Introduction to Data Science, How to start?
We will keep posting Guesstimates, Case Studies, Puzzles, etc. But it’s high time we should start with Data Science.
On an overall
1. Statistics – This job has a lot to do with Maths, so we will put it as the top priority
2. SQL – You need to fetch data and you need to be really good in it
3. R/Python – I will keep Python above all, but if you are good at R, then it should do the job
4. Visualization tool – Tableau/PowerBI. Both are almost the same, but Tableau is preferred.
5. MS Excel and
Approx. amount of time you should dedicate to cover the above topics:
1. Statistics – Are you kidding me ??
2. SQL – ~40 hours
3. R/Python – 80 hours
4. Visualization – 40 hours
5. MS Excel and
How to start ??
I don’t really want to promote the website, you can choose any good resource from the below list:-
1. Statistics – Don’t go for
2. SQL – Practice on www.sqlzoo.net and do try all the questions posted on the website. Try the following:-
a. 112 Questions to crack Data Science Interview using SQL
b. Write better SQL Queries + SQL interview Questions
There are many other books on Kindle, do practice 🙂
3. R/Python – Any good source should do. You can enroll in some online courses on Coursera, Datacamp, etc. But do practice a lot. We have a book on Python and R, see if you like it:-
a. 100 Python Questions to crack Data Science/Analyst Interview
b. 100 Questions to Learn R in 6 Hours
We will keep publishing books on these topics.
4. Visualization Tools – We strongly recommend any online or paid course on Tableau or PowerBI
5. MS Excel and PowerPoint – Ask your seniors or team leads for resources. The internet is full of cheat sheets which are actually helpful
To start with, start devoting 1-2 hours per day, follow this website or any website of your choice. We want to create a resourceful blog which can actually help people like you.
Try Hackathons on AnalyticsVidhya and Kaggle to chisel your skill. We strongly and very strongly believe that Hackathons can actually uplift your level of understanding and knowledge about different algorithms.