We are generating about 2.5 quintillion bytes a day and the demand for data scientists is increasing more than ever. It should, after all, be no surprise that many platforms are offering tracks to prepare you for a data science career! My favorite so far has been DataCamp. But are you using the platform the right way?

From high quality content to a user-friendly interface, the platform has everything you need to get started with mastering the basics of the field. Here are a few things you should keep in mind to maximize your learning and make the best out of the platform experience.

The “Career Tracks” are great! But don’t miss those hidden gems in the “Courses” section.

Don’t you just love those thoughtfully tailored career tracks that most data science platforms are curating for users? Whether you are aspiring to be a Python Data Scientist, a Data Engineer or an R Data Analyst, DataCamp curated carefully chosen progressive courses to build a “Track” (or what other platforms call a “Path”) for you. You start learning in a linear fashion and after about 26 courses, you complete the track you chose. You earn a certificate too!

But is that enough? Certainly not! And the platform is very well aware of the need for other courses to supplement those tracks. Say you chose (like many of us just starting do) a track as a Python Data Scientist. The curated track offers you everything from an introduction to the Python programming language, to data manipulation and cleaning, to a few machine learning courses, etc…

What you end up missing is some of the more advanced and targeted courses that are hidden all the way down in the “Courses” tab outside the curated tracks! Courses like the “Introduction to Deep Learning in Python”, “Introduction to Natural Language Processing in Python”, “Parallel Programming with Dask in Python”, “Visualizing Geospatial Data in Python”, “Image Processing with Keras in Python”, “Software Engineering for Data Scientists in Python” and a ton of other super informative and quite necessary courses to get a more of a well-rounded taste of the field.

Learning is great! But are you applying everything you learn on a daily basis?

There is a great danger (shared among all learning platforms) in passively sitting back and absorbing new information. Are you watching the video lectures? Are you trying your hands at the coding exercises after each video? Are applying the methods and techniques you learned elsewhere on datasets and side-projects you are working on? Are you trying to explain the concepts you just learned to a friend or a data-savvy colleague?

These are all questions you should be asking yourself on the regular if you want to make sure that your learning is happening actively and with intention. Afterall, it doesn’t matter how many pandas techniques and tricks you know! What matters is how many of them do you really have under your built ready to be used in the right place and context. And always remember that the best way to learn something is to go ahead and try explaining it to someone else.

#data #data-science #computer-science #programming #online-learning #data analysis