Link to Content:

Coursera

Created/Published/Taught by:

Coursera

Jeff Leek

Roger D. Peng

Brian Caffo

Johns Hopkins Bloomberg School of Public Health

Content Found Via:

Coursera

Free? Partially: Some Free Content, Some Paid

Cost Range:

$0.00 - $470.00

Tags: data analysis / data cleaning / data exploration / git/GitHub / inference / machine learning / programming / R / regression / statistics

Difficulty Rating:

From Coursera:

“Ask the right questions, manipulate data sets, and create visualizations to communicate results.

This Specialization covers the concepts and tools you’ll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.”

Courses:

- The Data Scientist’s Toolbox
- R Programming
- Getting and Cleaning Data
- Exploratory Data Analysis
- Reproducible Research
- Statistical Inference
- Regression Models
- Practical Machine Learning
- Developing Data Products
- Data Science Capstone

Recommended Prerequisites: Some programming experience (in any language) is recommended. We also suggest a working knowledge of mathematics up to algebra (neither calculus or linear algebra are required).

Go to Content: Coursera Data Science Specialization

## By Katherine October 9, 2015 - 1:50 pm

To say these courses have whetted my appetite for data science would be a severe under-statement. I started them in January 2015 and just finished the last one bar the capstone project which I hope to do whenever it is next offered. I had no knowledge of data science going in, and now it is without doubt the direction I want to move my career in (I am a software engineer.) I found Statistical Inference and Regression Models to be the meatiest courses, both with Brian Caffo as the instructor, whose presentation style won’t be to everyone’s liking but his weaknesses on that front I felt were more than made up for by the content. I can’t say the same about the Developing Data Products course, which he also presents, because in this case the content is really quite thin (learn to build Shiny apps, which one can do just as well by following the tutorial at http://shiny.rstudio.com/tutorial/) and so Caffo’s slow and at times confused presentation of the content can be quite maddening.

I really enjoyed Roger Peng’s courses focused on learning R: R Programming (a great intro to the R language) and Exploratory Data Analysis (learning how to plot data in R).

The Practical Machine Learning course I found to be a little rushed because it covers a lot but without going into enough depth on any of the algorithms. If you are happy with “there’s an R package for that” when it comes to building prediction models, and aren’t interested in the actual implementation of any of the algorithms used, then this certainly is a very practical guide to machine learning. But if you want to get a deeper understanding of how these algorithms work, then Andrew Ng’s Machine Learning course (also on coursera – https://www.coursera.org/learn/machine-learning), offers just that.

Early Beginner- new to data science / totally new to this topic / few prerequisitesEarly Beginner - was totally new to this topic / had few prerequisites

(5) More than I even wanted to!

Detailed Content Survey:Needed to gain or strengthen general data science knowledge

I have used content from this publisher/series before

Somewhat

All of it

Basic programming knowledge, basic math.

RStudio

R programming, statistical inference methods (t-tests etc), building prediction models, visualizing and exploring data, and much more...

The Swirl programming assignments, which I forgot to mention in my review, but they were an excellent tool for drumming in a real understanding of the content.

The Developing Data Products course was a bit of a waste of time.

Do the courses in order.

I started a bit backwards :) I first did the R programming course to get up to speed on R basics but then went straight to Statistical Inference and Practical Machine Learning which I did at the same time. This was a mistake as I had very little time to spend on them and they were quite demanding. I'd recommend starting at the beginning and doing them in order :)

Each course is 4 weeks long and I spent as little as 2 hours per week on some and as much as 6 hours/week on others.

people new to data science

Andrew Ng's Machine Learning course on Coursera (https://www.coursera.org/learn/machine-learning)

An Introduction to Statistical Learning: with Applications in R (http://www.amazon.com/Introduction-Statistical-Learning-Applications-Statistics/dp/1461471370)

## By rgap October 21, 2015 - 4:20 pm

I took the first and and tried to finish the exploratory data analysis one and I didn’t like it at all. I prefer python over R. It was not very useful for me. Maybe for R lovers 😛

Advanced Beginner- familiar with data science / new to this topic / some prerequisitesIntermediate - had relevant prerequisites / was familiar with this topic

(2) A Little

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