Udemy Discounts: All Courses $15 Until Thursday

ShortTermSitewide-15usd300x250Did you ever consider taking an R course? It might be a good complement to the exercises here on R-exercises. Udemy is a platform that currently offers the largest number of R courses (more than 40). Until this Thur (19 january) you can sign up for any course for just $15. This is a huge discount, given that the average Udemy R course is priced at ~$60, and prices vary from $20 to $200 (besides some free courses).

For a full overview of all Udemy R courses, go to the Udemy selection in R Course Finder, or go to Udemy directly to search through all their courses.

Courses with the steepest discount (regular price $200, now for $15):

If you’d sign up for these five courses now, you’d have lifetime access and save $900! Tens of thousands of students have enrolled in these courses already (Machine Learning A-Z has close to 25,000 students enrolled), and ratings are 4.5 / 5 or higher.

Disclaimer: If you sign up for a course using these links, R-exercises earns a commission. It does not impact what you pay for a course, and helps us to keep R-exercises free.

Did you take a Udemy R course already? Please tell us about your experiences, and add a comment below! What did you learn? How did it help you to advance your career or studies? Would you recommend it to others?




Bonus: Tidy the data up!

We just added this week’s set of bonus exercises! Bonus exercises are weekly exercises sets, available to subscribers to our weekly newsletter. Please sign up using the form on the right, and receive further details by email how to get access to the bonus exercises (and solutions, of course).

This week’s bonus exercises focus on the Tidyr package.

As a subscriber, you also have access to all previous bonus sets:

  • ROC curves
  • Working with tm package and wordclouds
  • Working with and visualizing a confidence interval
  • Evaluating a linear time series model
  • Peparing data for time series model
  • Rebuilding a Figure
  • Simplifying For loops



Bonus: ROC curves

We just added this week’s set of bonus exercises! Bonus exercises are weekly exercises sets, available to subscribers to our weekly newsletter. Please sign up using the form on the right, and receive further details by email how to get access to the bonus exercises (and solutions, of course).

This week’s bonus exercises focus on ROC (receiver operating characteristic) curves.

As a subscriber, you also have access to all previous bonus sets:

  • ROC curves
  • Working with tm package and wordclouds
  • Working with and visualizing a confidence interval
  • Evaluating a linear time series model
  • Peparing data for time series model
  • Rebuilding a Figure
  • Simplifying For loops



Bonus: Text mining and wordclouds

We just added this week’s set of bonus exercises! Bonus exercises are weekly exercises sets, available to subscribers to our weekly newsletter. Please sign up using the form on the right, and receive further details by email how to download the bonus exercises (and solutions, of course).

This week’s bonus exercises focus on the tm package and word clouds.

As a subscriber, you also have access to all previous bonus sets:

  • Working with and visualizing a confidence interval
  • Evaluating a linear time series model
  • Peparing data for time series model
  • Rebuilding a Figure
  • Simplifying For loops



Bonus: Working with and visualizing a confidence interval

Bonus iconWe just added this week’s set of bonus exercises! Bonus exercises are weekly exercises sets, available to subscribers to our weekly newsletter. Please sign up using the form on the right, and receive further details by email how to download the bonus exercises (and solutions, of course).

This week’s bonus exercises focus on confidence intervals (after running a regression model). Feel free to post any comments on these exercises (and solutions) below.




Bonus: Timeseries modelling (part 2)

Bonus iconWe just added this week’s set of bonus exercises! Bonus exercises are weekly exercises sets, available to subscribers to our weekly newsletter. Please sign up using the form on the right, and receive further details by email how to download the bonus exercises (and solutions, of course).

This and last week’s bonus exercises aim to practise time series modelling. In part 2, we’re going to evaluate a simple model with one dependent and two explanatory variables. Feel free to post any comments on these exercises (and solutions) below.




Bonus: Timeseries modelling (part 1)

Bonus iconWe just added this week’s set of bonus exercises! Bonus exercises are weekly exercises sets, available to subscribers to our weekly newsletter. Please sign up using the form on the right, and receive further details by email how to download the bonus exercises (and solutions, of course).

This and next week’s bonus exercises aim to practise time series modelling. In part 1, we’re going to prepare the data and run a simple model with one dependent and two explanatory variables. Feel free to post any comments on these exercises (and solutions) below.




Bonus: Rebuild a figure (step-by-step)

rebuild-a-figureWe just added this week’s set of bonus exercises! Bonus exercises are weekly exercises sets, available to subscribers to our weekly newsletter. Please sign up using the form on the right, and receive further details by email how to download the bonus exercises (and solutions, of course).

This week’s bonus exercises aim to practise building a figure step-by-step, using the ggplot package. In 8 exercises you will learn to build the figure shown, starting with a small dataset and adding more detail in each exercises. Feel free to post any comments on these exercises (and solutions) below.




Bonus: Avoiding for-loops exercises

Bonus icon. Internet button on white background.

We just added our first set of bonus exercises! Bonus exercises are weekly exercises sets, available to subscribers to our weekly newsletter. Please sign up using the form on the right, and receive further details by email how to download the bonus exercises (and solutions, of course).

This week’s bonus exercises aim to practise vectorization, i.e., replacing (slow) for() loops with fast vectorized code. Feel free to post any comments on these exercises (and solutions) below.