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Rajasekar Nonburaj
Rajasekar Nonburaj

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Career Roadmap - How to Transition to Datascience

This is a series of articles on career transitions, the first article is about the career transition to Datascience. I am going to list down how you can learn and do a career transition to either one of the tech (Datascience, Cloud, DevOps and Full-stack)

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There are a lot of course on the internet where they teach Datascience, if you do a search on google you can find out and below is the result and out of which some of them are paid and free courses, it's up to to the individual which one they want to choose, even nowadays we can see some of the universities are offering courses on Datascience.

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I have been in the IT industry for 16 plus years and most of my experience is in Business Intelligence, Datawarehousing, etc.. this was once a booming tech where we work on data analysis and creating some meaningful insights for clients.

As of now, you can see most of the people who once worked as a BI Developer, ETL Developer, Database Developer (Junior to senior level) had themself transition to various other Datascience roles and for some still, they find it difficult to take up the transition and this article is for those people, the below screenshot shows what you can choose if you are in the crossroads and don't know what to decide.

This article is about my personal experience in interaction with various people in the IT industry. The tech specified below is one of the top emerging job roles in 2020 according to LinkedIn, we can't be a master of all, so I choose some which will be useful for someone to do the transition quickly, the first article about how to start transitions to Datascience.

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Datascience Paid Course:-
The courses specified here are paid courses, these are my personal recommendation and udemy has many more courses which are also good, you can search for a specific topic and take it up if this is not good for you. Please read the reviews and ratings before taking up any courses.

Before you start getting into Datascience, you need to have a basic understanding of Statistics, the below udemy course “Statistics for Business Analytics” is by “Kirill Eremenko”, he is one of the top-rated instructors in udemy for Datascience, ML and Deep Learning courses, he has more than 10 Million students registered for his courses. This course has a rating of 4.4 (5400) and so far 32,089 students has registered for the course.

Once you have done the statistics course, you need to learn any one of the statistical / programming languages (R/Python) in order to develop ML Models. The debate of whether to learn python or R is a never-ending one, you need to start first instead of debating over the language. R programming is considerably earlier one for someone like me who is not from the programming side, so I would suggest starting with R Programming.

The below udemy course “R Programming A-Z™: R For Data Science With Real Exercises!” is also by “Kirill Eremenko”, this course has a rating of 4.6 (26644) and so far 119,956 students have registered for the course.

After you completed R programming, it’s time for getting our hands into Datascience and Machine learning, I have found this course “Data Science and Machine Learning Bootcamp with R” by “Jose Portilla” useful, he is also the “Head of Datascience” for “Pierian Data Inc”, and also one of the top-rated udemy instructors on Datascience, ML Courses. This course has a rating of 4.6 (9402) and so far 50,413 students have registered for the course.

If you programming background and like to build ML Models using python, then this course is for you. This is a newly launched course from Andrei Neagoie and Daniel Bourke, the course “Complete Machine Learning and Data Science: Zero to Mastery” is launched on Feb 2020 and has a rating of 4.6 (859) and so far 8,826 students have registered for the course. This is one of the top rated courses now in Datascience in Udemy.

Andrei is a self-taught programmer and also the instructor of the highest-rated Development course in Udemy.

Daniel is also a self-taught Machine Learning Engineer, he has previously worked as a Machine learning engineer at maxkelsen.

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