Datacamp will hold your hand as you dip your toes in with your very first Kaggl competition.  This mini course will walk you through the steps and make sure you get to feel the success of crawling up the leaderboard on Kaggle in no time. 

Here is my review of the datacamp course Kaggle R Tutorial on Machine Learning, which is build on Trevor Stephens tutorial.

 Exploratory Data Analysis is the 4th course in the Coursera data science specialization

Quick Overview
Duration of the course 4 weeks
Work load 3-7 hours pr week, most time will be spend on the projects.
Videos with slides Approximately 5 hours in total.
Quizzes 2, in week 1 and week 2.
Other material No.
Course project 2 peer reviewed projects, in week 1 and week 3, see details below.
Formal prerequisites  This course has hard dependencies on R Programming and The Data Scientist’s Toolbox
Level of difficulty given only the formal prerequisites Easy to Medium. 

  

 Reproducible Research is the 5th course in the Coursera data science specialization

Quick Overview
Duration of the course 4 weeks
Work load 6-9 hours pr week, most time will be spend on the projects.
Videos with slides About 4 hours in total.
Quizzes 2, in week 1 and week 2.
Other material No.
Course project 2 peer reviewed projects, in week 2 and week 3, see details below.
Formal prerequisites  This course has hard dependencies on R Programming and The Data Scientist’s Toolbox

Additional, helpful 

prerequisites

If you have some experience with writing academic reports, in addition to the formal prerequisites, this course will not be hard. You also need to make plots in the projects, which is taught in the Exploratory Data Analysis course, so I strongly recommend taking that course first.
Level of difficulty given only the formal prerequisites Medium to Hard 
Level of difficulty given the formal and additional prerequisites Easy.

 

 Statistical Inference is the 6th course in the Coursera data science specialization

 

 
Quick Overview
Duration of the course 4 weeks
Work load 5-10 hours pr week, depending on your background and whether you want to do the swirl and homework exercises.
Videos with slides About 5 hours in total.
Quizzes 4, one per week.
Other material 14 swirl exercises (not mandatory). 4 homework exercises made in slidify (not mandatory), 4 homework videos, about 30 minutes each.
Course project In week 3, see details below.
Formal prerequisites  R programming, mathematical aptitude. 
Course dependencies: This course has hard dependencies on R Programming and The Data Scientist’s Toolbox. In addition, students will need basic (non calculus) mathematics skills.

Additional, helpful 

prerequisites

A little bit of experience with basic statistics and probability calculus will make this course a lot easier.
Level of difficulty given only the formal prerequisites You will need to work hard.
Level of difficulty given the formal and additional prerequisites Medium.