These aren’t technically speaking “canonical” data scientist roles, but close enough and considered among the larger data science fields.

If you would still like to become a data scientist, then you should work on these skills:

• Basic Probability and Statistics: Nothing too fancy, just the rudimentary stuff
• SQL: You are probably familiar with this (weird) language. You have probably used an ORM to interact with different databases. Learn a little bit more about it: window functions, CTEs, triggers, good SQL style guide, and so on
• Modeling: Again, nothing too fancy. Learn some good models to use and when to use them. Read the documentation and tutorials online when needed. This skill also requires domain knowledge about the things you are working at (ranges from health insurance to warehouse logistics)
• Data Visualization: Data analysis is not very valuable until you turn it into a graph: could be a map, a time series, a 3D pie chart (just kidding, don’t do that please), or anything else
• Reporting: Once you have some solid insights, you should make it available and organized into a compelling report. It could be a document or a dashboard (always prefer these)
• Communication: Finally, you have produced a report and/or a dashboard. You should be at ease when discussing it with your colleagues and superiors. This is a very hard skill to master but totally worth it in the long (and the short!) run

3. Should a UX Designer/Researcher Become a Data Scientist? – Chris R. Becker

I took this answer from an Analytics Vidhya article itself given how relevant it is. A lot of application developers want to transition into data science but are unsure if they are qualified enough for it.

This article by Ankita Ghoshal will put any doubts to rest! She explains, in thorough detail, how you can make this career transition successfully if you get started now!

The best way of penetrating into a new field is by first understanding the current technologies. The buzzwords back in 2016 were, as you might have guessed, ‘Data Science’ and ‘Machine Learning’.

I had vaguely heard about these terms through online articles. I started exploring career options in this field and found that Statistics was the base of Data Science. This meshed perfectly with my interests – Statistics has always fascinated me. There is nothing better than working in a field that you love!

A quick Google search on ‘Analytics Machine Learning Tutorials’ led me to India’s largest data science community, ‘Analytics Vidhya’. I went through their articles on educational institutions providing courses for careers in Data Science.

I spent most of my professional career in programming before switching to Data Science. As a kid, I studied statistics but those concepts were long forgotten (as I’m sure you’ll relate to!). Making this transition was indeed tough, but not impossible.

The key is to never stop learning. During this switch, I realized that you don’t need to unlearn your existing skills to pick up a new one. I used my programming skills as a bridge between IT and Data Science to structure my machine learning code more logically.

This transition also helped me understand that the presentation of project results varies significantly from industry to industry.

For example, in the IT industry, the output of a web development project is a web page that is completely understandable by the stakeholders. In the world of data science, the output is (usually) numbers. It is the role of a data science professional to reveal these numbers to the customers/stakeholders using an indicative story.

For those who are ready to start your transition into Data Science, I recommend reading the below suggestions carefully:

1. Ask yourself this – are you are really interested in data science and are you a good fit? Don’t just fall for the glamour and hype. There are plenty of resources available online, such as the various articles and blogs on Analytics Vidhya, to get a feel of what this field is all about
2. Statisticians and programming professionals will undoubtedly have a bit of an advantage. But here’s the good news – the transition is possible even for non-technical people. The primary thing that matters is your thought process and skill of questioning and analyzing the information at hand
3. If you are an experienced professional in any other sector, get ready to be treated as a relative fresher when you make the switch to data science. It might be quite difficult for many people to accept a transition where you have to forego some years of seniority. I understand that. But if you give your best, then this industry will bestow even more knowledge, greater successes and awesome salary hikes on you

There is no background required for you to become a data scientist in the long-run, it’s all about your interest and you asking whether you are interested to work with data and envisage yourself in a role where data and decision making are aligned.

I can suggest a high-level learning pathway, but individual learning needs with regards to time and effort might require appropriate tweaking in these steps.

Beginners ideally would need to start learning programming, in case they have no prior experience. You can follow this learning in three steps:

• Learn Programming (R or Python) and become proficient in that language
• Gain the knowledge in these subjects – Intermediate Statistics & Probability, College Algebra, Linear Algebra, Machine Learning algorithms and methods
• Work with independent projects. Try to implement your learning in a step by step fashion while solving the objectives of these projects

My suggestion is a high-level overview of what you can do to start, but you will find the best path once you begin learning by doing.

Note: You can also check out Analytics Vidhya’s comprehensive and free learning path to become a data scientist in 2020.