A Data Analyst who understands segmentation will never be short of work.

Problem is, it's not covered often in courses.

Here's everything I know about customer segmentation condensed: Customer segmentation helps businesses understand their customers better.

It allows them to tailor marketing strategies, product offerings, and customer experiences to meet the specific needs of each segment.

A segment is a group of customers that share characteristics.

You can segment based on factors like age, gender, location, income, and interests.

Here are some examples of different types of segmentation:

Learn SQL in 10 steps (the simple way): 1. The basics are your best mates.

From SELECT to WHERE, get familiar with them.

They’ll make the advanced stuff much easier to grasp.

Don't go too deep into windows functions etc yet. Plenty of time for that later.

To do Data Analysis using Python you must master Pandas.

But this library contains a lot.

Here is what you need to focus on from day 1 👇 Pandas is an open-source Python library built on top of a Python core packages called NumPy (Numerical Python).

Pandas offers Data Analysts an easy way to work with data and provides many tools for extracting maximum value.

Let's get into it...

Nobody showed me how to create a data analysis portfolio.

I was lucky to land my first job.

If I were to start again, I'd create one using this 5-step plan: 👇 Step 1: Pick a subject that you are passionate about and enjoy.

You must have a genuine interest in the topic you are researching.

This will allow your mind to wander and be inquisitive, a key element in data analysis.

Pivot tables explained in simple terms: Pivot tables are a way to interactively group, filter, and interrogate large amounts of data.

You might be thinking:

"But Tom. I can do all this in Excel anyway?"

But here's why they're so powerful...

When I first started as a data analyst, I was dashboard crazy.

I quickly learnt the consequences of not validating my work properly during development.

Don't make the same mistake I did.

Instead, use this 4-step framework to bulletproof your work: 1. Verify data accuracy.

Ensure up-to-date data sources, accurate calculations, aggregations, and transformations in your visualisation tool.

Cross-check with original sources or validate samples to confirm accuracy.

This doesn't need to be complicated...

Do totals match up?

When you group by particular categories, does the segmentation work as expected?

Etc

When starting out, a lot of people struggle with SQL joins.

The truth is, once you've grasped it, you won't forget it.

Here's my super simple breakdown of common SQL joins: To start, let's quickly cover what a join is in SQL.

Data can be stored in RDMS, which means relational database management systems.

Being relational indicates data in tables can relate to each other.

How to write a killer data analyst resume with no experience: Use functional/combination format to focus on skills & achievements, not work history.

Highlight your skills and accomplishments without emphasising the lack of work experience.

Focus more on what you can bring to the table rather than what you've done in the past.

SQL is the undisputed champion of the data world.

To be successful in data, SQL skills are non-negotiable.

If you want to become a SQL champion, here's how to start: 👇 First things first, learn the basics of SQL.

Start by understanding the fundamental concepts of SQL, such as tables, queries, and basic syntax.

Familiarise yourself with commonly used SQL statements like SELECT, INSERT, UPDATE, and DELETE.

How I decide whether to use Excel or SQL for data analysis projects: As a data analyst, choosing the right tools for data analysis is crucial to ensuring efficient and effective project outcomes.

Here are some factors I consider...

RIP to old Data Analysis certificates.

Employers want certifications earned through exams.

Want to land a job quickly?

Here are the certifications that will help you do just that: Microsoft Certified: Power BI Data Analyst Associate

Instructor-led OR self-paced. ✅
One exam ✅
Based on end-to-end project ✅
Free renewal ✅

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In every interview I've had for data related roles, there has been at least one SQL question.

If you can't handle them well, it's an instant rejection.

Here are 3 case study-style SQL interview questions and the answers I would give: 1) Imagine you work for an e-commerce company, and they want to segment their customers based on their purchase history.

How would you group customers into: "High Value" (top 20% of spenders), "Medium Value" (next 30% of spenders), and "Low Value" (bottom 50% of spenders)?

How much Excel, SQL and PowerBI did I actually use as a data analyst? (Honest take): Excel by far was the tool I spent the most time using.

Why?

Because a lot of people know how to use it.

That makes it incredibly efficient for collaborating and sharing work with other departments.

And it's very user-friendly in terms of interface and navigation.

When I'm hiring a data analyst, here's what SQL I'd expect candidates with 1-2 years of experience to know: 1/ JOINS.

Why?

Because after a bit of time working with data, you'll be expected to look at the bigger picture.

Creating analysis that is well-rounded and captures different angles.

Selecting data from one table won't be enough.

Here's the fastest way to use Python if you already know SQL: If you've not encountered it before, start with Python (and programming) fundamentals. 

Get a grasp on variables, data types, and control structures. 

This will give you a great platform to kick off from.

Pivot tables explained in simple terms: Pivot tables are a way to interactively group, filter, and interrogate large amounts of data.

You might be thinking:

"But Tom. I can do all this in Excel anyway?"

But here's why they're so powerful...

5 dead simple tips for creating world-class PowerBI dashboards: 1. Design on paper first.

You might think I've lost my mind, but hear me out.

By drawing ahead of time, you can see right in front of you what the final output will look like.

You won't waste hours developing it for it to fail.

Draw first, refine or critique, then build.

10 pandas (python) functions I used the most as a Data Analyst: 1/ read_csv(): Reads data from a CSV file into a DataFrame.

2/ head(): Displays the first few rows of a DataFrame.

3/ info(): Provides metadata about the DataFrame.

99% of the problems we face as data analysts are not new.

Here are the 6 most common issues and how to overcome them:

(THREAD) 1: Getting to grips with messy data

Data rarely comes in a neat and tidy package.

It is therefore crucial to be able to clean and transform data well.

Tools I've used that worked well are Pandas (python), SQL queries, and Power Query (Excel).

The standard Data Analyst roadmap is:

Excel
SQL
Visualisation Tool
Optional: Python/R

That's not very helpful.

Here's a detailed breakdown of what you need to learn for each: Excel 1/4 - Data Manipulation and Cleaning:

Learn how to handle common data issues such as missing values, duplicates, and inconsistencies.

Focus on filtering, sorting, and conditional formatting.

Also, learn about text functions, such as CONCATENATE, LEFT, RIGHT, and MID.

6 analysis techniques I used the most as a data analyst: 1: Regression Analysis:

Understanding relationships.

Regression helps find connections between variables. 

For example, it can be used to analyse how sales change when advertising spending goes up or down, ultimately aiding in optimising marketing budgets.