Applied Data Science

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Below are the top discussions from Reddit that mention this online Coursera specialization from IBM.

Offered by IBM. Get hands-on skills for a career in data science. Learn Python, analyze and visualize data. Apply your skills to data ... Enroll for free.

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Taught by
Joseph Santarcangelo
Ph.D., Data Scientist at IBM
and 18 more instructors

Offered by

This specialization includes these 3 courses.

Reddit Posts and Comments

0 posts • 24 mentions • top 4 shown below

r/csMajors • comment
1 points • my_password_is______

you want math courses, not programming courses

CS degrees have lots of math in their first two years
Calculus, Linear Algebra, Discrete Math ...

that being said, I don't know of any courses in math that would impress a selection committee

so I think maybe do an Introduction to Python course

these two are specializations

-- this one is a sequence of 4 courses
Applied Data Science Specialization

-- this one is a sequence of 5 courses
Applied Data Science with Python Specialization

each specialization is self paced so you can work as fast as you can

you should pick one specialization and do at least the first course
(the first course in each is an introduction to python)
continue and complete the specialization if you want to

or after completing the first course in the specialization do this project
COVID19 Data Analysis Using Python

I think what might impress them is this specialization
but it requires basic knowledge of python

so maybe rush through one of the Introduction to Python courses, do the covid project and start the math specialazation and do as much of it as you can

r/learnmachinelearning • comment
1 points • synthphreak

  • Step 1. Learn Python, culminating in this Coursera specialization. Last two courses are totally optional.

  • Step 2a: Brush up on calculus and linear algebra using this Coursera specialization. Last course is optional but recommended, as how well you can follow it will serve as a reasonable test of how rigorous your ML math understanding is.

  • Step 2b: Fill in any gaps left by that specialization on your own using the web. There is an infinity of free resources out there. Spare no efforts on this step as the math only gets more intense from here.

  • Step 3a. Get more acquainted with many formal ML details using this Coursera course.

  • Step 3b. Then go back and review the content in the third course of the specialization in Step 1. You should understand it much more deeply now.

  • Step 4. Get started with DL using this Coursera specialization.

By the time you finish Step 4, you should have a decent enough understanding of the “big picture” of ML, the types of problems in can tackle, how several popular learning algorithms work, and what else you will need to learn. Thus, at this point you’ll be prepared to go off on your own and specialize.

I’ll also point out that ML involves a good bit of stats too, and these steps don’t address that. I can’t recommend any good stats courses because I learned all my stats in school so haven’t had to do much self-directed study. So you’ll need to figure that out as well, probably best done just before or just after Step 2a.

Good luck!

r/WGU • comment
1 points • create_a_new-account

have you started it yet ?

if you haven't then try





see if its something you really enjoy

r/datascience • comment
1 points • YouNeedToGrow

Planning on doing these Coursera courses in this order:

I want to become proficient enough in Data Science to be able to have it as a "tool in my toolbox." What are your thoughts on my self-teaching course plan?