Our Content Library Growth in the Past 3 Months

October 11, 2018

(This article was first published on DataCamp Community - r programming, and kindly contributed to R-bloggers)

The third quarter of the year just ended, and our data science content library has never grown faster. We now have a content library of +180 courses, +35 projects, and +20 practice modules. In addition, we greatly improved the feedback messages learners receive when making a mistake and general course quality went up to on average 4.6 stars out of 5.

In this post, we want to give you a more detailed overview of all what happened in the past 3 months at DataCamp related to the release of new content material, content quality improvements and getting to better feedback messages.

New Content Materials

At DataCamp we work according to the Learn-Practice-Apply model. You learn via courses, you practice via our practice modes, and you apply via our interactive projects. In all 3 areas, we have expanded our content library significantly in the past 3 months, having now over 1000 hours of learning material available to our learners.


In the third quarter of the year, our content teams broke all records in terms of new course launches: 42 new courses in total! Some exciting milestones we passed along the way:

These new courses have something for everyone, covering topics such as machine learning, data visualizations, reporting, and much more.

This quarter our internal curriculum team also made considerable progress on a more diverse instructor field (but we are not there yet). Today 43 of our courses are taught by women and/or non-binary, and within our biggest curriculum (R) 30% of instructors are women and/or non-binary. Increasing the diversity of our instructor base (in all dimensions) remains a work in progress, but we are committed to continuing our efforts here.

If you want to take advantage of all these new courses hurry to our course library.


This quarter we have been directing our efforts towards (i) making our practice environment mobile first, and (ii) on making practice a much bigger part of your learning experience. As a result, our practice library has grown with 10 new practice modules, all linked to our most popular courses on the platform.

Want to experience all these new practice modes? Download or open the DataCamp mobile app and start practicing.


Where Courses teach you new data science skills and Practice Mode helps you sharpen them, building Projects gives you hands-on experience solving real-world problems. In the past 3 months, our projects library has grown with 13 new R and Python projects, bringing the total project library to 35+. See how best to apply your skills in our projects library.

Content Quality

At DataCamp, we pride ourselves on having the best platform and the best curriculum for learning data science and therefore we put a lot of effort into ensuring that the quality of our content materials stay at the highest level. To do this, our newly formed Content Quality team works with the instructors to improve our existing courses.

In the past 3 months, our content quality team has reworked in total over 50 existing courses representing 100s of exercises. Some of the major improvements that were done are:

With the increase of the content library, we see content quality and course maintenance taking up a growing role over time in our content creation process.

Next Quarter

Our past 3 months were great. We never delivered more content to our learners. However, we obviously will not stop here. So what can you expect in the next quarter?

  • The release of our 200th course
  • The release of our 50th project
  • New practice modules for our most popular courses
  • More SQL & Spreadsheet courses
  • Better interactive feedback messages
  • And much more!

Stay tuned!

Interested in creating your own learning material? Apply here.
Interested in joining our team? Apply here.


New DataCamp Courses Launched Quarter 3 2018:

  1. Interactive Data Visualization with rbokeh
  2. Analyzing Police Activity with pandas
  3. Designing and Analyzing Clinical Trials in R
  4. Biomedical Image Analysis in Python
  5. Analyzing Survey Data in R
  6. Analyzing US Census Data in R
  7. Fundamentals of Bayesian Data Analysis in R
  8. Nonlinear Modeling in R with GAMs
  9. Financial Analytics in R
  10. Categorical Data in the Tidyverse
  11. Machine Learning in the Tidyverse
  12. Differential Expression Analysis in R with limma
  13. Convolutional Neural Networks for Image Processing
  14. Factor Analysis in R
  15. Bayesian Regression Modeling with rstanarm
  16. Analyzing Social Media Data in Python
  17. Visualization Best Practices in R
  18. Generalized Linear Models in R
  19. Building Dashboards with flexdashboard
  20. Analyzing Election and Polling Data in R
  21. Developing R Packages
  22. Building Recommendation Engines in PySpark
  23. Machine Learning for Time Series Data in Python
  24. Introduction to Bioconductor
  25. Marketing Analytics in R: Choice Modeling
  26. Preprocessing for Machine Learning in Python
  27. Working with Data in the Tidyverse
  28. Bayesian Modeling with RJAGS
  29. Machine Learning for Finance in Python
  30. Visualizing Big Data with Trelliscope
  31. A/B Testing in R
  32. Intro to Python for Finance
  33. Network Analysis in R: Case Studies
  34. Statistical Simulation in Python
  35. Predictive Analytics using Networked Data in R
  36. Dealing With Missing Data in R
  37. ChIP-seq Workflows in R
  38. Mixture Models in R
  39. Parallel Programming in R
  40. Single-Cell RNA-Seq Workflows in R
  41. Customer Analytics & A/B Testing in Python
  42. Financial Forecasting in Python

New DataCamp Projects Launched Quarter 3 2018:

  1. Functions for Food Price Forecasts
  2. Scout your Athletics Fantasy Team
  3. Naïve Bees: Predict Species from Images
  4. The GitHub History of the Scala Language
  5. Predict Taxi Fares with Random Forests
  6. A Visual History of Nobel Prize Winners
  7. Visualizing Inequalities in Life Expectancy
  8. Generating Keywords for Google AdWords
  9. Classify Song Genres from Audio Data
  10. Explore 538’s Halloween Candy Rankings
  11. Extract Features from Bee Images
  12. Rise and Fall of Programming Languages
  13. Who Is Drunk and When in Ames, Iowa?

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