Data Science for Beginners: 2023 - 2024 Complete Roadmap
Published: 2023-10-04 03:07:29

So you want to become a data scientist in 2024. Here is a complete guide to avoid the common pitfalls that aspiring data scientists encounter ending up thwarting their ambitions. Before even thinking of what you need to learn or the resources you will require, the first question you need to address is why do you want to become a data scientist? Why? Establishing the reason(s) behind this question will lay a foundation on the path and the approaches you will need to take to learn data science. This will paint a clear picture of the goals and aspiration you want to achieve as you progress with your learning journey.

What are some of the reasons that could be driving you towards data Science? Is it to work in Big Tech companies? Is it to use data science to create solutions for governments? Is it to run a start-up or is it just mere curiosity to be abreast with the current technology and be at task when an opportunity arises. All this questions will help draw a footprint on the networks you need to build and the resources you need that aligns with your goals. For example, if you desire to work in government, then you will need know where and how to get government related data and use that in your learning journey.

Upon establishing your goals, create a learning plan. What do you wish to have achieved by end of week three for example. In a month or in two months’ time, what will have built? Create a simple time frame that you can iterate over what you learn to ensure maximum retention.  The biggest challenge with learning technology is that you’re bombarded with thousands of resources such that it becomes difficult to choose the perfect one for you. All these resources can act as a distraction and lead to information overload trap. Like drawing a plan, pick a resource and stick to it, while occasionally consulting third parties resources for clarification or advanced topics.

With all this resources, it’s easy to be caught in a tutorial trap. As you progress with the learning, it’s important to work on milestone projects. Projects are the best way to assess your knowledge retention and on whether you’re making progress or not. Projects will give you clear picture of whether your learning is aligned to your initial goals. As part of your projects, invite peers to review the projects, this will give you more insights on where you need to improve and any advancement you need to make both on the projects and on your learning.

Whereas learning technology is not a walk in the park, it’s easy and easier to seek help from our peers whenever we feel stuck. Not a bad thing at all, however what this does is over reliance on others to solve problems hence hindering our own growth. Before seeking help, research, debug the code, dig deeper into the learning resources to unearth new ways of doing things and most importantly don’t give up!

I intentionally skipped this at the very beginning on the technologies you will need to learn. For starters you are learning data. So you will need the language of data, which is SQL. On top of SQL, you will need Python and its data science libraries (Pandas, Numpy, and Matplotlib) just to mention a few before diving into machine learning tools. May be learn R or Tableau. All this can be overwhelming which is why you need to draw a plan first and stick to it.