Basics
Is Data Science for you
Before starting out your data science career, there are some questions you’ll want to ask yourself so that you will know if a career in Data Science is actually what you need.
Ask yourself if you enjoy these things:
- Statistics and programming…might be your prior knowledge and experience, or one of your courses in school that you love.
- Working in a tech role that will require constant upskilling and learning of the latest tools and technologies.
- Working in different data roles other than Data Scientist.
If your answer to the above questions is Yes, then you’re on track to learn data science.
How to learn
If you have ever played a game before, you would understand that at the beginning your first task is to complete some sort of beginner level or stage 1 before you can play your way to a much more advanced stage. It is the same for Data Science, you have to hone your skills with the fundamental concepts.
To understand this, let’s take a look at the Pyramid of Data Science Needs, which originally stemmed from the Maslov hierarchy of needs, and we are going to use this to unlock how to learn data science, ultimately becoming a world-class data professional. This is because the Pyramid is really helpful to understand how to align your job responsibilities to your technical skills. This is what I am going to explain in this article to help you learn data science much better.
In the Pyramid of Data Science Needs, every component depends on each other, and you need a good understanding of the base components to be successful as you move up the Pyramid.
The Hierarchy of needs can also show the different careers in data science, and the processes involved with each specialization.
You must know that this is also determined by the company you work with, some companies value specialization where you have a data engineer who is responsible for a different task when you compared to the data scientist or when you compare with the machine learning engineer.
And some companies employ generalization in their data needs, where they only have like two talented individuals on the team, who own the whole project, and they wear different hats. The latter is the most common experience, especially if you find yourself working in startups.
The Pyramid of Data Science will be our guide to determining the best way to learn data science, and you’d be on your path to becoming a full-stack data professional.
If you are just starting out whether you are coming from a different background, you can commit a learning period between 6 months to 12 months to fully pick all the needed concepts. You can do this in less period, it depends on your learning strategy, schedule, and motivation.
Let’s talk about what to learn and where to learn them from.
What to Learn and Where to Learn them from
Level 1 Fundamentals: Python Programming, SQL, Version Control
The first skill that you should pick up is the Python programming language. This would form the basis of your programming skills, and because Python is the most used programming language in data science, I’d say that you are learning a valuable skill. Also, it can come in pretty handy when you want to do other stuff asides from Data Science because Python is a general-purpose language.
My advice here is to learn the language and not just some Python for Data Science crash course that wouldn’t teach the foundation of the Python programming language.
You are expected to know how concepts like data types, functions, control flow, data structures and algorithms, object-oriented programming, and how to work with external libraries.
Good Python programming skills will set you up really nicely for your Data Science career, and you can use the Python skills to pivot into building software perhaps if that’s what you are interested in later in your career.
The next skill to pick up is SQL which is Structured Query Language, basically a language that is used to communicate with databases, and you can pick up concepts like Querying data with SQL statements, filtering data, Joins, aggregations, joins, subqueries et cetera.
For this fundamental stage, the last skill to pick up is version control which essentially is learning how to work with Git and Github. Because as a data scientist, you might find yourself working with teams on a large project, and you have to work with other analysts and engineers, what that means is that you be able to save your changes, and download changes from others. And you do this by working with Git, and Github which is a repository. A repository is basically a central directory location used to store multiple versions of files.