Learn How to Become a Self-Taught Data Analyst

In this blog, I share steps I have taken to learn data analysis, including the books and resources that helped me along the way. Find out why it’s good to know Microsoft Excel, what the benefits of SQL are, and where you can discover more about machine learning.

Table of Contents

Step 1: Learn How to Analyse Data with Microsoft Excel

I had just graduated from university, and I quickly learned that I knew nothing. It was 2006, so London was in the middle of a financial boom. Like others, I sent my applications to several banks. Most of the replies indicated that I lacked qualifications or better applicants were out there.

After a desperate search, I finally accepted a job offer from a consulting firm. On my first day, one of my new team members asked me to help her with Excel work. It was a rainy day, and the prospect of filling out numerous spreadsheets for the foreseeable future depressed me.

But I persevered. I decided it was time to dust off my Excel knowledge, which I still remember from high school. I also figured that most tedious tasks, such as creating dashboards, could be easily automated or made efficient using creative formulas.

There are many online resources which can help you with your first steps, but if you are looking for one book, I highly recommend “Excel Hacks: 100 Industrial Strength Tips and Tools 1st Edition”: https://www.amazon.com/Excel-Hacks-Industrial-Strength-Tools-ebook/dp/B0043GXMPM

For a step-by-step tutorial, check out my blog Your First Steps in Microsoft Excel – Beginner’s Crash Tutorial. Also, to see an example of what analysing data looks like, go to How to Analyse Data in Microsoft Excel with Power Query and a Pivot Table.

Screenshot: An Excel spreadsheet with a Data Table

Step 2: Automate Data Analysis with VBA

VBA is an Excel programming language, and it’s instrumental if you need to deal with many repetitive tasks. Those can be copying and pasting, moving data from one place to another or merging spreadsheets with thousands of different rows of data into one workbook.

The easiest way to test writing a VBA is to record a macro for a simple task like highlighting a cell. You will notice a simple script if you right-click on a sheet’s tab and select ‘View Code’. That’s the VBA code.

The good news is that you don’t need to know too many commands to start writing scripts. If you have done some programming before, you will be familiar with if statements, loops, and variables. In that respect, VBA is just like any other programming language.

To learn specific commands on how to, for instance, copy and paste values, you can record a macro. You can then copy the code to re-use it for your script.

To get a good start, I highly recommend checking out https://www.excel-easy.com/vba.html. Not only is it an excellent free overview of VBA, but it is also an essential reference point for using spreadsheets. Also, it is a great learning resource, especially when starting a new job as a data analyst.

And to find examples of useful Excel macros and VBA code, visit https://zingurl.com/24-useful-excel-macro-examples-for-vba-beginners-ready-to-use/.

Screenshot: An example of an Excel VBA script

Step 3: Learn SQL to Extract and Aggregate Data

To become a good data analyst, learn how to deal with databases and extract data. There is no better way to prepare yourself for that task than by learning SQL. Yes, there is a chance that you will never have to use it. Your company will present you with an easy-to-use interface from which you can extract data, so why bother?

The truth is that most of those interfaces take your request and translate it into an SQL query anyway. Learning and mastering SQL will satisfy you immensely and simplify your data analyst life. Also, you will not need to rely on your other developer colleagues if you suddenly need to pull thousands of rows of data and your interface crashes.

There are many books and resources out there, but I highly recommend “Learning SQL” book by O’Reilly: https://www.oreilly.com/library/view/learning-sql-3rd/9781492057604/. The material is concise and easy to read, and you will soon be able to put together “SELECT” and other SQL queries in your sleep.

Screenshot: A simple SQL SELECT query

Step 4: Diversify Your Data Analysis Skills

Because my degree was in social science, I knew I needed more analytical qualifications. One of my first roles involved working for a start-up. I was reporting to the chief financial officer, so my tasks involved dealing with numbers and finances. I had to learn how to create financial models in Microsoft Excel or help my colleagues in the marketing department understand how user churn impacts overall profitability.

Going back to school was not an option, so I opted to take a Chartered Financial Analyst (CFA) qualification. Learning CFA is a great way to get up to speed with financial analysis, economics or statistics. In other words, it teaches you how to use data in a more commercial setting.

It is one of the most challenging degrees, but the good news is that you don’t need to attend any classes. All you need to do is sign up, study on your own and show up at one of the examination centres around the world. And although there are three levels in total, passing the first one will significantly enrich your knowledge.

For more info on CFA program, visit https://www.cfainstitute.org/

To learn more about financial modelling, check out my tutorial How to Build a Cash Flow Forecast in Microsoft Excel.

To discover how to analyse customer behaviour, go to How to Use Cohort Analysis to Calculate Retention and Churn Rate in Microsoft Excel.

Step 5: Learn to Visualise Data with Tableau

To become a good data analyst, you need to learn how to best articulate and present your data. While you can be good at handling data, your audience will need to grasp the numbers in an easy-to-follow way. The task can be challenging, but luckily, there are tools to help you. And they are not just Excel graphs and PowerPoint slides.

Tableau is a powerful application that allows you to extract data from different sources, such as spreadsheets or SQL databases. It can aggregate the data and create interactive dashboards. You can even write scripts and publish your dashboards so others can easily share them.

It may be a learning curve, but the benefit of presenting your data to others is enormous. Tableau has a great online community that most likely will have an answer to any issue you are facing.

For free training videos in Tableau, go to https://www.tableau.com/learn/training/20203.

An example of a dashboard in Tableau. Source: https://www.tableau.com/products/viewer

Step 6: Accelerate Data Analysis with Python

If you need to process vast quantities of data or script repetitive processes, Python is Data Analyst’s best friend. It opens new areas of possibilities, combining programming and analytical skills you have learned over time.

I learned Python later in my career and wish I had done it earlier. Consider merging Excel files. Yes, you can write a VBA code, but the process will likely take a long time. With Python, you only need to write a few lines of scripts.

An Example of a Python script to consolidate CSV data files.

And that is just the beginning. Different modules available in Python allow you to merge, process, transform and visualise data. Python is an open-source project, and it enjoys considerable support from the online community. Head to Stackoverflow if you have a question or an issue, and there is a high chance someone will have an answer for you.

There are a lot of resources out there, but the website I found most helpful was Dataquest. It is an online interactive course that takes you through a practical journey through Python. The best part is that its basic plan is completely free of charge.

If you are looking for a good book on Python, I recommend “Python for Data Analysis” by O’Reilly: https://www.oreilly.com/library/view/python-for-data/9781449323592/.

To learn more about Python, check out my tutorial How to Use Python and Pandas for Data Consolidation and Transformation.

Step 7: Keep Up with Machine Learning

Learning Python allows you to expand your data analytical horizons and learn more advanced topics. One of them is machine learning. It is a hot topic, and it’s probably here to stay. It is good to have at least some familiarity with machine learning. In addition, it can be very fascinating!

The good news is that if you are a data analyst and have some understanding of statistics, it is not as difficult as it sounds.

Visit Kaggle.com to get involved in machine learning challenges and increase your practical knowledge. It is an excellent destination for developing machine learning and data analytical skills. The website is also a great online source of data and Python scripts.

Step 8: Have Fun and Make Yourself Useful

To become a good data analyst, always try to stay involved and relevant. If you look around, you will always find someone who needs assistance with spreadsheets or data.

Helping others with their spreadsheets or data questions is a chance to see how your analysis contributes to other projects. It is also an excellent way to get a different perspective and context when requests and tasks come your way.

Summary: How to Become a Self-Taught Data Analyst

Working as a data analyst can be extremely rewarding, and the good news is that you can learn it independently. Make sure that you stay up to date with the recent developments. Learn as much as possible about Microsoft Excel, SQL, machine learning, etc. As data analysis continually evolves, solid technical knowledge will keep you on top of your game.

Remember that being a good data analyst is not just about writing an SQL script or creating a spreadsheet. You need to learn to use data commercially, so get involved in various projects. Taking on different roles will help you learn how to utilise data in finance, marketing or sales.

The field of data analytics comes with countless opportunities. Enjoy the process of learning because working with data and spreadsheets can be fun!

Get in Touch

Hi, my name is Jacek and I love spreadsheets! I hope you’ve enjoyed reading this tutorial as much as I did writing it! If you have any questions about data analysis, don’t hesitate to get in touch.