With one table, that’s easy to hold in your head. Add a JOIN, and one question trips up almost everyone: when you combine two tables and then filter them, what runs first?

The JOIN. It builds the combined table before any filter touches a row, and missing that is the source of most multi-table bugs.

So today we trace it step by step, on one real query.

The order, with a JOIN in it

FROM / JOIN → WHERE → GROUP BY → HAVING → SELECT → ORDER BY → LIMIT

JOINs don’t add a new step. They live inside step one. Everything after that runs on the joined result, not on your original tables.

The query

Two tables. employees has each person’s name and field. employee_company_data has their salary and experience.

The goal: find the field with the highest average salary, among employees with 5+ years of experience.

SELECT
      e.Field,
      AVG(ed.Salary) AS Avg_salary,
      COUNT(e.ID)    AS num_answers
FROM employees AS e
LEFT JOIN employee_company_data AS ed
  ON e.ID = ed.ID
WHERE ed.Experience_years > 5
GROUP BY e.Field
HAVING COUNT(e.ID) >= 5
ORDER BY Avg_salary DESC
LIMIT 1

Here's the order SQL actually runs it in.

#1 FROM - Defining the Base Table(s)

This is where SQL starts everything. It determines which table(s) we’re working with.

➡️ If we’re using JOINs, SQL processes them here before doing any filtering!

FROM employees AS e
LEFT JOIN employee_company_data AS ed
  ON e.ID = ed.ID

At this point, SQL combines the tables based on the JOIN condition. The type of JOIN (INNER, LEFT, RIGHT, FULL) affects which rows stay in the result set.

#2 WHERE - Filtering Rows

Now that we have a combined dataset, SQL filters individual rows before aggregation.

If you filter before the JOIN, you might accidentally remove rows you actually need.

WHERE ed.Experience_years > 5

#3 GROUP BY - Aggregating Data

Once SQL has the right rows, it groups similar data into categories.

This is especially useful when calculating averages, totals, or counts per category.

GROUP BY e.Field

#4 HAVING - Filtering Groups

HAVING is like WHERE’s big sibling, but for grouped data.

➡️ It filters after GROUP BY, meaning it only works with aggregated values.

HAVING COUNT(e.ID) >= 5

This keeps only fields with 5 employees or more, enough for the average to mean something.

#5 SELECT - Picking the Columns

Now SQL decides what to show in the final output.

You can only select columns that are in GROUP BY or are aggregated values.

SELECT
Field,
AVG(ed.Salary)  AS Avg_salary,
COUNT(e.ID)     AS num_answers

#6 ORDER BY - Sorting the Output

Now that we have our final dataset, SQL sorts the results based on our preferences.

ORDER BY Avg_salary DESC

This arranges fields from highest to lowest average salary.

#7 LIMIT - Keeping it Concise 🔢

If we only want to see a single result, we limit the number of rows returned.

LIMIT 1

And we land on the answer: Engineering, the field with the highest average salary.

Same query, a completely different order from the one on the page, with every step running on what the one before it produced. That is execution order in action.

Once you can picture that flow, the JOIN mistakes that trip most people up become easy to spot before they happen. Here are three.

Three mistakes this prevents

1. Filtering before you picture the join. A JOIN can add or drop rows. Build the combined table in your head first, then reason about WHERE.

2. Using WHERE for grouped data. Any condition on COUNT, SUM, or AVG belongs in HAVING. WHERE runs before the aggregate exists.

3. Selecting ungrouped columns. After GROUP BY, every column in SELECT must be in the GROUP BY or wrapped in an aggregate.

Build the joined table first, then filter, group, and filter the groups. Hold that order and multi-table queries stop surprising you.

Here's this week's cheatsheet 👇🏻

—Josep

Are you still here? 🧐

👉🏻 I want this newsletter to be useful, so please let me know your feedback!

Before you go, tap the 💚 button at the bottom of this email to show your support—it really helps and means a lot!

Any doubt? Let’s start a conversation! 👇🏻

Leave a comment

Want to get more of my content? 🙋🏻‍♂️

Reach me on:

• LinkedIn and X (Twitter) to get daily posts about Data Science.

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• Just email me at rfeers@gmail.com for any inquiries or to ask for help! 🤓

The one you wrote. And the one your database actually uses to run it.

That’s it. Strip away the syntax, and that’s what’s happening underneath: the clauses you typed top to bottom get quietly reshuffled into a completely different sequence before a single row is touched.

Most people learn to write SQL. Almost no one learns how it runs.

And that gap (between writing order and execution order) is where the confusing errors live. The alias that “doesn’t exist.” The WHERE clause that refuses to filter your COUNT(). The query that flies on 100 rows and crawls on 100 million.

None of it is random. It all traces back to one thing: the order in which the engine actually processes your query.

I’ve written more SQL than I can remember, and I still run that order in my head every time a query surprises me. It’s the single mental model that’s saved me the most debugging time.

Today’s issue breaks down what it means for SQL to be declarative, why writing order and execution order diverge, and what happens at each of the seven steps.

SQL tells the database what, not how

SQL is a declarative language. You describe the result you want; you don’t spell out the steps to compute it.

Think of ordering at a restaurant. You ask for the dish. You don’t walk into the kitchen and dictate which pan gets heated first, when to salt, how to plate it. You declare the outcome, the kitchen decides the sequence.

SQL works the same way. You declare the shape of the result, and the engine’s query planner decides how to get there. Which means it’s free to evaluate your clauses in whatever order is logically correct, regardless of the order you typed them.

So before anything else, it’s worth separating the two orders in your head.

Coding order vs. Execution order

This is the structure you write, almost every time:

SELECT → FROM → WHERE → GROUP BY → HAVING → ORDER BY → LIMIT

And this is the order the engine actually runs it in:

FROM / JOIN → WHERE → GROUP BY → HAVING → SELECT → ORDER BY → LIMIT

Look at where SELECT lands.

You write it first. It runs fifth.

That one detail causes more SQL confusion than anything else, and we’ll come back to exactly why in a moment. This logical sequence even has a name: logical query processing, the term Itzik Ben-Gan has spent a career teaching. The order in which clauses are evaluated, as opposed to the order you read them. Once it clicks, SQL stops feeling arbitrary.

Let’s walk the seven steps.

The seven steps, command by command

#1 — FROM (and JOIN)

The starting point. Before SQL can filter or group anything, it needs the data. FROM identifies the source table, and when your data lives across several tables, JOIN stitches them together right here, first. Everything downstream operates on this assembled result.

#2 — WHERE

Now SQL filters individual rows, keeping only the ones that meet your condition. Crucially, this runs on raw rows, before any grouping exists.

#3 — GROUP BY

SQL collapses rows into groups: one row per distinct value, or combination of values. This is where aggregation lives: COUNT(), SUM(), AVG(), MIN(), MAX().

#4 — HAVING

A second filter, but this one runs after grouping, so it can filter on aggregated values. “Only groups with more than five members” is a HAVING job, never a WHERE job.

#5 — SELECT

Only now does SQL pick the columns you asked for, apply your aliases (AS), and compute final expressions. Fifth. Not first.

#6 — ORDER BY

With the result assembled, SQL sorts it in ascending (ASC) or descending (DESC) order.

#7 — LIMIT

Finally, it trims the output to the number of rows you want. Invaluable when you’re testing against a huge table and don’t want the whole thing back.

Two bugs that suddenly make sense

Here’s the payoff. Two errors every analyst hits, both explained by a single line on that list.

1. WHERE can’t filter an aggregate.

   You try WHERE SUM(minutes) > 100 and SQL rejects it. Of course it does: WHERE (step 2) runs before GROUP BY (step 3). At that point the sum doesn’t exist yet. That’s the entire reason HAVING exists, it’s the filter that runs after aggregation.

2. A SELECT alias is invisible to WHERE.

   You name a column AS total_time, then try to use total_time in your WHERE clause, and you get an error. The alias is born in SELECT (step 5), three steps after WHERE already ran. You’re reaching for something that doesn’t exist yet.

Neither is a quirk. Both fall straight out of execution order.

Watch it run: a worked example

Say a phone company has two tables. CLIENTS holds each customer and their monthly limit. USAGE holds their daily minutes.

The goal: find the clients who blew past their monthly limit.

SELECT DISTINCT
       usage.id,
       clients.owner_name,
       SUM(usage.usage_minutes) AS total_browsing_time,
       clients.browsing_minutes_limit
FROM usage
LEFT JOIN clients
  ON clients.ID = usage.ID
WHERE usage.Date > “2023-05-01” AND usage.Date < “2023-06-01”
GROUP BY 1,2,4
HAVING SUM(usage.usage_minutes) > clients.browsing_minutes_limit
ORDER BY clients.browsing_minutes_limit DESC

You read that from SELECT down. The engine runs it like this:

1. FROM + JOIN merge CLIENTS and USAGE into one combined table.

2. WHERE drops every usage record outside of May.

3. GROUP BY rolls each client’s daily rows into a single total.

4. HAVING keeps only the clients whose total exceeded their limit — a filter on an aggregate, only possible now.

5. SELECT picks the columns and surfaces the summed minutes.

6. ORDER BY sorts the offenders by their limit.

7. LIMIT would cap the rows returned (we kept them all here).

Same query. A completely different order from the one on the page — and every step depends on the one before it.

The shift that makes SQL click

Stop reading your queries top to bottom. Read them the way the engine does, and the language stops fighting you.

You’ll know exactly where each clause can reach: why WHERE can’t see your groups, why HAVING can, why your alias works in ORDER BY but not in WHERE. The error messages turn from mysteries into signposts.

Don’t memorize the order — internalize the flow: build the data, filter the rows, group them, filter the groups, then select, sort, and trim. Once you see SQL the way SQL sees itself, the bugs stop being bugs.

Next week: the same order, but with JOINs and a real dataset — where execution order goes from useful to essential.

Here's this week's cheatsheet 👇🏻

—Josep

Are you still here? 🧐

👉🏻 I want this newsletter to be useful, so please let me know your feedback!

Before you go, tap the 💚 button at the bottom of this email to show your support—it really helps and means a lot!

Any doubt? Let’s start a conversation! 👇🏻

Leave a comment

Want to get more of my content? 🙋🏻‍♂️

Reach me on:

• LinkedIn and X (Twitter) to get daily posts about Data Science.

• My Medium Blog to learn more about Data Science, Machine Learning, and AI.

• Just email me at rfeers@gmail.com for any inquiries or to ask for help! 🤓

Data. And a design decision.

That’s it. Strip away the complexity and that’s what you’re left with, what information you’re showing, and how you’re choosing to show it.

Most people focus on the “what.” The best data communicators obsess over the “how.”

Today’s issue breaks down the foundation of Data Visualization: what a chart actually is, what types of data exist, and how visual encodings work.

Three cheatsheets. One complete picture.

As Alberto Cairo defines in his book The Functional Art:

Data visualization is a collection of methods that use visual representations to explore, make sense of, and communicate quantitative data.

Think of Data Visualization as our compass, guiding us through this numeric ocean, translating vast datasets into meaningful and understandable insights.

So, what constitutes an effective visualization?

What differentiates a visualization that illuminates from one that only adds to the confusion?

In our journey today, we delve into the core principles of Data Visualization.

This article aims to unravel the mysteries of this crucial skill, uncovering how it illuminates the path through the data-driven landscape we navigate daily.

So let’s try to understand together the theory behind Data Visualization 👇🏻

The Basics Behind Data Visualization

In the Data Science field, one of the most vital yet challenging skills is the art of storytelling through data. And this is precisely the field of Data Visualization. A glance at the dictionary definition gives us a clear starting point:

“The act of representing information as a picture, diagram or chart, or a picture that represents information in this way”

At its core, Data Visualization is about shaping a narrative from a dataset.

It transforms data into a form that is not only digestible and visually appealing but also delivers a powerful impact.

While it may not have the same reputation (or appeal) as other fields like Machine Learning, Data Visualization is a fundamental aspect of a data scientist’s toolkit.

In our modern, data-centric world, think of Data Visualization as a pair of glasses that brings clarity to a blurry scene.

So… a natural question that arises is, why do we need Data Visualization?

A Brief Overview of the Data Visualization Field

During the XX century, data was predominantly displayed in stark tables, with little consideration for ease of interpretation.

Not long ago, the types of charts and graphs we instantly recognize today would have been foreign to people.

Modern concepts like Pie Charts, Bar Charts, and trend lines are quite modern developments. William Playfair is heralded as one of the pioneers of modern Data Visualization, credited with creating many of the graphical formats we use today.

And the big question is… why did he do so?

When trying to identify Scotland’s most significant economic ties, Playfair realised that crafting a visualization with bars representing the number of importations and exportations for each partner smoothed the process.

And this is how the Bar Chart was born.

Bar Chart — 1781

Bar Chart Playfair’s bar chart depicted Scotland’s trade balance with various European territories and the New World.

A glance at this chart revealed Scotland’s major trading partners and their respective trade volumes and balances.

Line Chart — 1786

For England, with more data at his disposal, Playfair was able to produce time-series graphs.

At a glance, it highlights that until 1755, England had an unfavorable trade balance, which shifted to a favorable one from that year onwards.

Pie Chart — 1801

Fifteen years later, in 1801, Playfair designed the first known pie chart. This chart illustrates the proportions of England’s total trade volume with various regions of the world.

So basically, Data Visualization was born as a way to smooth the process of interpreting data.

It serves as a vital bridge, particularly for those who aren’t fluent in the complex languages of numbers and algorithms, allowing them to navigate and comprehend intricate data stories with ease.

Data Visualization Basics

One of the foundational concepts of the field of Data Visualization is that every chart or graphical representation we encounter is fundamentally constructed from two primary elements:

• Data

• Visual elements

It is quite difficult to attribute this specific statement to a single individual, as this is a basic idea held and expressed by many experts in the field.

However, one notable figure in Data Visualization who has emphasized the importance of combining data and visual elements effectively is Edward Tufte.

He has extensively written regarding the principles of good graphical representation, highlighting the interplay between data and visuals in creating meaningful and clear presentations of information.

1. The key element: Data

When contemplating Data Visualization, it’s common to first think of numerical data. However, this represents just a fraction of the wide variety of data types at our disposal. For effective visualization, it’s crucial to recognize and utilize the full spectrum of data types available.

This spectrum includes both continuous and discrete numerical values. According to Clause O. Wilke, beyond these, data can also manifest as discrete categories, temporal elements (like dates and times), and even textual information.

Each of these data types brings its own nuances and requires different visualization approaches. To categorize these data types further, we can consider:

• Numerical data as Quantitative, offering measurable and countable characteristics.

• Categorical data as Qualitative, which includes dates, times, and text. It emphasizes non-numeric aspects and qualities.

Recognizing these categories is fundamental in data visualization as it guides us in choosing the most appropriate and effective methods to represent our data.

2. The Art of Encoding Data — Visual Encodings🎨

At the heart of Data Visualization lies the skill of visual encoding, where abstract numerical data is transformed into easily understandable graphical forms. This is akin to speaking a universal visual language, where everyone, regardless of their background in data analysis, can interpret the information at a glance.

Interestingly, diverse visualization types, such as scatterplots, pie charts, and heatmaps, share a common visual dialect.

This shared language is all about converting data values into visual elements — think of it as turning cold numbers into vivid, colorful representations on your screen or paper.

Yet, here’s a fascinating aspect: There’s an almost endless array of methods to visually represent data!

To simplify these methods, and according to T.Munzner, there is a primary encoding type called Retinal Encoding.

• Retinal Encodings: This category includes visual characteristics like shape, size, color, and intensity. These are the attributes that immediately grab our attention, acting as the visual ‘hook’ of the data story. Each element’s distinct appearance plays a crucial role in how we perceive and interpret the data.

On the other hand, and commonly associated with our brain’s spatial skills, there is a second group that some people call “spatial encodings”.

• Spatial Encodings: These leverage the spatial awareness capabilities of our brain’s cortex to represent information. This encoding approach can be realized by utilizing a position on a scale, establishing a specific sequence, or employing relative sizes for representation.

Incorporating various encodings into a single chart is technically feasible, yet it risks overwhelming the viewer. Simplicity in data visualization is key, too many encodings can muddle the message.

Ideally, limiting to one or two retinal encodings per chart enhances clarity and comprehension.

Remember, in Data Visualization, embracing minimalism often yields the most impactful results.

It’s akin to seasoning a meal — a judicious sprinkle of salt and pepper can elevate the dish, whereas an excessive amount can spoil it.

The choice of encoding should align with the narrative you aim to convey with your data. Therefore, a more pertinent question to ponder is not just which encoding to use, but how it serves the story your data tells.

Choosing the Right Visualization

In the wide landscape of Data Visualization, it’s essential to select the most effective visual encodings that match the nature of your data.

Not all visualization methods suit every kind of data, so it’s crucial to pair the right type of visualization with the specific characteristics of the data you’re working with.

For instance, when dealing with continuous variables like weight and height, their best representation often comes from positioning them on a scatter plot. This approach effectively communicates variations and relationships within the data.

On the other hand, categorical variables, which include categories like gender or nationality, are more effectively represented through distinct visual elements such as color coding or shape. This distinction allows these variables to stand out clearly and be easily interpreted in the context of the visualization.

Underpinning the effectiveness of these choices are fundamental theories that explain why certain visual encodings resonate more intuitively with us.

Understanding these underlying principles can guide us in creating more impactful and comprehensible visualizations.

The Core of Effective Data Visualization

In Data Visualization, striking a harmonious balance between aesthetic appeal and practical functionality is essential. Sometimes, choosing certain visual encodings, even if they appear less efficient, is intentional to convey a specific message or evoke emotions.

In our contemporary world, where data streams are ever-expanding, the significance of crafting visual stories that resonate is paramount, particularly when our audience comprises individuals not well-versed in data.

Effective data visualization transcends mere number presentation, it involves weaving a compelling narrative with our data.

It’s about animating our data, interweaving stories that establish a link between abstract figures and tangible, real-world consequences and insights.

For those of us passionate about technology and data, Data Visualization is more than a skill — it’s an art form, a unique language, and a bridge connecting us to the wider world.

Now… what you have been waiting all along… Here goes our weekly cheatsheet(s)👇🏻

1. The Concept of Chart

2. Visual Encodings

3. The Data

Are you still here? 🧐

👉🏻 I want this newsletter to be useful, so please let me know your feedback!

Before you go, tap the 💚 button at the bottom of this email to show your support—it really helps and means a lot!

Any doubt? Let’s start a conversation! 👇🏻

Leave a comment

Want to get more of my content? 🙋🏻‍♂️

Reach me on:

• LinkedIn and X (Twitter) to get daily posts about Data Science.

• My Medium Blog to learn more about Data Science, Machine Learning, and AI.

• Just email me at rfeers@gmail.com for any inquiries or to ask for help! 🤓

That’s why I want to remind you all that SQL is still the number one data language and the easiest one to learn.

If you’re looking to break into this field, there’s not better advice than…

START

LEARNING

SQL

RIGHT

NOW!!

Still not sure? Let’s give some reasons why 👇🏻

Humankind has always been strongly shaped by its ability to store and share information. Studies indicate that a key distinction between humans and other animals lies in our ability to create, preserve, and inherit knowledge and culture across generations.

And guess what… all of this is just DATA!

Today we are amid a significant shift in how our world works: Data has become the fuel of the XXI century. All fields and sectors rely on it to make decisions.

One thing is certain: The need for data-related skills will only keep surging.

Organisations today gather raw data from both internal and external sources at an unprecedented rate. By analysing this data, they can use reporting applications, dashboards, and other tools to answer questions and gain valuable insights.

So the right question to be done is how to manage all this data?

And the answer is quite simple: SQL remains the best option to do so.

You can go check the latest StackOverflow Developer Survey 2025, where SQL stands as the 3rd most used programming language of all developers!

(The first one if we only consider those used in the data science field 🥇)

So let’s try to understand better SQL’s advantages 💥

SQL’s main advantages

#1. It can deal with big amounts of Data

SQL is designed to work with big data and can handle complex queries on large datasets much faster than other tools like spreadsheets or even some programming languages like Python.

Learning SQL helps you manage and analyze big data effectively.

In many organizations, the core of the data environment is typically a data warehouse, where SQL is the primary language for interaction.

#2. It can be easily integrated with other tools

SQL integrates seamlessly with other data science tools and programming languages, such as Python and R. Libraries like pandas (Python) and dplyr (R) allow you to run SQL queries directly within your code.

This interoperability makes it easier to combine SQL’s data handling capabilities with the advanced analytics, visualization and ML features of these languages.

#3. It is standardized

To query or manipulate data with SQL, you use statements with keywords such as SELECT and FROM. This SQL syntax is standardized by ANSI and ISO-certified, ensuring consistency across the hundreds of databases and data tools that support SQL today.

While some databases and tools may extend the syntax with specialized operators, commands, or functions, the fundamental principles of SQL remain consistent.

Once you master the basics of SQL, you can apply this knowledge universally across different platforms.

#4. It is easy to understand

Basic SQL syntax is highly readable, resembling natural language. It outlines how data should be retrieved or manipulated.

Consider the following example query:

SELECT 
    first_name, 
    last_name, 
    date_of_hire
FROM employees
WHERE date_of_hire > ‘2018-12-31’
ORDER BY date_of_hire, last_name

In this query, the SQL keywords SELECT, FROM, WHERE, and ORDER BY define the actions to be performed and any person can understand the main purpose of the query.

So after all of this information, you might be wondering…

How to (ACTUALLY) get started with SQL?

This roadmap will help you to get started, build a strong foundation, and progress through more advanced topics.

Let’s break down each of the previous steps!

1. Learning the Basics

Before diving deep, it’s essential to grasp what is SQL. Begin with:

• What is a Relational Database?

• Comparing SQL and NoSQL Databases

• Understanding SQL Syntax and Basic Commands

2. Exploring Data Types

Once you have a clear image of SQL, you should understand the core data types you will be working with 👇🏻

• Integers, Decimals, and Booleans

• Character Types: CHAR and VARCHAR

• Dates and Binary Data

3. Mastering Basic Keywords

The first real step is to familiarize yourself with the commands that form the backbone of SQL querying:

• SELECT, FROM, WHERE

• ORDER BY and LIMIT Clauses

You will see that SQL queries are usually quite easy to understand, as their syntax resembles natural language. The most common SQL query you will find is.

Already proficient with basic commands?

Now it’s turn to understand how to manage databases with the following two steps:

4. Data Definition

Learn how to structure and manage your databases with:

• CREATE, ALTER, DROP Statements

5. Data Manipulation

Get hands-on with inserting, updating, and deleting data:

• INSERT, UPDATE, DELETE Commands

Now you should keep getting better with SQL programming language 👇🏻

6. Understanding Aggregation

Aggregate functions are powerful tools for summarizing data:

• SUM, AVG, COUNT, MAX, MIN

This will be one of the most important steps when creating KPIs and specific metrics.

7. Enforcing Constraints

Ensure data integrity through constraints:

• PRIMARY KEY, FOREIGN KEY, UNIQUE

• NOT NULL and CHECK Constraints

Our data will usually be spread across many different tables, this is why being able to merge data from different sources is crucial in SQL.

8. Mastering JOINs

JOINs are critical for combining data from multiple tables:

• INNER, LEFT, RIGHT, and FULL OUTER JOINs

Your SQL skills here are already really good!! 🙌🏻 But learning must go on…

9. Leveraging Subqueries

As queries keep getting more complex, you need to create subqueries.

• Nested and Correlated Subqueries

• EXISTS and NOT EXISTS

Now you should understand advanced syntax like specific functions and new ways of merging data like UNION.

10. Advanced Syntax

Explore more complex SQL operations:

• UNION, INTERSECT, EXCEPT

• CASE, COALESCE, and Functions for NULL Handling

Once a result is good, you might want to create a view!

11. Working with Views

Views can simplify complex queries:

• Creating, Modifying, and Dropping Views

Now you should focus on crafting better queries by leveraging indexes and following best security practices 💥

12. Optimizing with Indexes

Indexes improve query performance:

• Unique, Clustered, and Non-Clustered Indexes

13. Data Integrity and Security

Now that you are here, I strongly recommend you get familiar with security best practices. Maintain the security and integrity of your database:

• Using GRANT and REVOKE

• Database Security Best Practices

The two final steps is learning how to craft better and more optimized SQL queries. You know I am a big fan of modular code with CTEs, so that’s something we will talk more about in coming issues! 😉

14. Performance Optimization

Techniques to enhance the efficiency of your queries:

• Query Analysis, Optimizing JOINS, Reducing Subqueries

15. Advanced SQL Techniques

For seasoned users, explore sophisticated SQL features:

• Windows Functions, Recursive Queries

• Pivot/Unpivot Operations, Common Table Expressions (CTE)

And this is all for today!

We will see in more detail most of the previous steps, this is just a summary to help you get started with SQL!

How to Get Started

1. Master the basics: SELECT → FROM → WHERE, then ORDER BY, GROUP BY/HAVING, and core JOINs (INNER, LEFT).

2. Set up your stack: Use SQLite/DuckDB locally or a warehouse (BigQuery/Snowflake), with a comfy editor (DB UI or Jupyter + DuckDB); add a simple BI view if helpful.

3. Ship tiny projects: KPI query pack (WAU/MAU, conversion, revenue by channel), a cohort/retention table, and a customer 360 (users + orders → first/last order, AOV, LTV).

A final note

If you’ve made it this far, you’ve already done the hardest part: deciding to start.
SQL rewards short, consistent reps.

Ship one tiny query this week, then another next week.
A KPI today, a JOIN tomorrow, a view next Friday.

Momentum compounds.

Are you in?

— Josep

Still with me? 🧐

As fellow data enthusiasts, I’m sure you’d be eager to help me shape some impactful KPIs and take this newsletter to the next level!

So here’s how you can help:

👉🏻 I want this newsletter to be truly valuable for you, so please share your feedback!

Before you go, tap the 💚 and the restack buttons at the bottom of this email to show your support—it really helps and means a lot!

P.S. Share this with the coworker who “doesn’t need SQL” and spends lunch VLOOKUP-ing.

Any doubt? Let’s start a conversation! 👇🏻

Too many tabs, not enough progress?

This guide cuts the noise and gives you a shippable path.

Only the pieces that actually move you forward.

Why this, why now

Python is the most versatile “one language, many careers” tool: analytics, ML, web, scripting, automation, LLM apps—you name it.
If you learn it now, you compound for the next decade.

Need more reasons? Then let’s try to give them!

Why Python can supercharge your career?

From data science and web development to automation and artificial intelligence, Python’s applications are vast and continue to expand. You can check the latest StackOverflow Developer Survey 2025, where Python stands as the 4th most-used programming language of all developers!

(And the second one if we only consider those used in the data science field 🔥)

So here are the 5 main reasons why you should start learning Python today…

#1. Beginner-Friendly

Python’s clear syntax resembles English, making it the perfect starting point for beginners.

The learning curve is gentle, allowing you to focus more on solving problems than wrestling with complex code structures.

#2. Extensive Libraries

Python’s ecosystem is rich with libraries. Tools like Pandas, NumPy, and Matplotlib simplify data analysis, while Django and Flask are great for web development.

This wide array of libraries reduces the need to write code from scratch, speeding up development.

#3. High Demand in the Job Market

Python developers enjoy competitive salaries, thanks to the language’s popularity across industries like AI, data science, software development, and automation. Its applications span across finance, tech, healthcare, and more.

#4. Artificial Intelligence

Python is dominant in AI and machine learning. With libraries such as TensorFlow and Keras, developers can implement sophisticated AI models for tasks like natural language processing, image recognition, and even self-driving car algorithms.

#5. Strong Community Support

One of Python’s greatest strengths is its active community. With numerous tutorials, forums, and resources, help is always available. This vibrant support system ensures you won’t be stuck for long when facing a challenge.

So after all of this information, you might be wondering…

How to (ACTUALLY) get started with Python?

This roadmap will help you to get started, build a strong foundation, and progress through more advanced topics.

Let’s break down the steps to mastering Python!

Navigate Your Way Through Python

Whether you’re a data enthusiast, a budding analyst, or an experienced developer looking to sharpen your data science coding skills, understanding Python is crucial.

So let’s start by…

1. Learning the Basics

Before diving into data-heavy tasks, you need to understand Python fundamentals:

• Basic Syntax and Variables

• Data Types: Strings, Integers, Floats, etc.

• Conditionals and Type Casting

• Error Handling and Functions (including built-in functions)

• Data Structures: Lists, Tuples, Sets, Dictionaries

• Loops: for and while loops

2. Data Structures

Once you have a solid foundation of the basics, a good grasp of data structures is crucial for efficient programming:

• Arrays and Linked Lists

• Heaps, Stacks, and Queues

• Hash Tables

• Trees, including Binary Search Trees

3. Algorithms

With data structures covered, it’s time to explore algorithms:

• Sorting Algorithms: Bubble, Merge, Quick Sort, etc.

• Searching Algorithms: Binary Search and Linear Search

• Recursion and its applications

Already proficient with Python’s algorithms?

Now it’s turn to further understand Python’s world with its modules and functions.

4. Modules

Python’s extensive library of modules can make your life easier:

• Built-in Modules: os, sys, math, etc.

• Creating Custom Modules for reusability

5. Advanced Topics

Dive deeper with advanced Python techniques:

• Lambdas: Anonymous functions

• Decorators: Enhancing functions

• Iterators and Generators

• Regular Expressions for pattern matching

If you have arrived here… congrats! You are already a mid-level user. Now it’s turn to spice up your coding abilities with OOP 🔥

6. Object-Oriented Programming (OOP)

Python is an object-oriented language, so understanding OOP is essential:

• Classes and Objects

• Inheritance

• Dunder (Magic) Methods to customize class behavior

Now it’s turn to start understanding how to install (and why!) Python’s best libraries.

7. Package Managers

To install libraries and manage dependencies, you’ll need to know:

• PyPI and pip

• Conda for data science environments

• Poetry for project management

Already bored of essentials concepts? Then Python still has a lot more to offer ✨

8. Comprehensions

Python offers elegant ways to create data structures:

• List Comprehensions

• Generator Expressions

9. Learn a Framework

Choose a framework based on your goals:

• Web Development: Django, Flask, Pyramid

• Data Science/Visualization: Plotly Dash

• Asynchronous: FastAPI, Aiohttp, Tornado

10. Concurrency

For high-performance applications, learn concurrency:

• Global Interpreter Lock (GIL)

• Threading and Multiprocessing

• Asynchrony with async/await

11. Environments

Managing different Python environments is critical:

• Virtualenv and Pyenv

• Pipenv for dependency management

12. Static Typing

Add type hints to improve readability and catch errors early:

• Pydantic and Mypy

• Pyright and Pyre

13. Code Formatting

Maintain clean, readable code with formatting tools:

• Black, YAPF, and Ruff

14. Documentation

Document your code for clarity:

• Sphinx for generating docs

15. Common Packages

Familiarize yourself with essential packages:

• Typing and Tox

16. Testing

Testing is crucial for reliable code:

• Pytest and Unittest

• Doctest and Nose

17. DevOps

Finally, integrate Python into DevOps practices for deployment and scaling.

Phew, that was a lot to cover!

This roadmap is just a summary to help you start your Python journey.

We’ll dive deeper into each topic in upcoming issues, so stay tuned for more!

How to Get Started

1. Master the Basics: Begin with Python fundamentals—variables, control structures, functions, and data types.

2. Explore Libraries: Get hands-on with Pandas for data manipulation, Matplotlib for visualizations, and Flask for creating web applications.

3. Work on Real Projects: Apply your skills by building small projects like a task automation script or a simple web app.

A final note

If you’ve made it this far, you already did the hardest part: decide to start.
Python rewards short, consistent reps.

Ship something small this week, then another next week.

Momentum compounds.

Are you in?

— Josep

Still with me? 🧐

As fellow data enthusiasts, I’m sure you’d be eager to help me shape some impactful KPIs and take this newsletter to the next level!

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P.S. Share this with the coworker who isn’t confident in Python, yet.

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And how can you get started?

Today I’m bringing a simple introduction to Machine Learning, its types, and real-world examples — plus giving you a cheatsheet to keep things crystal clear. 🙌🏻

So let’s get started with the full-resolution cheatsheet 👇🏻

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Understanding how Python handles data internally can make you a better coder: it helps you write cleaner code, debug faster, and optimize performance.

Before diving in, here’s the full-resolution cheatsheet for you to explore 👇🏻

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If you've made it to this site, chances are you've already come across my visuals and cheatsheets.

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Every week, a new cheatsheet will be released, covering essential topics in:

✅ SQL – Queries, optimization, and best practices
✅ Data Visualization – Making charts that tell a story
✅ Machine Learning – Key models and techniques
✅ Python – Essential data science workflows
✅ AI & Deep Learning – Understanding cutting-edge architectures

Whether you’re sharpening your SQL skills, mastering data visualization, or diving deep into AI, you’ll find practical, easy-to-digest insights here.

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📜 Available Cheatsheets

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• Understanding the Essentials of Time Series

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• Unlocking the Power of Geospatial Data

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• What It Is, How It Works & Why It Matters

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• How Python Manages Data In Memory

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More cheatsheets are coming soon—stay tuned! 🎯

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In simple terms, geospatial data refers to information tied to specific geographic locations on Earth’s surface.

Think of:

• Maps showing streets and terrain

• Addresses linked to latitude and longitude

• Satellite images revealing land use or vegetation

With its ability to connect data to physical locations, geospatial data has become essential in industries like logistics, urban planning, and environmental science.

And this leads us to our main focus…

Understanding Geospatial Data

Before starting, here you have the full-resolution cheatsheet 👇🏻

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Outliers are the peculiar data points that don’t quite fit with the rest of the dataset. These anomalies can arise from:

• Data entry errors 🖋️

• Measurement issues 🛠️

• Genuine rare events 🌟

While they can skew your analysis and lead to incorrect conclusions, they can also hold valuable insights (think fraud detection or identifying unique customer segments).

This leads us to the main question…

So, what’s the best way to approach them?

So let’s dive into how to deal with outliers!

Before starting, here you have the full-resolution cheatsheet 👇🏻

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A time series is a special type of dataset:

• It’s an ordered sequence of values collected at consistent time intervals.

• Think of it as data with a built-in clock!

You’ll find time series data everywhere:

Stock prices, weather patterns, website traffic, and even your Spotify listening history.

Analyzing this kind of data helps us uncover trends, predict future behavior, and identify unexpected changes.
So let’s dive into Time Series!

Before starting, here you have the full-resolution cheatsheet 👇🏻

The Four Pillars of Time Series

1. Trend 📈

This is the long-term movement in the data. Does it rise, fall, or stay stable over time?

• Example: A company’s annual revenue increasing steadily.

• Pro Tip: Use tools like moving averages to isolate the trend from other components.

2. Seasonality 🌱

Seasonality captures recurring patterns that happen over a fixed period, like days, months, or years.

• Example: E-commerce sales spiking every Black Friday.

• Pro Tip: Seasonal decomposition (using libraries like statsmodels) helps separate seasonal patterns from raw data.

3. Cyclical 🌀

Unlike seasonality, cyclical patterns occur over longer and irregular intervals, often tied to economic or environmental factors.

• Example: Housing market cycles lasting several years.

• Pro Tip: Cyclical behavior is harder to predict, so combine it with trend analysis for better forecasts.

4. Irregular 🤷‍♂️

These are the random, unpredictable fluctuations that don’t follow any pattern.

• Example: A sudden sales spike after a celebrity endorsement.

• Pro Tip: Use smoothing techniques to filter out noise for clearer insights.

Why You Should Care About Time Series Analysis

Time series analysis isn’t just a tool—it’s a superpower for making data-driven decisions. From forecasting future sales to detecting anomalies in IoT devices, time series opens doors to a whole new level of analytics.

Here’s where it shines:

• Forecasting: Predict stock prices, weather, or customer demand.

• Anomaly Detection: Spot unusual activity, like fraud or system failures.

• Optimization: Understand patterns to schedule resources or improve workflows.

Ready to Dive In?

If you’re new to time series, don’t worry—I’ll be sharing a detailed beginner’s guide soon! In the meantime, here are a few tips to get you started:

1. Explore Your Data
   Visualize it! Tools like Matplotlib and Seaborn make it easy to spot trends and patterns.

2. Decompose Your Series
   Break it into trend, seasonality, and residuals using Python libraries like statsmodels.

3. Master Key Models

   • ARIMA/SARIMA for forecasting.

   • Prophet for handling missing data and seasonality.

   • LSTMs for complex patterns in deep learning.

And this is all for now! ✨

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