always matter.

How I continue to grow

I keep learning. I build small projects. I write this newsletter. I teach. I ask questions. I talk to people who are learning just like me.

You do not need to have it all figured out. Just stay in motion.

If you are reading this, you are already on your way.

Stay curious. Stay useful.

Ismail

If this lesson helped you, please share and support Data Nomads Lab on Substack. Your support helps keep this content free and accessible to learners around the world.

Share

👋🏽 Hey, it’s Ismail. Welcome to data nomads lab newsletter on learning data analytics, career growth, networking, building portfolios, and interview skills to break into tech role as a high-performer.

In Lesson 4, we explored SQL aggregations—learning how to count, sum, average, and group our data to get big-picture insights.

If you missed that one, you can go back and check it out

but here’s a quick refresher: aggregations help us summarize data across multiple rows, answering questions like “What's our average sale amount?” or “How many customers do we have in each region?”

But what if your questions get more complex?

What if you need to find songs that are longer than the average length? Or

customers who spend more than most other customers? Or

artists who have released more albums than usual?

This is where SQL subqueries come in—queries nested inside other queries that help us answer multi-step questions with a single SQL statement.

Think of subqueries like a helper who runs off to find some information, then brings it back so you can use it to answer a bigger question.

It’s like asking “Which songs are longer than average?” where you first need to know “What's the average song length?”

Why Subqueries matter

As data users, we often need to:

• Compare individual items against averages (like finding songs longer than the average length)

• Filter data based on information from other tables (like finding all songs by a specific artist)

• Answer questions that require multiple steps (like finding customers who spend more than average)

• Create temporary results to use in another question

Subqueries let us do all of this in a single SQL statement, without having to run multiple separate queries.

What we will cover

In this lesson, we will explore:

• What subqueries are and how they work (in simple terms)

• Different types of subqueries and when to use them

• Real-world examples using a music store database

• How to write your own subqueries

• Tips for making your subqueries work better

By the end of this lesson, you will be able to write SQL queries that answer complex questions about your data—taking your skills to the next level.

Let’s dive in!

Understanding SQL Subqueries

What is a Subquery?

A subquery is simply a query inside another query. The inner query runs first, and its result is used by the outer (main) query to complete its job.

Here’s a simple example:

SELECT 
    Name AS "Song Name", 
    Milliseconds/60000 AS "Length (minutes)"
FROM 
    tracks
WHERE 
    Milliseconds > (SELECT AVG(Milliseconds) FROM tracks);

In this example:

1. The part in parentheses (SELECT AVG(Milliseconds) FROM tracks) is the subquery

2. It calculates the average song length

3. The main query then finds all songs longer than this average

It’s like first asking “What's the average song length?” and then using that answer to find “Which songs are longer than that?”

Syntax and Structure

Subqueries are always enclosed in parentheses ( ) and can appear in different parts of a SQL statement:

• In the SELECT part (to calculate values)

• In the FROM part (as a temporary table)

• In the WHERE part (for filtering)

• In the HAVING part (for filtering after grouping)

The basic pattern looks like this:

SELECT column_name(s)
FROM table_name
WHERE column_name operator (SELECT column_name FROM table_name WHERE condition);

How Subqueries Work?

Let’s look at two main types of subqueries:

Non-correlated Subqueries

These are independent subqueries that run once, and their result is passed to the outer query

-- Find songs with above-average length
SELECT 
    Name AS "Song Name", 
    Milliseconds/60000 AS "Length (minutes)"
FROM 
    tracks
WHERE 
    Milliseconds > (SELECT AVG(Milliseconds) FROM tracks);

How it works:

1. The database first calculates the average song length

2. Then it finds all songs longer than this average

Correlated Subqueries

These subqueries reference the outer query and run once for each row processed by the outer query.

-- Find artists with more albums than the average artist
SELECT 
    artists.Name AS "Artist Name",
    (SELECT COUNT(*) FROM albums WHERE albums.ArtistId = artists.ArtistId) AS "Number of Albums"
FROM 
    artists
WHERE 
    (SELECT COUNT(*) FROM albums WHERE albums.ArtistId = artists.ArtistId) > 
    (SELECT AVG(album_count) FROM 
        (SELECT COUNT(*) AS album_count FROM albums GROUP BY ArtistId)
    );

How it works:

1. For each artist, it counts how many albums they have

2. It also calculates the average number of albums per artist

3. It shows only artists with more albums than average

Types of Subqueries

Scalar Subqueries

These return a single value (one row, one column). They're often used in comparisons.

-- Find songs with length above average, and show the average too
SELECT 
    Name AS "Song Name", 
    Milliseconds/60000 AS "Length (minutes)",
    (SELECT AVG(Milliseconds)/60000 FROM tracks) AS "Average Length (minutes)"
FROM 
    tracks
WHERE 
    Milliseconds > (SELECT AVG(Milliseconds) FROM tracks);

Column Subqueries

These return a single column with multiple rows. They are typically used with operators like IN.

-- Find all songs by AC/DC
SELECT 
    tracks.Name AS "Song Name"
FROM 
    tracks
WHERE 
    AlbumId IN (
        SELECT AlbumId 
        FROM albums 
        WHERE albums.ArtistId IN (
            SELECT ArtistId
            FROM artists
            WHERE Name = 'AC/DC'
        )
    );

Table Subqueries (derived tables)

These return multiple rows and columns, creating a temporary table. They are used in the FROM part.

-- Find the top 3 genres with the highest average track length
SELECT 
    genre_stats.Genre,
    genre_stats.AvgLength
FROM (
    SELECT 
        genres.Name AS Genre, 
        AVG(tracks.Milliseconds)/60000 AS AvgLength
    FROM 
        tracks
    JOIN 
        genres ON tracks.GenreId = genres.GenreId
    GROUP BY 
        genres.Name
) AS genre_stats
ORDER BY 
    genre_stats.AvgLength DESC
LIMIT 3;

Practical Applications of Subqueries

Finding Items Above Average

One of the most common uses of subqueries is to find records that exceed average values.

-- Find customers who spend more than the average customer
SELECT 
    FirstName || ' ' || LastName AS "Customer Name",
    (SELECT SUM(Total) FROM invoices WHERE invoices.CustomerId = customers.CustomerId) AS "Total Spent"
FROM 
    customers
WHERE 
    (SELECT SUM(Total) FROM invoices WHERE invoices.CustomerId = customers.CustomerId) > 
    (SELECT AVG(customer_total) FROM 
        (SELECT SUM(Total) AS customer_total FROM invoices GROUP BY CustomerId)
    )
ORDER BY 
    "Total Spent" DESC;

This query:

1. Calculates how much each customer has spent

2. Calculates the average spending per customer

3. Shows only customers who spent more than average

Finding related records

Subqueries are great for finding records that are related to other records in specific ways.

-- Find all albums by artists who have more than 1 album
SELECT 
    albums.Title AS "Album Title",
    artists.Name AS "Artist"
FROM 
    albums
JOIN 
    artists ON albums.ArtistId = artists.ArtistId
WHERE 
    artists.ArtistId IN (
        SELECT ArtistId
        FROM albums
        GROUP BY ArtistId
        HAVING COUNT(*) > 1
    )
ORDER BY 
    artists.Name, albums.Title;

This query:

1. Finds artists who have more than one album

2. Shows all albums by those artists

Comparing groups

Subqueries can help compare groups of data against each other.

-- Find genres with more songs than the average genre
SELECT 
    genres.Name AS "Genre",
    COUNT(*) AS "Number of Songs"
FROM 
    tracks
JOIN 
    genres ON tracks.GenreId = genres.GenreId
GROUP BY 
    genres.Name
HAVING 
    COUNT(*) > (
        SELECT AVG(song_count) 
        FROM (
            SELECT COUNT(*) AS song_count 
            FROM tracks 
            GROUP BY GenreId
        )
    )
ORDER BY 
    "Number of Songs" DESC;

This query:

1. Counts how many songs are in each genre

2. Calculates the average number of songs per genre

3. Shows only genres with above-average song counts

Performance tips and best practices

When to use subqueries?

Subqueries are most useful when:

• You need to compare values against averages or totals

• You need to filter based on information from another table

• You want to avoid creating temporary tables

Making subqueries run faster

1. Keep subqueries simple: The simpler a subquery, the faster it will run.

2. Consider using JOINs: Sometimes a JOIN can be faster than a subquery.

-- Subquery approach
SELECT 
    tracks.Name AS "Song Name"
FROM 
    tracks
WHERE 
    GenreId IN (
        SELECT GenreId
        FROM genres
        WHERE Name = 'Rock'
    );

-- Equivalent JOIN approach (often faster)
SELECT 
    tracks.Name AS "Song Name"
FROM 
    tracks
JOIN 
    genres ON tracks.GenreId = genres.GenreId
WHERE 
    genres.Name = 'Rock';

3. Use EXISTS instead of IN for large datasets: The EXISTS operator often performs better because it stops once it finds a match.

-- Using IN
SELECT 
    artists.Name AS "Artist"
FROM 
    artists
WHERE 
    ArtistId IN (
        SELECT ArtistId
        FROM albums
        WHERE Title LIKE '%Live%'
    );

-- Using EXISTS (often more efficient)
SELECT 
    artists.Name AS "Artist"
FROM 
    artists a
WHERE 
    EXISTS (
        SELECT 1
        FROM albums
        WHERE albums.ArtistId = a.ArtistId
        AND Title LIKE '%Live%'
    );

4. Avoid too much nesting: Try not to nest subqueries too deeply (more than 2-3 levels) as it makes queries hard to read and can slow things down.

Why subqueries are useful?

Think of subqueries like having a helper who can quickly answer a question while you are in the middle of asking a bigger question.

For example, if you want to know “Which songs are longer than average?”, you first need to know “What's the average song length?” A subquery lets you answer that second question right inside your main question.

Without subqueries, you would have to:

1. Run a separate query to find the average song length

2. Write down that number

3. Run another query using that number to find the longer songs

Subqueries let you do all of this in one step, making your work more efficient and your code cleaner.

Conclusion: mastering SQL subqueries

Congratulations! You've now explored SQL subqueries and learned how they can help you answer complex questions about your data. Let's recap what we've covered:

Key takeaways

1. Subqueries are queries nested within other queries, allowing you to perform multi-step operations in a single SQL statement.

2. Different types of subqueries serve different purposes:

   • Scalar subqueries return a single value (like the average song length)

   • Column subqueries return a list of values (like all album IDs for an artist)

   • Table subqueries return a temporary table with multiple rows and columns

3. Subqueries can appear in different parts of a SQL statement:

   • In the SELECT part (to calculate values)

   • In the FROM part (as a temporary table)

   • In the WHERE part (for filtering)

   • In the HAVING part (for filtering after grouping)

4. There are two main types of subqueries:

   • Non-correlated subqueries run once and pass their result to the outer query

   • Correlated subqueries reference the outer query and run once for each row processed

5. Performance tips to remember:

   • Sometimes JOINs are faster than subqueries

   • EXISTS often performs better than IN for large datasets

   • Keep your subqueries as simple as possible

Real-world applications

The techniques you have learned in this lesson can help you:

• Find songs that are longer than average

• Identify customers who spend more than others

• Discover which music genres are most popular

• Find artists who have released multiple albums

• Answer complex business questions with a single query

By mastering subqueries, you have added a powerful tool to your SQL toolkit that will help you tackle more complex data challenges.

Stay tuned for more examples and walkthroughs!

If this lesson helped you, please share and support Data Nomads Lab on Substack. Your support helps keep this content free and accessible to learners around the world.

Share

👋🏽 Hey, it’s Ismail. Welcome to data nomads lab newsletter on learning data analytics, career growth, networking, building portfolios, and interview skills to break into tech role as a high-performer.

From rows to insights

In Lesson 3, we learned how to join tables together using SQL joins. That gave us the power to connect different parts of the Chinook database—artists to albums, customers to purchases, and tracks to invoices.

If you missed that one, you can go back and read the full SQL Joins lesson here

But here’s the thing: sometimes we don’t want to look at individual rows.
We want to look at the big picture.

• How many tracks are there in total?

• What’s the average price of a track?

• How much revenue did we generate from invoices?

• Which album has the most songs?

To answer questions like these, we use SQL aggregation functions.

Today, we’ll cover:
✅ COUNT() – how many?
✅ SUM() - total value
✅ AVG() - average
✅ MIN() / MAX() – lowest and highest
✅ GROUP BY – grouping rows for summaries
✅ Real-world examples using the Chinook database

COUNT(): how many rows?

The COUNT() function tells you how many rows meet a condition—or exist at all.

Question:
How many tracks are in the database?

SELECT COUNT(*) AS TotalTracks  
FROM Track;

✅ This gives you one number: the total number of rows in the Track table.

SUM(): total value

SUM() adds up the values in a numeric column.

Question:
How much total revenue has the business made?

SELECT SUM(Total) AS TotalRevenue  
FROM Invoice;

✅ This adds up every Total value from the Invoice table, giving us the total revenue across all invoices.

AVG(): average value

AVG() calculates the average (mean) value.

Question:
What is the average price per invoice?

SELECT AVG(Total) AS AverageInvoiceValue  
FROM Invoice;

You can also apply AVG() to things like track length or price.

MIN() / MAX(): smallest and largest

MIN() returns the lowest value, and MAX() returns the highest.

Question:
What’s the longest track in the database (in milliseconds)?

SELECT MAX(Milliseconds) AS LongestTrack   
FROM Track;

Question:
What’s the shortest track?

SELECT MIN(Milliseconds) AS ShortestTrack  
FROM Track;

GROUP BY: breaking data into categories

Now this is where aggregations get powerful.
GROUP BY allows you to calculate things for each group—not just a total.

Question:
How many tracks are in each album?

SELECT AlbumId, COUNT(*) AS TrackCount  
FROM Track  
GROUP BY AlbumId;

Each row in the result tells you how many tracks belong to each album.

You can also join tables to make the results more meaningful.

Question:
How many tracks does each artist have?

SELECT Artist.Name, COUNT(*) AS TotalTracks  
FROM Artist  
JOIN Album ON Artist.ArtistId = Album.ArtistId  
JOIN Track ON Album.AlbumId = Track.AlbumId  
GROUP BY Artist.Name  
ORDER BY TotalTracks DESC;

✅ This joins three tables to count tracks per artist and orders them from most to fewest.

Combining WHERE+ GROUP BY

Question:
How many invoices did we have in each country, but only for countries with at least 5 invoices?

SELECT BillingCountry, COUNT(*) AS InvoiceCount  
FROM Invoice  
GROUP BY BillingCountry  
HAVING COUNT(*) >= 5  
ORDER BY InvoiceCount DESC;

👉 HAVING is like WHERE but works after grouping.
We use HAVING to filter groups, not individual rows.

Real-world applications of aggregations

These functions help us answer everyday business questions:

• How many users signed up last month?

• What is the average customer order size?

• Which products are selling the most?

• Who are our top-spending customers?

And just like that, raw data becomes a story.

Lesson recap

✅ COUNT() – number of rows
✅ SUM() – total value
✅ AVG() – average value
✅ MIN() / MAX() – smallest/largest
✅ GROUP BY – summarize by category
✅ HAVING – filter groups after grouping

You now know how to take thousands of rows and boil them down into key insights.

Next lesson: SQL Subqueries – queries inside queries

In our next lesson, we will explore SQL subqueries—how to write queries inside other queries to answer more complex questions.

You will learn how to:

• Filter results based on a summary

• Compare one row to group statistics

• Write cleaner, more readable SQL

Stay tuned for more examples and walkthroughs!

If this lesson helped you, please share and support Data Nomads Lab on Substack. Your support helps keep this content free and accessible to learners around the world.

Share

Until next time—happy querying!

👋🏽 Hey, it’s Ismail. Welcome to data nomads lab newsletter on learning data analytics, career growth, networking, building portfolios, and interview skills to break into tech role as a high-performer.

In Lesson 2, we explored SQL basics—how to retrieve, filter, and sort data using simple queries. Now, we’re stepping into the real power of SQL: Joins.

A database is rarely just one table. In most cases, data is spread across multiple tables that are connected through relationships. Joins allow you to combine these tables and analyze data holistically.

Today, we will cover:
✅ Understanding how tables relate in the Chinook Database
✅ The four main types of SQL joins (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN)
✅ Real-world examples of each

Let’s dive in!

Understanding SQL Joins

Think of a database like a music streaming app:

• Artists release albums.

• Each album contains multiple tracks.

• Customers purchase those tracks.

These entities are stored in different tables, but they share relationships.

Example: Chinook Database Structure

The Chinook Database follows this structure:

• Artists (ArtistId, Name)

• Albums (AlbumId, Title, ArtistId)

• Tracks (TrackId, Name, AlbumId)

• Invoices (InvoiceId, CustomerId, Total)

Our goal is to connect these tables using SQL joins.

Understanding joins: Why do we join certain tables first?

When writing a JOIN query, the first table we pick is often the one we want to keep as the base. Every row from this table will appear in the result, as long as it has a match in the second table.

• The primary key of one table typically joins with the foreign key in another table.

• If we’re looking at albums, it makes sense to start from the Album table and join it with the Artist table on ArtistId.

• If we’re tracking sales, we start with Invoice because it contains purchase records, and then join it to details like tracks or customers.

Single join: Linking albums and artists

Question:
How can we list all albums alongside their artist names?

Try writing a query before looking at the answer.

SELECT Album.Title, Artist.Name 
FROM Album 
JOIN 
Artist ON Album.ArtistId = Artist.ArtistId;

How it works:

• We start with Album because we want to see all albums.

• We join Artist using ArtistId, the primary key in Artist and foreign key in Album.

• The result shows albums with their respective artist names.

Joining three tables: Finding who purchased which tracks

Question:
How can we find the first name, last name, and tracks purchased by each customer?

SELECT Customer.FirstName, Customer.LastName, Track.Name AS TrackPurchased  
FROM Customer  
JOIN Invoice ON Customer.CustomerId = Invoice.CustomerId  
JOIN InvoiceLine ON Invoice.InvoiceId = InvoiceLine.InvoiceId  
JOIN Track ON InvoiceLine.TrackId = Track.TrackId  
ORDER BY Customer.LastName;

Why this works:

1. Start with Customer because we want to list all customers.

2. Join Invoice to connect customers to their purchase records.

3. Join InvoiceLine to get the individual tracks purchased.

4. Join Track to display track names instead of just IDs.

This is a multi-table join—a common real-world SQL query pattern.

If you are new to SQL or need a refresher, check out “Lesson 2: SQL Basics” here:

Different types of joins

Now that we’ve covered basic joins, let’s explore the four main types of SQL joins.

Inner join: Returning only matching records

An INNER JOIN returns only the rows where there is a match in both tables.

Question:
How can we list only albums that have a matching artist?

SELECT Album.Title, Artist.Name 
FROM Album INNER 
JOIN 
Artist ON Album.ArtistId = Artist.ArtistId;

✔ If an artist has no album, they won’t appear in the result.

Left Join: Keeping all records from the left table

A LEFT JOIN keeps all rows from the first table and matches data from the second table. If there’s no match, it fills the missing values with NULL.

Question:
How can we list all artists, even if they don’t have an album?

SELECT Artist.Name, Album.Title 
FROM Artist LEFT 
JOIN 
Album ON Artist.ArtistId = Album.ArtistId;

✔ If an artist has no album, the album column will show NULL.

Right Join: Keeping all records from the right table

A RIGHT JOIN is the opposite of a LEFT JOIN. It keeps all rows from the second table and matches data from the first table.

Question:
How can we list all albums, even if their artist is missing?

SELECT Artist.Name, Album.Title 
FROM Artist RIGHT 
JOIN 
Album ON Artist.ArtistId = Album.ArtistId;

✔ If an album has no artist, the artist column will show NULL.

Full Outer Join: Including everything

A FULL OUTER JOIN includes all records from both tables. If a match exists, it appears. If not, NULL fills the gaps.

Question:
How can we see all artists and albums, even if there’s no direct match?

SELECT Artist.Name, Album.Title 
FROM Artist FULL OUTER 
JOIN 
Album ON Artist.ArtistId = Album.ArtistId;

✔ This ensures no data is left out, even if no relationship exists.

Practical applications of joins

SQL joins are used everywhere:
🔹 E-commerce: Joining orders with customer data to track purchases.
🔹 Healthcare: Connecting patients, doctors, and prescriptions.
🔹 Finance: Merging transaction logs with account details.

Try writing your own joins with different tables in the Chinook Database!

Next Steps

The next lesson will introduce SQL Aggregations, where we’ll summarize data using functions like COUNT, SUM, and AVG.

📢 For paid subscribers, stay tuned for a video breakdown of these joins!

If this lesson was helpful, please share and support Data Nomads Lab on Substack. Your support keeps this content coming.

Until next time—happy querying!

If this newsletter inspired you, share it with someone who is starting their data journey. Your support helps me keep writing and creating helpful content for aspiring data professionals. Let us grow together!

Share

👋🏽 Hey, it’s Ismail. Welcome to data nomads lab newsletter on learning data analytics, career growth, networking, building portfolios, and interview skills to break into tech role as a high-performer.

Hello Readers,

When I was first getting into data analytics, I believed that if I learned all the right technical skills, success would follow.

If I mastered SQL and Python, I would get a great job.

If I worked hard on every dataset I touched, I would stand out.

If I built dashboards that looked great, people would recognize my value.

There’s truth in this. Hard work matters. Learning SQL, Python, and visualization tools is essential. But success in a data analytics career isn’t a perfect equation of effort = results.

The best data analysts aren’t the ones who simply know the most functions in Pandas or the best visualization techniques in Tableau. They aren’t necessarily the hardest-working people in the room either.

So what’s the missing factor?

Leverage

Some skills, projects, and approaches have a multiplier effect—they take the same effort but generate far greater impact.

Not all work is created equal.

The essence of career growth in data analytics is to identify the highest-leverage activities for YOU.

You and I have different strengths, backgrounds, and career goals. What’s strategic for you might not be strategic for me.

Some people find their leverage in technical depth—becoming exceptional at machine learning and AI. Others find it in business impact—translating data insights into actions that drive revenue. Some thrive in storytelling and visualization, making complex data accessible and persuasive.

Copying someone else’s career path blindly is intellectually lazy.

A better approach is to think:

• What are you naturally good at?

• What kind of problems excite you?

• What do companies and stakeholders actually value?

• Where can you apply data analytics for the highest impact?

1. Master the right skills for maximum impact

It’s easy to get caught in the trap of endless learning—watching tutorials, getting certifications, and adding tools to your resume.

But real leverage comes from identifying which skills actually matter.

For example:

• If you work in finance, mastering SQL and Excel will provide more leverage than deep learning.

• If you want to transition into AI-driven analytics, then Python, machine learning, and cloud computing are your best bets.

• If you’re in marketing analytics, A/B testing, customer segmentation, and dashboarding will generate more impact than advanced regression models.

More knowledge is great, but applied knowledge that creates business impact is what matters.

2. Choose high-leverage projects

Some projects have a bigger multiplier effect than others.

If you spend weeks cleaning data that no one will use, that’s hard work with little return.

If you build a dashboard that answers key business questions and helps executives make better decisions, that’s strategic work.

Ask yourself:

• Is this project solving a valuable problem?

• Will the insights drive action?

• Is this work visible to decision-makers?

A well-placed project can accelerate your career faster than years of unnoticed work.

3. Capture value from your work

The most successful data professionals don’t just create value—they capture value.

• If you help a company save $1M per year, a $20K raise is reasonable.

• If your analysis helps a team make better decisions, you become an indispensable asset.

• If you publicly share your work (blog posts, GitHub projects, case studies), opportunities find you.

It’s not just about technical skill—it’s about understanding how data creates value and ensuring you benefit from it too.

The shift from hard work to strategic work

The difference between grinding in data analytics and thriving in data analytics is strategic work.

Success isn’t about how many dashboards you build, how many lines of SQL you write, or how many hours you put in. It’s about:

• Choosing the right skills to master

• Working on high-leverage projects

• Understanding how data creates business value

Anyone can work hard. The real winners in data analytics work smart.

Are you working strategically?

Thanks,
- Ismail Osman

P.S. Pass this along to any really good friends who are also interested in tips for a successful data career and life. They can click here to join so they also receive these emails.

Share

👋🏽 Hey, it’s Ismail. Welcome to data nomads lab newsletter on learning data analytics, career growth, networking, building portfolios, and interview skills to break into tech role as a high-performer.

Hello Readers,

I recently hosted an AMA in a Reddit community I am a member of, inviting questions on related to data analytics, from its fundamentals and how to get started to the essential skills and emerging career opportunities with an overwhelming response that ranged from entry-level basics to the impact of recent AI advancements on the industry.

I have compiled the most valuable insights from our discussion into this comprehensive guide to help others looking to break into this dynamic field.

Lets get started!

Data analytics has emerged as one of the most promising career paths in today’s digital economy. As organizations continue to recognize the value of data-driven decision making, the demand for skilled analysts continues to grow.

In this newsletter, I will share insights from my experience in the field that I shared during the AMA to help you navigate your journey into data analytics.

Building your professional network

Success in data analytics isn’t just about technical skills, it’s also about building meaningful professional connections. The key is being proactive and genuine in your networking approach:

• Join LinkedIn groups focused on data analytics and actively participate in discussions

• Engage with online communities like Reddit’s data analysis forums and relevant Discord servers

• Share your learning journey and projects on LinkedIn

• Connect with professionals for coffee chats, being specific about what you hope to learn

• Contribute meaningful insights to industry discussions

Creating an impressive portfolio (show not only tell)

Your portfolio is your chance to demonstrate real-world problem-solving abilities. Here’s what to focus on:

• Build projects that solve actual problems using public datasets

• Include diverse projects showcasing different tools (Excel, Python, SQL, Tableau, MS Power BI)

• Ensure all code is well-documented on GitHub

• Focus on demonstrating your ability to clean messy data, derive insights, and communicate findings effectively

Essential skills for entry-level success

The skills needed for data analytics can be grouped into three main categories:

Technical Skills:

• SQL

• Excel

• Descriptive statistics

• Business metrics

• Data visualization tools

Analytical Skills:

• Problem-solving

• Statistical thinking

• Attention to detail

Soft Skills:

• Communication

• Time management

• Quick learning ability

Standing out as a new hire

Once you land that first job, here's how to make your mark:

• Develop specific competencies that become your signature strengths

• Look for process improvements, even in small tasks

• Ask thoughtful questions about the broader business context

• Take initiative while knowing when to seek guidance

• Build relationships across different teams

• Consistently meet deadlines

• Build credibility by doing what you promise you will do

Learning resources

While the field evolves quickly, these resources can help you build a strong foundation:

Online Platforms:

• Coursera.org

  • Google Data Analytics

  • IBM Data Analyst Professional Certificate

• edX.org data analysis courses

• DataCamp

• YouTube educational channels

Data tools

• Tableau Public

• Microsoft BI Desktop

• Excel & Google Sheet

• DB Browser for SQLite

• Google Charts

• Datawrapper

Promote your experience with data analysis tools

Proficiency in data analysis software is in high demand, and showcasing these skills can boost your professional image and unlock new opportunities.

A few ways you can promote your knowledge and expertise in data analysis tools are:

• Add your skills to your resume: Tailor your resume to emphasize how you have used analytics to drive business decisions.

  • Feature specific achievements like “Developed executive dashboards that improved sales forecasting accuracy by 30%” or “Led cross-functional analysis that identified $2M in cost-saving opportunities.”

  • Highlight proficiency in essential business analytics tools like Tableau, Power BI, Advanced Excel, and SQL.

  • Include experience with business metrics (KPIs, ROI analysis, market sizing) and emphasize your ability to translate complex data into clear business recommendations.

• Create a professional portfolio: Create a portfolio that showcases your ability to solve real business problems through analytics.

  • Include case studies that walk through your problem-solving process: from defining business questions and selecting appropriate analytical methods to delivering actionable recommendations.

  • Feature projects that demonstrate commercial acumen, such as customer segmentation analysis, revenue optimization studies, or market trend analyses.

  • Clearly articulate the business context, methodology, and most importantly, the tangible business outcomes of your work.

• Utilize LinkedIn and other social profiles: Develop your professional presence to reflect your business analytics expertise.

  • On LinkedIn, detail experiences where you have partnered with stakeholders to drive data-informed decisions.

  • Share insights about business analytics trends and tools, focusing on their practical business applications.

  • Connect with business analytics professionals and engage with content related to your industry's analytical challenges.

  • Consider writing about how specific analytics techniques can solve common business problems, demonstrating your ability to bridge the gap between technical analysis and business strategy.

Industries with strong demand

Data analysts are needed across virtually every sector, but these industries show particularly strong demand:

• Healthcare/Biotech (patient data analysis)

• Financial Services (risk analysis, fraud detection)

• E-commerce/Retail (customer behavior analysis)

• Technology (product analytics)

• Consulting (varied projects)

• Manufacturing (supply chain optimization)

• Insurance (risk assessment, claims analysis)

The impact of AI on data analytics

Rather than replacing data analysts, AI and generative AI will empower those who learn to use them—leaving behind those who don’t.

• Learn how to work with AI tools (ChatGPT, Claude.ai) and integrate them into your workflow.

• Familiarize yourself with AI-powered analytics platforms and automation.

• Stay up to date with the latest AI trends and how they impact data analysis.

• Practice prompt engineering to get better, more accurate insights from AI.

• Share your learning and experiment, adapting is the key to staying relevant.

Compensation insights

Entry-level data analysts in the US typically earn between $50,000–$70,000 annually, with experienced analysts earning $80,000 or more.

Data scientists command higher salaries, starting at $70,000-$90,000 and reaching $100,000-$150,000+ with experience.

Freelance opportunities can also be lucrative, with earnings varying based on project scope and expertise.

Global opportunities

The beauty of data analytics is its global reach. Whether you're based in a major tech hub or a developing market, opportunities exist through:

• Remote work for global companies

• Freelance platforms like Upwork

• Local roles with international organizations

• Positions with NGOs and financial institutions

• Monitoring and evaluation specialist roles

Looking ahead

Despite current market fluctuations, the future of data analytics remains bright.

The field continues to evolve with new tools and technologies,

but the fundamental need for data-driven insights persists.

Success in this field requires

continuous learning, adaptability, and a commitment to solving real-world problems through data.

Remember

the path to becoming a successful data analyst isn’t just about mastering tools and techniques

it’s about developing a mindset of curiosity, problem-solving, and continuous improvement.

Start building your skills today, and

you will be well-positioned for a rewarding career in this dynamic demanding field.

Thanks,
- Ismail Osman

P.S. Pass this along to any really good friends who are also interested in tips for a successful data career and life. They can Click Here to join so they also receive these emails.

Share

👋🏽 Hey, it’s Ismail. Welcome to data nomads lab newsletter on learning data analytics, career growth, networking, building portfolios, and interview skills to break into tech role as a high-performer.

The best data analysts don’t just analyze data; They understand the broader picture.

Over the years, I’ve been asked, “How can I become a better data analyst?”

My answer has evolved, but one thing has stayed the same: only focusing on technical skills isn’t enough.

Yes, you need Excel, SQL, Python, Tableau & PowerBI and a good grasp of statistics. But if you only focus on the mechanics of data analysis, you limit your impact. The best analysts know how to think, not just how to query.

They develop expertise in adjacent disciplines. These areas give you the intuition to ask the right questions, challenge assumptions, and tell compelling stories with data.

Under the hood, the best analysts are also:

• Business strategists

• Storytellers

• Psychologists

• Communicators

• (And sometimes all of the above)

These skills are the secret sauce that separates a great analyst from someone who just knows how to make charts.

If you want to level up, here are some key adjacent disciplines I recommend:

1. Business Thinking

Data is only as powerful as the decisions it influences. Many analysts focus on running queries and generating reports but struggle to tie their findings to real business impact. Understanding how businesses operate, what drives revenue, and what key stakeholders care about will set you apart.

Learn about:

• Revenue drivers (cost vs. volume)

• How different teams use data

• What decisions your stakeholders need to make

• How companies measure success

• Identifying the real question behind the request

2. Communication & Storytelling

Numbers don’t speak for themselves—you have to make them resonate. A great analyst doesn’t just share data; they craft a compelling narrative around it. The ability to communicate insights effectively ensures your work leads to action instead of getting lost in a dashboard.

Learn about:

• How to structure a narrative

• Simplifying complex ideas

• Making numbers feel real (percentages vs. raw numbers)

• Using visuals effectively

• Anticipating and addressing skepticism

3. Psychology & Behavioral Economics

Data is about people—how they think, behave, and make decisions. Understanding human psychology will sharpen your ability to interpret trends and predict behavior. People don’t always act rationally, and knowing why can help you make better recommendations.

Learn about:

• Cognitive biases

• Decision-making frameworks

• How people perceive risk and reward

• The psychology of persuasion

• What motivates action

4. Data Engineering & Infrastructure

Most analysts hit a ceiling because they don’t understand where the data comes from or how it’s structured. Without a basic understanding of data engineering, you will struggle with messy data, inefficient queries, and unreliable pipelines.

Learn about:

• How databases work

• ETL (Extract, Transform, Load) processes

• Data governance and quality

• Common pitfalls in data pipelines

• Writing efficient queries

5. Product & User Experience (UX)

If you work in tech, your data exists to improve a product. The best analysts understand how users interact with the product and what drives engagement. A deeper understanding of UX allows you to ask better questions and measure what truly matters.

Learn about:

• User journeys and pain points

• Conversion funnels

• A/B testing methodologies

• What makes a good metric

• The difference between correlation and causation

6. Decision Science & Strategy

Data is valuable when it leads to better decisions. But many organizations struggle with decision paralysis. Your role is to help cut through the noise and focus on what truly matters.

Learn about:

• Framing problems effectively

• Identifying trade-offs

• Prioritization frameworks

• The limits of data-driven decision-making

• When intuition and experience matter

7. Writing & Presenting

Your insights are only as good as your ability to communicate them. Writing well and presenting clearly ensures your findings have the impact they deserve.

Learn about:

• Writing concisely

• Structuring reports for clarity

• Presenting data persuasively

• Avoiding unnecessary complexity

• Adapting your message to different audiences

Read about these topics. Practice them. Take on projects that challenge you to think beyond the numbers.

Mastering these adjacent disciplines will give you better judgment, clearer thinking, and stronger execution, which, at the end of the day, is what makes an analyst truly valuable.

Which of these skills are you most interested in improving?

Share your ideas in the comments, I would love to hear from you.

If this newsletter inspired you, share it with someone who is starting their data journey. Your support helps me keep writing and creating helpful content for aspiring data professionals. Let us grow together!

Share

👋🏽 Hey, it’s Ismail. Welcome to data nomads lab newsletter on learning data analytics, career growth, networking, building portfolios, and interview skills to break into tech role as a high-performer.

Read time: 2 minutes

As a data analyst, your job is to uncover the truth.

But sometimes, that truth makes people uncomfortable.

You run an analysis, and the numbers don’t look great. Maybe a strategy isn’t working, a new initiative isn’t delivering results or the insights are not yielding any trends.. You present the data, and instead of acceptance, you get resistance.

Can you check again?
Are you sure this is right?
Let’s cut the data another way?

At first, these seem like reasonable requests. Of course, you want to be thorough. But after a while, you realize what’s actually happening:

They don’t like the answer, so they keep pushing until they get a different one.

And if you are not careful, you start feeling responsible for their disappointment.

You apologize when the data doesn’t tell the story they want to hear.

You scramble to find a better-looking number.

You start questioning your own work, not because it’s wrong, but because the reaction makes you feel like it is.

That’s how analysts fall into the trap of shaping data to make people happy.

Why this is a problem

1. It’s a waste of time. If the financials don’t support the strategy, slicing the data a hundred different ways won’t change that.

2. It creates a culture of bad decisions. If leadership only hears what they want to hear, they won’t make the right calls.

3. It kills morale. Analysts become frustrated when their work isn’t taken at face value. They stop feeling like problem-solvers and start feeling like spin doctors.

How to handle it instead

🚫 Giving in to pressure

takeholder: "Can you tweak the numbers so it looks more in line with expectations?"
Analyst (You): “The data is what it is. If we’re not seeing the expected results, we should focus on understanding why.”

✔ Why this works: You reinforce that the data is factual, not something to be adjusted based on preference.

🚫 Endless rework with no new insight

Stakeholder: “We need to keep slicing the data. There must be something we’re missing.”
Analyst(You): “Okay, I’ll keep looking.”

❌ What’s wrong? You’re wasting time on analysis that won’t change the reality of the situation.

✅ Pushing for action instead of more analysis

Stakeholder: “We need to keep slicing the data. There must be something we’re missing.”
Analyst(You): “We’ve analyzed multiple angles, and the trend is clear. Instead of more slicing, let’s discuss what actions we can take based on this.”

✔ Why this works: You set a boundary and redirect the conversation toward decision-making.

What to do instead

Stand by your analysis. If you’ve done your due diligence, trust your work.

Be clear and direct. Instead of saying, “Unfortunately, the data doesn’t show X,” say, “The data shows Y, which suggests we need to rethink X.”

Shift the conversation. Instead of chasing numbers that don’t exist, ask: “What action can we take given this reality?”

The goal of data isn’t to make people feel good. It’s to make people see clearly.

You don’t need to apologize for the truth. You just need to present it.

Have you ever been pressured to “find a better number”? comment below —I would love to hear your experience.

If this newsletter inspired you, share it with someone who is starting their data journey. Your support helps me keep writing and creating helpful content for aspiring data professionals. Let us grow together!

Share

👋🏽 Hey, it’s Ismail. Welcome to data nomads lab newsletter on learning data analytics, career growth, networking, building portfolios, and interview skills to break into tech role as a high-performer.

SELECT these columns or all columns FROM this table WHERE this condition is true or false but I don’t want to see all thousands or millions of results, just LIMIT is to first 10, 20, 50 or 100 data records.

But I want you to ORDER BY the results from smallest to largest (A to Z) aka Ascending (ASC) or from largest to smallest Descending (DESC).

Since we spend a lot of money on music for different countries, I want to GROUP BY the total amount we spent in each country for music.

You can further refine your query to be more precise and adaptable to different conditions by using SQL command

• IN: to check is a value exist in a specific list

• LIKE: to search for patterns in text

• BETWEEN: to filter values within a range

• NOT: to exclude specific values or conditions

If you are still confused do not stress — we will use the Chinook database to explain everything above in examples that is easy to follow.

SELECT and FROM: the core of SQL

Every SQL query begins with SELECT and FROM. Think of these as the what and where of your query.

Example

Imagine you are looking at the Tracks table and want to list all track names:

SELECT Name 
FROM Tracks;     

This query tells SQL: Find the Name column in the Tracks table

Output (partial)

Name 
---------------- 
For Those About to Rock 
Put The Finger On You 
Let's Get It Up 
...

By default, SQL returns every row in the table—whether there are 10 or 10,000 rows.

In SQL, the asterisk (*) is a wildcard that represents all columns in a table. When you want to select every column from a table, you simply use SELECT *.

Example

Suppose you want to view every piece of information about tracks from the Tracks table:

SELECT * FROM Tracks;

This query returns every column and every row from the Tracks table.

WHERE: Filtering Your Data

The WHERE clause is your filter. It helps you narrow down the results to only those that meet specific conditions.

Example

Suppose you want tracks with a Milliseconds value greater than 300,000 (about 5 minutes):

SELECT Name, Milliseconds 
FROM Tracks 
WHERE Milliseconds > 300000;

This query returns only tracks longer than 5 minutes.

LIMIT: Keep It Short and Sweet

Sometimes, you don’t need all the data. LIMIT helps you control how many rows are returned.

Example

Let’s retrieve the first 5 tracks:

SELECT Name 
FROM Tracks 
LIMIT 5;

Output

Name 
---------------- 
For Those About to Rock 
Put The Finger On You 
Let's Get It Up 
Inject the Venom 
Snowballed

ORDER BY: Sorting Matters

ORDER BY arranges your data. By default, it sorts in ascending order, but you can specify DESC for descending.

Example

List the top 5 longest tracks:

SELECT Name, Milliseconds 
FROM Tracks 
ORDER BY Milliseconds DESC 
LIMIT 5;

GROUP BY: Aggregating Your Data

GROUP BY lets you summarize data. Pair it with aggregate functions like COUNT, SUM, or AVG.

Example

Count how many tracks each album contains:

SELECT AlbumId, COUNT(*) AS TrackCount 
FROM Tracks 
GROUP BY AlbumId;

IN and NOT: Specifying Multiple Values

Use IN to filter data based on a list of values. Add NOT to exclude those values.

Example

Find all tracks from artists with IDs 1, 3, or 5:

SELECT Name 
FROM Tracks 
WHERE ArtistId IN (1, 3, 5);

BETWEEN: Working with Ranges

BETWEEN is ideal for specifying ranges, whether dates, numbers, or strings.

Example

Find tracks with Milliseconds between 200,000 and 300,000:

SELECT Name, Milliseconds 
FROM Tracks 
WHERE Milliseconds BETWEEN 200000 AND 300000;

NULL: Handling Missing Values

In SQL, NULL represents missing data. Use IS NULL or IS NOT NULL to filter for or exclude these rows.

Example

Find tracks with missing composer information:

SELECT Name 
FROM Tracks 
WHERE Composer IS NULL;

Wrapping Up

Mastering these foundational SQL commands, SELECT, FROM, WHERE, LIMIT, ORDER BY, GROUP BY, IN, NOT, BETWEEN, and NULL is your first step to becoming a confident data analyst. Each command is like a puzzle piece that helps you construct meaningful queries.

If this newsletter inspired you, share it with someone who is starting their data journey. Your support helps me keep writing and creating helpful content for aspiring data professionals. Let us grow together!

Share

👋🏽 Hey, it’s Ismail. Welcome to data nomads lab newsletter on learning data analytics, career growth, networking, building portfolios, and interview skills to break into tech role as a high-performer.

Hey there, future data explorer!

Before we dive in, I want to highlight something important: Many SQL courses skip over ERDs and database relationships, jumping straight into SELECT statements and queries.

This is a huge mistake!

Understanding how tables are connected and what data they contain is crucial - it’s like trying to navigate a city without looking at a map first.

This knowledge will make writing SQL queries much more intuitive and help you avoid common mistakes.

Trust me, spending time understanding the database “blueprint” will save you hours of frustration later.

Let me show you a real-world example you are probably familiar with - an Amazon order:

Amazon order details showing customer information, shipping address, payment method, and order summary

Looking at this order page, you might see just one screen, but behind the scenes, this information is stored across multiple connected tables in a database:

• A Customers table storing your name and shipping address

• An Orders table with the order date and total

• An OrderItems table listing individual items

• A Products table with item details

• A PaymentMethods table for your payment information

All these tables work together through relationships, which is exactly what we will learn about today using our Chinook database example!

Welcome to our first lesson on SQL and database fundamentals.

Today, we are going to learn about how data is organized in databases using something called an ERD (Entity Relationship Diagram).

We will use a real-world example of a digital music store database called Chinook - think of it as a mini iTunes or Spotify!

What We'll Cover

• What is an ERD and why should you care?

• Understanding tables and their relationships

• Primary keys and foreign keys (with real examples!)

• How to read crow’s foot notation (the special symbols that show relationships)

What is an ERD?

An ERD (Entity Relationship Diagram) is like a map of your database. Imagine you're looking at a blueprint of a music store:

• Each room (table) has a specific purpose

• Doors (relationships) connect different rooms

• Items (data) are organized in specific ways

Here's the ERD for our Chinook database:

Understanding the Tables

Just like how Amazon’s order system connects customer, order, and product information, our Chinook music store database connects artists, albums, and tracks. Let's break down the main tables:

• Artist

  • Contains information about music artists

  • Each artist has a unique ArtistId (primary key)

  • Stores the artist's name

• Album

  • Contains information about albums.

  • Each album has a unique AlbumId (primary key)

  • Contains AlbumTitle and ArtistId (foreign key)

• Track

  • Contains information about tracks (Individual songs).

  • Each track has a unique TrackId (primary key)

  • Contains information like Name, Composer, Duration

• Customer

  • Contains information about customers.

  • Each customer has a unique CustomerId (primary key)

  • Contains FirstName, LastName, Email, etc.

• Invoice

  • Contains information about invoices.

  • Each invoice has a unique InvoiceId (primary key)

  • Links to Customer through CustomerId (foreign key)

• Remaining Tables

  • MediaType, PlaylistTrack, Genre, Customer, InvoiceLine, Employee) follow the same logic as above.

Understanding Crow's Foot Notation

In our ERD, you will notice special symbols at the ends of the lines connecting tables. These are called crow's foot symbols, and they tell us about relationships:

One-to-One (1:1):   ──|─────|── 

One-to-Many (1:N):  ──|─────< 

Many-to-Many (N:M): ──>─────<

Let’s look at real examples from Chinook:

1. Artist to Album (One-to-Many)

Artist ──|─────< Album

This means:

• One artist can have many albums

• Each album belongs to exactly one artist

2. Album to Track (One-to-Many)

Album ──|─────< Track

This means:

• One album can have many tracks

• Each track belongs to exactly one album

3. Customer to Invoice (One-to-Many)

Customer ──|─────< Invoice

This means:

• One customer can have many invoices

• Each invoice belongs to exactly one customer

Primary Keys: The Unique Identifiers

Think of a primary key like a social security number - it’s unique to each record. In Chinook:

Artist Table: 
ArtistId (Primary Key) | Name 
1                      | AC/DC 
2                      | Accept 
3                      | Aerosmith

Every table in Chinook has a primary key that ends with "Id" - this makes them easy to spot!

Foreign Keys: Creating Connections

Foreign keys are how we connect tables together. Let’s look at the Album table:

Album Table: 
AlbumId (Primary Key) | Title               | ArtistId (Foreign Key) 
1                     | For Those About...  | 1 
2                     | Balls to the Wall   | 2 
3                     | Restless and Wild   | 2

The ArtistId in the Album table points back to the Artist table, creating a connection.

Real-World Relationships in Chinook

1. One-to-One (1:1)

   • Employee and their manager (an employee can have one manager)

   • Not very common in Chinook, but important to understand

2. One-to-Many (1:N)

   • Artist → Albums (one artist has many albums)

   • Album → Tracks (one album has many tracks)

   • Customer → Invoices (one customer has many invoices)

3. Many-to-Many (N:M)

   • Playlist ↔ Tracks (through PlaylistTrack table)

   • One playlist can have many tracks

   • One track can be in many playlists

Want to see these concepts in action? Check out my TikTok video where I break down these concepts visually:

Practice Exercise

Open DB Browser for SQLite and explore Chinook:

1. Look at entity relationship diagrams (ERD) above

2. Find all tables columns that end with “Id” (these are primary keys)

3. Look for columns that reference other tables (foreign keys)

4. Try to draw the connections between Artist, Album, and Track

Key Takeaways

• ERDs show us how data is connected in our database

• Primary keys uniquely identify each record

• Foreign keys create relationships between tables

• Crow's foot notation tells us what type of relationship exists

• Real-world data is usually connected through one-to-many relationships

Next Week

We will start writing our first SQL queries to extract information from the Chinook database.

Now that you understand how the data is organized, writing queries will make much more sense!

Bonus Challenge

Try to find all the foreign keys in the Track table. What other tables is it connected to, and why do these connections make sense for a music store?

Happy learning!

If this newsletter inspired you, share it with someone who is starting their data journey. Your support helps me keep writing and creating helpful content for aspiring data professionals. Let us grow together!

Share

👋🏽 Hey, it’s Ismail. Welcome to data nomads lab newsletter on learning data analytics, career growth, networking, building portfolios, and interview skills to break into tech role as a high-performer.

Want to break into data analytics without a degree?

Here is how to create your first project this weekend.

Here is an uncomfortable truth: I used to think building a portfolio-worthy analytics project was complicated. Advanced coding, fancy visualizations, expensive software.

I was wrong.

Let me tell you about one of my students.

This student had been learning analytics for months, dabbling in Excel and Tableau, but felt stuck. Like many beginners, they thought creating a portfolio project would take weeks.

Just like many of you noticed, the same question kept popping up in LinkedIn posts:

How do I get started analyzing real-world data sets?

Instead of reading another tutorial or watching another video, they took action. On Saturday morning, hey downloaded a free Kaggle e-commerce dataset. That afternoon, they analyzed trends and built three simple Tableau dashboards showing sales, customer behavior, and product performance.

By Sunday night, they had:

• A clean, insightful dataset

• Three polished dashboards

• A one-page write-up explaining their process

The results?

They shared everything on LinkedIn and Github. Within a week, they connected with recruiters and a hiring manager. While not a job offer, it was progress.

It really can be that simple to get started.

The beginner portfolio method

Here is the framework this student used (and why it works so well):

1. Find ONE simple, real-world dataset

2. Analyze ONE specific question or theme

3. Create ONE polished visualization (or a few)

4. Write ONE short explanation of your findings

Here is the thing: most beginners overthink this. They try to tackle massive projects that cover everything, but in reality, your future employers do not want everything, they want to see how you approach problems.

Focus on solving one specific problem with your project.

Resources to jumpstart your first project

Here are some excellent resources to get started with data analytics and visualization.

• Data Analytics by Quantium:
  Explore the power of data and its ability to power breakthrough possibilities for individuals, organizations, and societies.

• Data Analytics & Visualization by Accenture:
  Apply your data analytics and visualization skills to advise a social media client on their content creation strategy.

• Power BI by PwC in Switzerland:
  Use Power BI to clean, wrangle, and visualize data to help a telecom client better understand their customers and employees.

• Data Visualization: Empowering Business with Effective Insights by TATA:
  Learn how to create impactful visualizations around important business questions and present findings to executives.

• E-Commerce Dataset on Kaggle:
  This fictional e-commerce dataset is perfect for practicing data analysis, visualization, and predictive modeling.

Quick win for today: Explore these business questions

If you are using the Kaggle e-commerce dataset, try answering some of these questions:

• What are the most popular product categories based on transaction volume?

• What is the average transaction amount across all purchases?

• How does the average product price vary across different categories?

• What is the distribution of discounts offered in transactions?

Spend a couple of hours exploring a dataset, build a simple dashboard or analysis, and share what you learned.

Did this post help you feel more confident about starting your first data project? Share your thoughts in the comments. Which of these resources are you most excited to try?

Leave a comment

If this newsletter inspired you, share it with someone who is starting their data journey. Your support helps me keep writing and creating helpful content for aspiring data professionals. Let us grow together!

Share

👋🏽 Hey, it’s Ismail. Welcome to data nomads lab newsletter on learning data analytics, career growth, networking, building portfolios, and interview skills to break into tech role as a high-performer.

Dear learners,

Today, I want to have an honest conversation about something we all face at some point: really understanding ourselves and finding our place in the work world. No fancy jargon, just real talk from the heart.

The power of looking inward You know that feeling when someone asks “What do you want to do with your life?” and your mind goes blank? Let us talk about why self-discovery is not just another buzzword, but actually the foundation of a fulfilling career. Think of it like getting to know yourself as you would a close friend - understanding your strengths, acknowledging your weaknesses, and recognizing what truly lights you up inside.

The AI revolution: friend, not foe Here is something interesting I have noticed in the data/business world: while everyone is worried about AI taking jobs, the reality is quite different. The real winners are turning out to be people who know how to work alongside AI. It is not about competing with machines; it is about being smart enough to use them as tools. If you are worried about AI, here is a perspective shift: focus on becoming someone who knows how to harness its power.

Market forces that matter Speaking of changes, let us talk about what is actually happening in the job market:

• Remote work is not just a trend - it is reshaping how we think about careers

• The rise of “quiet hiring” (when companies develop talent from within)

• The growing importance of showing your work online (80% of job seekers aged 18-34 found their last job through social media!)

A personal note Something I have learned in my own journey: your career path does not have to be a straight line. The key is staying adaptable and keeping your skills fresh. Think about this: companies are increasingly valuing skills from short courses and certificates. It is not just about traditional degrees anymore.

Quick exercise for you Before you close this email, take a moment to jot down three things:

• What tasks make you lose track of time?

• What problems do you enjoy solving?

• What would you do even if you were not paid for it?

These simple questions can reveal more about your ideal career direction than any complex personality test.

Looking ahead Next week, we will dive into practical ways to translate these insights into actual career moves. Until then, remember: the best career decisions come from understanding yourself first.

Would love to hear your thoughts! What is your biggest challenge in finding your career direction?

Stay curious,

What resonates most with your own journey of self-understanding? Share your thoughts below and let’s learn from each other’s experiences.

Leave a comment

If this resonated with you, please share it with someone who might need this perspective today.

This spreads the word and keeps me writing content that will help you break into tech and “data nomads lab” life. 🙏

Share

You are halfway through mastering Excel’s VLOOKUP / XLOOKUP when suddenly everyone is talking about Power Query. You have just gotten comfortable with basic SQL queries, and now your LinkedIn is flooded with posts about stored procedures and window functions. The Tableau dashboard you have been building? There is a new Power BI feature that promises “enhanced interactivity.”

Welcome to the tech learning treadmill, where the speed keeps increasing but the destination seems to stay just as far away.

The eternal student’s dilemma

As a career-changer diving into data analytics, you are not just learning, you are trying to hit a moving target. The tools, frameworks, and “must-know” technologies shift faster than any human can reasonably keep up with. It is not just about learning anymore; it is about learning while sprinting.

But here is what no one tells you: this is not a sprint. It is not even a marathon. It is a dance.

The hidden truth about tech evolution

The pace of technology is not your enemy, it is your opportunity. Here is why:

1. Core Principles remain: While tools change, fundamental business concepts stay remarkably stable. Understanding how to clean data, create meaningful relationships, and build impactful visualizations matters more than mastering the latest features in any specific tool.

2. Not Everything new is necessary: Despite the hype, most companies still run on “boring” technology. They need people who can solve problems, not just those who know the latest frameworks.

3. Experience compounds: Each new tool you learn makes the next one easier to grasp. Your learning speed increases naturally over time.

Practical strategies for staying current

1. The 80/20 approach to new technology

• Spend 80% of your time mastering fundamentals

  • Learn SQL queries for data extraction and manipulation

  • Practice your Excel formulas, pivot tables, and data modeling

  • Learn proper data cleaning and validation techniques

  • Understand business metrics and KPI development

  • Practice clear data visualization principles

• Reserve 20% for exploring new tools and trends

  • Experiment with Power BI if you are using Tableau

  • Try Power Query if you are comfortable with basic Excel

  • Explore DAX formulas when you are solid with Excel functions

  • Test new SQL features like Common Table Expression (CTEs) after mastering basic queries

2. Build Your learning filter

• Follow 3-5 trusted tech curators instead of trying to read everything

• Wait for technologies to prove themselves before diving deep

• Focus on solving problems, not collecting tools

3. Create your “technology radar”

• Inner circle: Tools you need to master

• Middle circle: Tools to be familiar with

• Outer circle: Tools to be aware of

The sustainable path forward

Remember: The goal is not to learn everything, it is to learn the right things at the right time. Your journey into tech is not a race against others or against technology itself. It is about steady progress, strategic choices, and sustainable growth.

Your journey starts now

Remember, every successful business analyst started exactly where you are. They faced the same overwhelming tools, the same doubts, and the same fear of falling behind. But here is the truth: the most valuable asset in business analytics is not the person who knows every tool, it is the person who knows how to solve business problems effectively with the tools they have mastered.

Start small, but start today:

• Pick one Excel function you do not know and master it this week

• Create one simple dashboard that answers a real business question

• Write one SQL query that makes your daily work easier

Let us get something straight about data analysis & business analytics.

There are two types of people in this field:

• Those waiting to be given a chance

• Those who create their own opportunities

The first group is stuck in the traditional mindset. They are collecting certifications, polishing resumes, and praying someone picks them from the stack of applications.

They are playing someone else’s game.

You know the type. They ask questions like:

• Which bootcamp should I join?

• What degree do I need?

• How many years of experience before I can apply?

The truth? The data analytics field does not care about your credentials. It cares about what you can do with data.

I recently hosted an AMA on Reddit about breaking into data analytics. The response was overwhelming. But here is what struck me - most participants were asking for permission to enter a field that is already wide open.

This guide is not about getting permission. It is about taking control.

If you are looking for a comfortable checklist of courses to take, stop reading now. But if you are ready to learn how real data analysts are built - through practical experience, real projects, and strategic networking - then keep going.

What follows is everything I have learned about breaking into and succeeding in data analytics, distilled from years of experience and hundreds of questions from aspiring analysts.

No fluff. No sugar-coating. Just actionable insights that work.

The Truth About Networking

Forget everything you have been told about “professional networking.” Here is what actually works:

• Do not ask for jobs. Create value first.

• Do not collect LinkedIn connections like Pokemon cards.

• Do not waste time at generic networking events.

Instead:

• Build something worth talking about

• Share your work publicly

• Help others solve problems

That is it. That is networking.

Portfolio Building: Stop Playing It Safe

Here is another hard truth: Nobody cares about your tutorial projects.

Want to stand out? Here is what you do:

• Find a real problem that needs solving

• Use public data to solve it

• Document every step

• Share your failures and learnings

One messy, real-world project is worth more than ten perfect tutorial follow-alongs.

The Skills That Actually Matter

Everyone will tell you about the technical skills you need. Here is what they will not tell you:

Technical skills are the bare minimum. Here is what actually sets successful analysts apart:

Business Understanding:

• Know why the data matters

• Understand what drives business decisions

• Learn to speak both data and business languages

Problem-Solving:

• Figure out what business questions to ask

• Know which problems are worth solving

• Learn to work with imperfect data

Communication:

• Explain complex findings simply

• Create visualizations that tell stories

• Write reports people actually want to read

Making Your Mark

Once you land that first job, here is how you become unforgettable:

Do not:

• Wait for assignments

• Stay in your lane

• Keep your head down

Instead:

• Find the messiest problems

• Volunteer for the hard stuff

• Be really great at what you do

  • Make yourself the go-to person for something specific

The Real Learning Path (With Actual Resources)

Forget expensive bootcamps. Here is your actual curriculum:

1. Learn the foundations:

   • SQL: Start with SQLZoo (free), graduate to StrataScratch (real problems)

   • Excel: Microsoft's free Excel course, then ExcelIsFun on YouTube

   • Statistics: Khan Academy Statistics, then StatQuest YouTube channel

   • Python: Google's Python Course on Coursera, then Real Python website

2. Free Resources That Matter:

   • Google Data Analytics Certificate on Coursera ($49/month)

   • DataCamp's Interactive Python Course (free introduction)

   • FreeCodeCamp's Data Analysis with Python (completely free)

   • Mode Analytics SQL Tutorial (free, industry-focused)

3. Where to Find Real Projects:

   • Kaggle Datasets (kaggle.com/datasets)

   • Google Dataset Search (datasetsearch.research.google.com)

   • Data.gov (government data)

   • WHO, World Bank datasets (global health, economic data)

4. Communities Worth Joining:

   • DataTalks.Club (Slack community)

   • Reddit (r/dataanalysis, r/datascience)

   • LinkedIn Groups (Data Analytics Professionals)

   • Local Data Meetups (meetup.com)

5. Daily Practice Resources:

   • 8weeksqlchallenge.com (real-world SQL problems)

   • analyticsvidhya.com (practice problems)

   • stratascratch.com (interview prep)

   • datalemur.com (tech interview questions)

Remember: These are not just links. These are your tools. Use them daily.

Where The Money Is

Let us talk about the industries that are desperate for data analysts:

Healthcare:

• Massive amounts of patient data

• Critical decision support

• High stakes, high rewards

Finance:

• Risk analysis

• Fraud detection

• Market intelligence

Tech:

• Product analytics

• User behavior

• Growth metrics

But here is the secret: Pick an industry you actually care about. Passion beats trendy every time.

The AI Question

Everyone is asking about AI. Here is the reality:

AI is not replacing analysts. It is replacing bad analysts.

Want to stay relevant? Here is how:

• Learn to work with AI, not against it

• Focus on the questions AI cannot ask

• Build skills in data interpretation

• Master the human side of analytics

The Path Forward

The market does not care about your excuses. It cares about results.

You have two choices:

1. Keep waiting for permission

2. Start building your future today

If you have chosen option 2, here is what you do next:

• Pick a real problem

• Start solving it

• Share your journey

• Help others

• Repeat

No more waiting. No more excuses. No more permission seeking.

The field is wide open. Take it.

Remember: The best time to start was yesterday. The second best time is now.

Get to work.

Last time we talked about different types of data. Today, let us learn how to make that data visual and engaging. Think of data visualization like the difference between reading a recipe and watching a cooking show - both tell you how to make the dish, but one is way more engaging!

Why do we need data visualization?

Before we jump in, let us talk about why visualization matters:

• It helps spot patterns and trends quickly

• Makes complex information digestible

• Helps communicate insights to others

• Makes it easier to compare different metrics

• Helps identify outliers and unusual patterns

Let us look at the visualizations in action!

The most common Charts: Bar Charts, Line Charts, Pie Charts, Histogram and Scatterplot. These chart types, or a combination of them, provide answers to most questions with relational data (data stores in tables).

I have created some charts using our coffee shop data below. Each one tells a different story about our business.

Understanding each chart type

1. Bar Charts: The reliable workhorse

Best for:

• Comparing categories (like drink types)

• Showing counts or frequencies

• Discrete data visualization

When to use it: Use bar charts when you want to compare different categories or show the frequency of different items. In our coffee shop example, it perfectly shows which drinks are our bestsellers.

Pro tip: Use horizontal bars when you have long category names!

When creating a bar chart, do not:

• Make each bar a different width

• Cram too many bars into subcategories

• Leave the axes unlabeled

2. Line Charts: The trend spotter

Best for:

• Time series data: What patterns appear in the data over time?

• Showing trends and patterns

• Continuous data over time

• Is one category performing better than other sets of data?

When to use it: Perfect for showing how your sales or customer traffic changes over time. It helps identify patterns like peak hours or seasonal trends.

3. Pie Charts: The part-to-whol

e visualizer

Best for:

• Showing proportions of a whole

• Comparing parts to the total

• Limited number of categories (ideally ≤ 6)

When to use it: Great for showing the breakdown of your total sales by category or the distribution of drink sizes. Just remember - too many slices make the pie hard to digest!

When to avoid pie charts:

• Your data has more than 5 categories (use a bar chart instead)

• Categories have very similar values (makes segments hard to compare)

• Values do not sum to 100% (breaks the “part of whole” concept)

• Data includes negative numbers or complex fractions

4. Histograms: Understanding your data's distribution

Best for:

• Showing distribution of continuous data

• Understanding the spread of values

• Identifying patterns in frequency

When to use it: Perfect for analyzing the distribution of order values, wait times, or daily customer counts. It helps you understand what's "normal" for your business and spot unusual patterns.

Avoid using a histogram if:

• Looking at relationships between multiple variables

• Data needs to be normalized or rescaled first

• You need to see exact individual values rather than general patterns

5. Scatter Plots: Finding relationships in your data

Best for:

• Showing relationships between two variables

• Identifying clusters

• Spotting outliers

When to use it: Perfect for exploring relationships, like whether longer wait times affect order values, or if there is a connection between time of day and order size.

Reading correlations:

• No correlation: Points spread randomly - no relationship between variables

• Positive correlation: As one variable increases, the other tends to increase

• Negative correlation: As one variable increases, the other tends to decrease

For example, in a coffee shop:

• Positive correlation: More customers → Higher daily revenue

• Negative correlation: Longer wait times → Lower customer satisfaction

• No correlation: Drink temperature → Time of purchase

Common mistakes to avoid

1. Wrong chart type: Do not use a pie chart for time series data!

2. Too much information: Keep it simple and focused

3. Misleading scales: Always start your y-axis at zero for bar charts

4. Poor color choices: Use colors that are colorblind-friendly

5. Missing context: Always include clear labels and titles

Quick decision guide

Not sure which chart to use? Here is a simple guide:

• Comparing categories? → Bar chart

• Looking at trends over time? → Line chart

• Showing parts of a whole? → Pie chart

• Exploring relationships? → Scatter plot

• Understanding distributions? → Histogram

Practice time!

Look at your own coffee shop data (or any business data you have) and try creating these different visualizations. Ask yourself:

1. What story am I trying to tell?

2. Who is my audience?

3. Which visualization best serves my purpose?

Share your visualizations in the comments below! I would love to see how you are applying these concepts to your own data.

Stay curious!
Ismail Osman

Want to practice more? Try creating different visualizations for the same dataset and see how the story changes. Drop your insights in the comments!

I have been getting a lot of messages asking about data types, and I totally get it - this stuff can be confusing! So I thought, why not break it down using something we all love? Coffee! ☕

Let me walk you through different types of data using our coffee shop dataset. Trust me, it is going to be way more fun than those dry textbook explanations.

Quantitative vs Categorical: The big divide

Think about all the information you collect at a coffee shop. Some things you can count or measure (that is quantitative), and others you can put into categories (you guessed it - categorical).

Quantitative data is anything you can do math with:

• How many cups of coffee you sell each day (450 cups)

• The price of each drink ($3.77)

• How many minutes customers wait in line (3 minutes)

• The temperature of your coffee

Categorical data is anything that fits into groups:

• Types of drinks (latte, cappuccino, americano)

• Customer payment methods (cash, card, apple pay, bitcoin)

• Whether a customer ordered food with their drink (yes/no)

• Size of the drink (small, medium, large)

Diving deeper: Let us talk about subcategories

Here is where it gets interesting! Both quantitative and categorical data have their own subcategories. I know what you are thinking - Please, not more categories! But stick with me, I promise to keep it simple.

Quantitative data: Discrete vs Continuous

Discrete data is like counting whole things:

• Number of customers per day (you cannot have 45.7 customers)

• Number of espresso shots in a drink

• Number of pastries sold

Continuous data can take any value within a range:

• Exact drink temperature (94.6°F)

• Time spent in line (3.5 minutes)

• Amount spent ($4.75)

Categorical data: Nominal vs Ordinal

Nominal data is categories without any natural order:

• Drink types (a latte is not "more than" an americano)

• Payment methods (cash is not "greater than" card)

• Customer names (alphabetical order does not mean anything here)

Ordinal data has categories with a clear order:

• Drink sizes (small < medium < large)

• Customer satisfaction ratings (1 star < 2 stars < 3 stars)

• Loyalty levels (bronze < silver < gold)

Why does this matter?

You might be wondering why we need to care about all these types. Here is the deal - knowing your data types helps you:

1. Choose the right visualizations

   • You would not make a line graph for drink types, but it works great for daily sales

2. Pick the right analysis methods

   • You cannot calculate the average of payment methods

   • But you can definitely find the average waiting time

3. Make better business decisions

   • Understanding which type of data you are looking at helps you ask the right questions

   • It prevents you from making incorrect comparisons or calculations

Pop quiz: Test your understanding!

Before we look at our coffee shop data, let us test what you have learned! Below are 10 different types of data you might encounter in various business scenarios. Try to classify each one into the correct data type.

Remember our categories:

• Quantitative (Discrete or Continuous)

• Categorical (Nominal or Ordinal)

1. Zip Code

2. Age

3. Income

4. Marital Status (Single, Married, Divorced, etc.)

5. Height

6. Letter Grades (A+, A, A-, B+, B, B-, ...)

7. Travel Distance to Work

8. Ratings on a Survey (Poor, Ok, Great)

9. Temperature

10. Average Speed

Think about each one carefully! Consider:

• Can you do meaningful math with it?

• Are there natural ordered categories?

• Can it take any value within a range?

🤔 Take a moment to write down your answers and share it the comments below. I will share the answers once there are enough responses in the comments.

Real talk: Looking at our coffee shop data

Let us look at some actual fields from our coffee shop dataset and categorize them:

Order ID: Discrete quantitative (whole numbers only) 
Drink type: Nominal categorical 
Size: Ordinal categorical 
Price: Continuous quantitative 
Temperature: Continuous quantitative 
Customer rating: Ordinal categorical 
Time of order: Continuous quantitative

Pro tip

Here is a simple trick I use: If you can do meaningful math with it (add, subtract, find the average), it is quantitative. If you cannot, it is categorical. And if it is categorical, ask yourself: Does the order matter? If yes, it is ordinal. If not, it is nominal.

Want to dive deeper?

Ready to start working with these different data types? Head over to my earlier post 👉 Coffee shop statistics: real-world data made simple, where we dive into descriptive statistics using our coffee shop data. You will see exactly how understanding these data types helps us choose the right statistical methods and make sense of our business metrics.

Until then, try this: Next time you are at a coffee shop, look around and try to identify different types of data they might be collecting. It is a fun way to practice, and yes, I am that person who turns everything into a data exercise! 😄

Would love to hear your thoughts! What other real-world examples help you understand data types? Drop them in the comments below.

Stay curious,
Ismail Osman

If you found this helpful, do not forget to like and share! And hey, if you have not subscribed yet, what are you waiting for? Next week is going to be even better!

I am sitting at my favorite locally owned coffee shop, sipping a perfectly foamy cappuccino with honey and cinnamon on top, just how I like it. The place is full of life today. A family plays Monopoly in the corner. Students study with their textbooks spread out. An older couple works on a crossword puzzle by the window. Kids are setting up board games at the big table.

Looking at all these people going about their day, I start thinking about patterns. Numbers are like that, too - they tell simple stories about everyday life. Let me show you what I mean.

The fear is real (but it does not have to be)

1. Common fear I hear: I will never understand all these numbers.

2. Common frustration: Why cannot someone explain this simply?

3. Common desire: I want to see how this works in real life.

Let me show you how I turned these worries into wins using only coffee shop numbers.

Sample coffee shop numbers

Here are our coffee sales per hour:

Frustration #1: There are too many numbers!

Morning Rush (7-9 AM):

• These are your busy hours

• Sales: 100, 150, 150 cups

Mid-Day (10 AM-12 PM):

• Your steady hours

• Sales: 80, 80, 80 cups

Afternoon (1-3 PM):

• Your quiet hours

• Sales: 60, 40, 40 cups

See? Already less overwhelming.

Fear #2: What if I calculate something wrong? let us break this down - finding the center

Most Common Number (Mode):

• Count how many times each number appears

• 150 appears 2 times

• 80 appears 3 times

• 40 appears 2 times

• 80 is your mode - that is your most typical hour

That is it! Just counting. No complex math.

Desire #3: I want to make better business decisions

Let us say you need to order cups for next week:

Average (Mean):

• Add all numbers: 100 + 150 + 150 + 80 + 80 + 80 + 60 + 40 + 40 = 780

• Divide by how many hours (9)

• 780 ÷ 9 = 87 cups per hour average

But wait! Sometimes average is not your best friend.

Middle Number (Median):

• Line up all numbers: 40, 40, 60, 80, 80, 80, 150, 150

• Find the middle: 80

• This ignores those super busy and super quiet times

Quick coffee tip: Imagine your coffee shop typically sells 80 cups each hour, but one day a huge business conference orders 500 cups for their event. Your average for that day would jump way up to 122 cups per hour ((8 × 80 + 500) ÷ 9 = 122). But is that really a “typical” hour? This is when median saves the day - it would still show 80 cups as your middle number, giving you a more realistic picture of your usual business.

Another example: What if a snowstorm hits and you only sell 5 cups during one hour? Your daily average would drop unusually low, but median would still show your typical 80 cups, helping you plan better for normal days.

Fear #4: But what about all the ups and downs?

Range (Highest to lowest):

• Highest number: 150

• Lowest number: 40

• Range = 150 - 40 = 110 cups This tells you how much your sales bounce around

Middle Range (IQR):

• Take the middle 50% of your numbers

• Highest middle: 150

• Lowest middle: 60

• Middle range = 150 - 60 = 90 cups - This tells you your normal bouncing around, ignoring extreme times

Real talk: why this matters

Remember that student fear: I will never get this? Here is what actually happens:

• You learn to count what happens most (mode)

• You learn to find the middle ground (median)

• You learn to see patterns in your busy and quiet times

Turning fears into power moves

1. When to use Average:

   • Ordering supplies for the whole day

   • Planning overall revenue

   • Making long-term predictions

2. When to use Middle Number (Median):

   • Setting staff expectations

   • Planning typical scenarios

   • Ignoring those crazy busy or super slow times

3. When to use Most Common Number (Mode):

   • Scheduling regular staff

   • Preparing standard inventory

   • Understanding your usual business

The big win

One of my students said it best: “I used to panic when I saw numbers. Now I see stories. I see busy mornings, lazy afternoons, and all the coffee in between.”

That is what statistics really is - finding the stories in your numbers.

Your turn

Next time you are at a coffee shop:

• Count how many people are in line at different times

• Notice the busy and quiet patterns

• Think about what “typical” means for that shop

Congratulations! You are already doing statistics.

Stay curious (and caffeinated)!

Ismail Osman

Share your own coffee shop number stories below. What patterns have you noticed at your local cafe?

I have analyzed over 10 high-priced data analytics courses offered online, and here is what I discovered:

• Most of them skip the foundational step of teaching statistics.

• Instead, they jump straight into tools like SQL, Tableau, or Power BI.

• But without a solid grounding in statistics, you are left building a house without a foundation.

If you have ever taken an online data analytics course or bootcamp, you might have noticed this gap. Statistics is the language of data, it’s where numbers come alive, whisper their secrets, and paint vivid pictures of hidden truths. Once you get it, you will start seeing patterns everywhere, even in your grocery list!

For over 10 years, I have worked in the field of data analytics, and for the past six years, I have had the privilege of teaching business and data analytics at one of Silicon Valley’s top online platforms, Udacity.com.

During this time, I have interacted with over 700 students, helping them break into tech careers.

One highlight of my journey has been contributing to the Udacity/OneTen scholarship program, which is dedicated to increasing Black representation in the tech industry.

These experiences have taught me what works and what does not when it comes to mastering data analytics.

Starting next week, I will be posting weekly lessons to help you build a strong foundation in data analytics. These lessons will not feel like a boring classroom lecture. They are simple, clear, and practical, dsigned to help you learn skills you can use right away.

Here’s what you can expect:

1. Descriptive Statistics (Lesson 1): Learn how to summarize and understand your data with concepts like averages, medians, and ranges. This is where every great data analyst starts.

2. Business Metrics (Lesson 2): Understand the numbers that businesses care about most, like revenue, profit, and growth rates.

3. Excel (Spreadsheets) (Lesson 3): Master the basics of organizing and analyzing data with Excel, a must have skill for any analyst.

4. SQL (Lesson 4): Dive into databases and learn how to pull the right data for your analysis.

5. Data Visualization (Lesson 5): Bring your data to life with tools like Tableau or Power BI.

But I will not stop there. Once you have the technical skills, we will focus on the soft skills that help you land a job and grow your career, like:

1. How to optimize your LinkedIn profile and build your network.

2. How to create a portfolio that stands out.

3. How to tailor your resume to specific job postings.

4. How to prepare for interviews like a pro.

Every week, I will share a new lesson with clear examples and actionable steps. My goal is to make these lessons simple enough for anyone to follow, whether you’re just starting or looking to sharpen your skills.

If you have ever felt overwhelmed by all the tools and jargon in data analytics, this is the newsletter for you. Let’s take it one step at a time and build your confidence together.

See you in Lesson 1: Descriptive Statistics!

Stay curious

As part of Udacity’s & OneTen scholarship program, aimed at increasing black representation in tech. Focused on helping people without a 4-year degree make the jump into high-demand digital roles.

• Over the past four years, I’ve had the honor of working with over 700 students

• From all walks of life - no four-year degree? No problem

• Instructors. Graders. Support. Every step of the way

What I have learned from students:

• Many want to switch from low-paying jobs to high-paying tech roles

• Others seek the flexibility of working from home, especially with digital skills

• Some are students now – hoping to switch their major into data analytics

• Some love their current role but want to grow data skills for promotion

The common challenge?

• How to actually break into data analytics

• It’s not just technical skills

• They need interview skills. LinkedIn polish, and a resume that works

• A strong portfolio that shows off what they can do

My goal with this newsletter:

• I want to bring everything I’ve learned to a larger audience

• Practical tips, resources, and insights from real students’ journeys

• This newsletter will help you skip the guesswork

• So, if you see yourself in these stories…….

Join me – let’s make tech careers more accessible, together

Real voices, real impact – feedback from my students who have been on this journey with me. Most recent cohort.

Share Data Nomads Lab