2020 has been a hard time for everyone in many ways. This pandemic also has taught us the need of digitization but in a harsh way. Many industries have adapted the technology to keep their employees safe and healthy. That in turn has created a huge demand for people who can help the world get digitally equipped. So, we need developers/ programmers, UX / UI designers, Data Scientists more than ever before.
As we are all aware, Data science is one of the fastest growing fields in the world and its applications are huge. Think of any industry, we are generating data every second in various formats and pace. Analyzing these huge data to get meaningful information is one of the fundamental hurdles we all are facing at the moment. Same time, the nature of data we are generating is getting more and more complex. So, we don’t need only people with the skills but also the ones who have the right mindset to adapt to this ever changing data sphere.
We would first try to understand what are the core things you need to learn to be a Data Scientist.
For example, I am always a foodie and I wanted to know what are the best restaurants near me. Therefore, I looked into Zomato and saw what kind of data they are collecting from restaurants, how many of these restaurants are serving my favourite tandoori chicken and not only that, how many of them are having good reviews on this (Berlin has many nice Indian restaurants compared to other German cities). You can go as deep as you want. Then, you can research a bit on that domain. Someone might be interested in soccer and he / she would like to educate himself / herself about the game before starting to work on the data.
But, again, you need to decide what kind of data you would like to work on.
It is always very tough to choose the best tool as there is nothing called the best. However, you can start with something and check whether you like it or not. For example, many of my students started data analysis with R, some of them eventually moved to Python and wondered why they did not start with Python in the first place. I experienced some reverse stories as well. I would therefore ask you to check syntaxes of both R and Python and decide which one you would like to start with. Also, it would be nice to research on LinkedIn what are the majority of employers at your place looking for - R or Python.
Once you have decided the programming tool, you stick to it. Do not change even if you feel like switching back and forth- remember, no one would give you a job just because you have written Hello World in 50 different programming languages.
Next, we will talk about Tools and Technologies in detail. Also, we will provide you with a roadmap to learn these tools ( how much time a student spend in general ) so that you are aware of the timeline and prepare accordingly. Stay tuned.