In the second part of this article, I will explain what resources I used to learn the basic skills to break into Data Analytics and what type of course (BootCamp, self-learning) path I recommend based on my experience. You can read the first part here.

Where to Learn?

You already know the requirements to become a Data Analyst. The next step is to figure out how to learn all those things without going crazy. There are three main paths you can choose:

• Master
• Bootcamp
• Self-study

I will cover the first two in this article, as those are the methods I decided to carry on.

Bootcamp

I decided to take a Bootcamp after one month of reflection. The main reason is that it was relatively short (3 months long) in a full-time modality. The summary seemed quite complete (Python, SQL, Stats & Math, Machine Learning), and there was an individual project at the end of the course. This last point was remarkable because I knew the importance of building a portfolio before sending resumes. Also, the price wasn’t so high compared to the other ones I saw (around 4.000 euros).

The Bootcamp I took was online as we were in lockdown in Spain. I think this is one of the reasons I didn’t enjoy it that much. I spent eight hours a day in front of the computer with tons of new concepts and doing group tasks with people who didn’t want even to share their screens.

So, my first advice is to choose a Bootcamp with an in-person modality if you have the opportunity. The learning curve would be softer, and it helps to socialize with people with the same dream as you. And, of course, the group task will be easier when you work this way.

My second piece of advice is to choose the cheapest Bootcamp that covers the minimum topic you want to study. Don’t ask for big loans. There are some Bootcamps you don’t need to pay for until you get a job, so that can be a good option too.

One of the things I realized is the huge number of concepts you need to interiorize in a very brief period. I felt I didn’t have enough time after spending all the afternoons and weekends reviewing them. The rhythm was too fast for me, and I don’t consider myself a person who needs tons of hours to understand how a for loop works, for example. This will be even harder if you haven’t coded before.

For this reason, you will need to sacrifice part of your free time to study yourself, especially after finishing the Bootcamp. It is a key point if you have a full-time job or a family you need to take care of.

Talk to people who already did that specific Bootcamp you are interested in. You can do a quick search on LinkedIn and send a couple of In-Mails. This way you will be sure of what you can expect from that specific Bootcamp, and it will be easier to make the final decision. 

Self-study

Self-study is the best way to start in Data Analytics for me. However, I understand it is not for everybody. You need a lot of self-discipline and a clear roadmap of what tools learn. You are all alone in your way: no teachers, no mentors. Also, be aware you will need a portfolio with at least a couple of projects where you show your skills. 

I spent around four months using this method before I started Bootcamp. I didn’t want to start the Bootcamp without knowing some Python, basic Statistics, and SQL.

For me, it was complicated to be constant at the very beginning. I took several online courses from different sources, and I wasn’t able to finish them. I felt very lost about which tools I should study first, so I decided to learn all of them at the same time.

My first piece of advice is to choose one tool at a time. That will allow you to understand better how it works, you will optimize your time, and will remain more focused. Don’t try to do everything.

It affected my consistency. My personal goal was to study for at least 8 hours on weekdays. However, I could only dedicate two or three hours per day. Every day I felt more demotivated and insecure about my decision. I quit my job to spend that time studying, and I was wasting it.

Of course, this goal was a bit over expectations. It’s not easy to spend 8 hours every day on a thing you are new to and be focused. Don’t try to study for eight or more consecutive hours without resting.

Creating a roadmap is key to saving time and will prevent you from buying resources you won’t ever use. Choose the materials you need for each tool and stick to that. Don’t overload your head with courses, books, and webinars to review. Keep it short and sweet. If you want to know more about how to create a tailored study plan, check out this other article.

Best Resources for Self-Study

Let’s take a look at the best resources I used to learn Data Analytics:

SQL

• Serious SQL. Probably the best hands-on course to start with SQL. I was interested in it because it has some marketing databases to play with. I loved it because it had everything I needed to start querying like a boss. It covers basic to advanced topics. This is all you need to practice and pass your technical interview. I think the price is very affordable (around 50$ dollars). 
• DataLemur. I don’t like this type of platform because they are usually full of errors or the explanations of the problems have low quality. However, DataLemur is the exception. This platform has a bunch of SQL questions from real-life interviews that are awesome to practice.
• Google BigQuery Public Datasets. It’s a collection of public datasets in Google BigQuery. This is perfect to work with unclean and dirty data for the first time.

Excel

• Master Microsoft Excel (LinkedIn Learning). It is one of my favorite resources to learn Excel from zero to hero. This mastery course covers a lot of topics that I already knew, so I didn’t review all the materials. However, it’s great to know basic and some advanced Excel topics: pivot tables, functions (like VLOOKUP), data formatting, and charts.
• Simon Sez IT. This YouTube channel specializes in Microsoft Excel and other programs from the Office family. You can find several free crash courses to master the tool on his YouTube channel.

Tableau

• Tableau Hans-On Training for Data Science (Udemy). In previous companies, I used to work with Looker or Power BI, so I found Tableau a bit difficult at the beginning because it works slightly differently in some aspects. This course has helped me a lot to understand how to use Tableau. Not only how to use the environment, but how to apply parameters and use correct functions to calculate custom metrics. 

Power BI

• Power BI Course by Maven Analytics. This course covers from the basic to the more advanced topics. There is another course that covers DAX, which is the Power BI language. It includes an end-to-end project, which I think it’s really important to put into practice all you learn.

Remember that you don’t need to learn both Power BI and Tableau. Just stick to one, depending on your local demand.

Python

I already said it is not mandatory to know Python to be a Data Analyst. However, it is a great tool to master if you want to differentiate yourself from the rest of the job seekers. 

• Crash Course on Python. This course was helpful to understand Python basics. It covers the basic syntax, loops, conditionals, and some OOP. It helped me a lot to prepare before I started the Bootcamp.
• Statistics Globe. This YouTube channel is focused on Statistics. However, it has a bunch of tutorials to start from zero with Pandas. And with R, of course. It is one of the resources in my day to day.
• Pandas For Data Analysis. I used this course to get in touch with Pandas after the Bootcamp. We dedicated only two weeks to Pandas, including practice, and I felt I needed to review some of the concepts. Here you have all the theory and practice you need.

The Projects

One of the most important things when doing a career switch into the Tech field – not only in Data – is creating a portfolio where you showcase your abilities. If you haven’t had any former relevant experience, this is the only way you have to show the world that you can do the job.

Don’t wait until the end to put things into practice. Watch a video from your course or book, and select a database to start working. It doesn’t matter if it seems to be something easy to understand. You need to replicate the code until you can do it intuitively.

Of course, you will always have the documentation on your side. However, when you constantly doubt if the WHERE clause goes or not after the FROM, you will need to practice the fundamentals.

Some people will tell you that you need to choose projects related to the field you want to work. However, this is not necessary. Maybe you don’t want to stick to just one field. Or you don’t have any idea yet. 

Also, I always recommend gathering your data. It will help you to showcase something new to recruiters, and you can improve other skills like web scrapping or connecting to an API. You could analyze your Spotify account stats, your Gmail account, or the steps you do over the day.

What’s Next?

In the third part of this series, I will explain how to build your portfolio to showcase your skills. Also, I will talk about how to choose the topic for your project and how to structure them.