Mapping The Journey

When first starting to learn Data Science, it can be confusing to know where to start. There are unlimited sources to learn from on the internet, most of them seemingly good. The issue is information overload. How do we separate the wheat from the chaff? The thesis of this article is that there are 4 basic skills you should learn on your journey and it may be surprise you what they are. The end goal is to get an entry level job and scale up your career from that point on.

You are not going to train towards being a data scientist

Yup, that’s right. I can write click bait headlines. Also, I am serious. The first step to becoming a data scientist, especially if you are not studying up for a degree, is to become a data analyst. The barrier to entry is much lower and will get your foot in the door to becoming a data scientist.

Essential Skills For Data Analysts

SQL
Power-BI
Tableau
Microsoft Excel

What is SQL?

Which type of SQL to learn:

Learn the server side management SQL.

Where to learn SQL?

The internet is filled with resources but that is almost a negative because it is difficult to filter through all of them! Below is a list of the resources I recommend:

1. The SQL Series by Alex the Analyst on YouTube

The 4 part SQL series by Alex the Analyst is a great place to start because Alex is a great presenter and a data analyst. His entire channel is great for developing a roadmap. Start here because he shows how to download and set up SQL on your computer. He does not get into great detail during this series but provides a wonderful SQL overview which is precisely what you need starting off.

2. Kaggle SQL

Kaggle.com is the next stop on our journey. Kaggle is a great site for data scientists and also offers both an Intro SQL mini course and advanced SQL course. I have taken several Kaggle courses on preprocessing data and machine learning. I love how they go end to end on projects and force you to code to get through the course. It is an active learning experience and also introduces you to the number one data science website which you are going to need to get familiar with. Many datasets can be found on here and you can practice analyzing them. Projects for your portfolio you will show employers will be here.

3. Khan Academy:

Khan academy in this Intro To SQL goes over concepts but in a way that has structure and many practice problems. You are only going to learn data science/analysis skills by doing. Khan Academy incorporates several practice problems into their course on SQL and then tells you immediately when you are going wrong. This is great for beginners learning the ropes.

4. Practicing Projects Yourself:

How cheeky. You’ve heard it once, you’ve heard it a thousand times mores, and you are going to hear it again from me. YOU MUST STRUGGLE THROUGH A PROJECT. Taking courses is a must to get a foundation but after you’ve taken the courses above and likely even before you finish all of them; you should do a project. Projects force you to create rather than just take in the information. My suggestion is using the Kaggle SQL course project as a model or following a YouTube tutorial for your first project or two. You can try to apply the same technique to a different data set or recreate the project from scratch while checking to see if you rewrote the code accurately after every couple steps. Prepare to get discouraged because you will spend a fair amount of time debugging the simplest issues but this is where the real learning happens. Don’t mistake the very few lines of code you produce as failure, you aren’t going to master a highly profitable skill quickly, otherwise, everyone would do it. Don’t be a wimp, finish the project!

Data Visualization: Power-BI versus Tableau

Power-BI and Tableau share the same purpose

Everyone knows that looking at an excel spreadsheet with hundreds, often thousands, of rows and multiple columns can be daunting. This is especially true for people that are not comfortable working with numbers and get easily overwhelmed. Your job as a data scientist/analyst is to communicate the insights of you’ve found in your data into actionable steps for the less tech savvy personnel in your organization.

Power-BI and Tableau Democratizes Accessing Data

These tools allow you to organize dashboards, charts, and graphs in very useful ways that your coworkers will be able to interpret and access through their logins to the system. It is a drag and drop system. I have not yet used these tools in my journey as I am focusing on the more valuable skill of SQL; however, the purpose of these tools is communicating with visuals so non-data scientists can make informed decisions.

All Job postings require proficiency in one or both tools

Take a look at the job listings for data analysts on sites like Glassdoor and LinkedIn. Almost every single one will state you need to know these skills. This information is corroborated by other sources so give it a search for more details as I still need to study up myself.

Learning Resources and Certifications for Power-BI and Tableau

Certifications for Power-BI can be found on the Microsoft website because it is a Microsoft product. Microsoft is a big name so this certification will carry more weight. Tableau offers certification through their own website making this a solid certification to pick up. I have not dived into either certification class yet but I will be using the following 2 sources to learn prior to taking those certification exams:

The Microsoft Data Analyst Certification is a valuable certification for your resume in itself. It teacher Power-BI and has a few extra goodies such as preparing data to be analyzed.

Microsoft Excel: Sending/Receiving Data

No frills here. As described in the previous sections, not everyone is a data expert. You need a way to format data in an understandable way and many people know Excel. For example, you would not send your coworker a SQL script if they don’t know the program. You would translate the information into an understandable Excel document.

Resources To Learn Excel

You may have your own experience with Excel and have your own go to resource since it is so popular; however, these are the sources I am planning on using to brush up on my Microsoft Excel skills:

1. Data Analysis and Presentation Skills: A PwC Approach Specialization

This is the specialization or series of courses I recommend. Remember, often the point of using Excel is to communicate. These courses hit 3 blues with 1 brown (2 birds 1 stone joke). You will be learning Excel, specifically for Data Analysis/visualization, and it goes over how to present to the team effectively. Picking up a few presentation techniques along with training with Excel is a bonus. It contributes to the larger picture of effectively communicating data insights to your team which is essentially your whole job description.

Summarizing The 4 Basic Skills

Keep in mind we are training to become an entry level data analyst so we can get our foot in the door for a data scientist position. The most important skill to learn is SQL to manage databases and do much of the technical work.

Then you need to get familiar with a visualization platform such as Tableau or Power-BI. These 2 tools allow for better communication of insights to your colleagues, pick one to get good with.

Lastly, we have boring, old Microsoft Excel. Why is it boring? It is because everyone knows it. Excel will be the tool you will be using frequently to send and receive data from the non-tech savvy plebs at work! Do not email these people a SQL script and expect them to understand.

Notice how 3 out of the 4 tools here center around communication. I hate toting soft skills because they aren’t objectively measurable; however, the importance of understanding how much of your job is teaching insights is crucial. If you follow the sources in this post and combine it with a few of the projects I will be posting later on; then you should be well on your way to getting yourself an entry level data analyst position.