A Data Scientist is a ‘jack-of-all-trades’ basically 3 key skills, that a Data Scientist needs to possess are Mathematics, Statistics, and Programming. To become a Data Scientist it demands a disciplined learning and strong dedication from you. I will draw a road map and how to go about it, but before that, it is of utmost importance to know a few things about data science.
Strengthen your basics - I have been interacting with a few aspirants lately, and most of them lay a lot of importance to mathematics. Data science forms its base on not just mathematics and Statistics but on Programming as well. People usually overlook the programming part which is a big blunder. The Deeper The Roots, The Higher The Branches is what I firmly believe in. Strengthening the fundamentals would initially grow your technical and understanding towards to subject that would help to deal with complex problems and data.
Programming
The popular tools among Data Scientist are R, Python, SAS, MATLAB, SPSS, My SQL, and Java. Now here, there is a lot of debate on Python vs R. Both languages have their own pros and cons. R was built by Statisticians, originally. Python was built by programmers. The Machine Learning capabilities of both have been massively enhanced over the past few years. Personally, I would recommend Python as it is a general multi-purpose language and has a lot of visualization libraries like Bokeh, Seaborn, and Pygal. Python’s usage has been increasing due to its multi-functionality and integration abilities.
Intermediate Python for Data Science
Importing Data in Python
Pandas Foundation
Python Data Science Toolbox
Databases in Python
Manipulating Data Frames with pandas
Data Visualization with Python
Interactive Data Visualization with Bokeh
Network Analysis in Python
Merging Data Frames with pandas
And hence it is of utmost importance of firm your roots. These later go on to be your weapons while solving any problem in Data Science, especially programming. The more comfortable you are the better you would be able to understand the problem and device an optimal solution. Once you have built a strong foundation you can easily start getting deeper into data science.
How to grow deeper into Data Science - The main objective of data science is to help solve complex business problems with the help of data. To be precise, if you don't work on real industry problems, you won’t be able to groom yourself as a successful data scientist.
I would always recommend data science aspirants to go for a program which is classroom-based, instructor-led and heavily practice driven. You will come across a lot of institutions, courses, programs that will help you learn data science. Some are way expensive while some are reasonable. But, one very important thing to keep in mind while you are enrolling for such courses is to know the worth of it in terms of knowledge and what at the end of the course you are developing within yourself. Make sure the emphasis of your course is more on practice and not just theory. The projects your work on is based on industry-relevant problem statements and not the age-old assignments. Learnings and Projects are collaborative and performed in teams. You get real industry tools and focus is on test-driven development
Attend more meetups - Although the prime requirement to become a data scientist will be through practice and practice only. It is always good to have a social network. You must start attending more and more meetups regarding data science. You get to learn a lot about data science.
Start with Internships - You can also try to work along with learning. You can opt for internships in some of the Data Science companies. You’ll get the industry experience with the academic knowledge which you’ll cover studying these topics. Most of the big technology companies use Data Science. All these companies should have Data Science internships positions. I would recommend you to go to Internshala, Linkedin, Glassdoor, Monster and search for Data Science Internships.
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