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:
- Crossing our 150th course (today our course library is at 181)
- Crossing our 100th R course
- Crossing our 50th Python course
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.
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:
- A big overhaul of the feedback messages provided towards beginner learners in our entry courses such as Intro to SQL, Intermediate Python, Intro to Git, and Intro to Shell. This led to a significant reduction in unhelpful feedback messages.
- A big refactor of the courses part of our 5 new skill track launches this quarter: Shiny Fundamentals, Big Data, Spatial Data, Text Mining, and Tidyverse.
- Increasing learner friendliness of some of our more challenging stat courses like Foundations of inference.
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.
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!
New DataCamp Courses Launched Quarter 3 2018:
- Interactive Data Visualization with rbokeh
- Analyzing Police Activity with pandas
- Designing and Analyzing Clinical Trials in R
- Biomedical Image Analysis in Python
- Analyzing Survey Data in R
- Analyzing US Census Data in R
- Fundamentals of Bayesian Data Analysis in R
- Nonlinear Modeling in R with GAMs
- Financial Analytics in R
- Categorical Data in the Tidyverse
- Machine Learning in the Tidyverse
- Differential Expression Analysis in R with limma
- Convolutional Neural Networks for Image Processing
- Factor Analysis in R
- Bayesian Regression Modeling with rstanarm
- Analyzing Social Media Data in Python
- Visualization Best Practices in R
- Generalized Linear Models in R
- Building Dashboards with flexdashboard
- Analyzing Election and Polling Data in R
- Developing R Packages
- Building Recommendation Engines in PySpark
- Machine Learning for Time Series Data in Python
- Introduction to Bioconductor
- Marketing Analytics in R: Choice Modeling
- Preprocessing for Machine Learning in Python
- Working with Data in the Tidyverse
- Bayesian Modeling with RJAGS
- Machine Learning for Finance in Python
- Visualizing Big Data with Trelliscope
- A/B Testing in R
- Intro to Python for Finance
- Network Analysis in R: Case Studies
- Statistical Simulation in Python
- Predictive Analytics using Networked Data in R
- Dealing With Missing Data in R
- ChIP-seq Workflows in R
- Mixture Models in R
- Parallel Programming in R
- Single-Cell RNA-Seq Workflows in R
- Customer Analytics & A/B Testing in Python
- Financial Forecasting in Python
New DataCamp Projects Launched Quarter 3 2018:
- Functions for Food Price Forecasts
- Scout your Athletics Fantasy Team
- Naïve Bees: Predict Species from Images
- The GitHub History of the Scala Language
- Predict Taxi Fares with Random Forests
- A Visual History of Nobel Prize Winners
- Visualizing Inequalities in Life Expectancy
- Generating Keywords for Google AdWords
- Classify Song Genres from Audio Data
- Explore 538’s Halloween Candy Rankings
- Extract Features from Bee Images
- Rise and Fall of Programming Languages
- Who Is Drunk and When in Ames, Iowa?