DataCamp Courses Summary

Last updated on:July 1, 2022

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This is a short summary of the courses I finished on DataCamp in 2020.

(Course content subject to change.)


Microsoft Visual Studio Dev Essentials used to offer two months of free access to DataCamp, but it has been stopped.

GitHub Student Developer Pack offers three months of free trial.

You can enjoy the trial subscription only once. Standard subscription costs 25 USD per month and is billed yearly.

There are PDF certificates for individual courses, skill tracks, and career tracks. Unlike Coursera, however, the list of course included in a track is not printed in the track certificate.

In most cases, Chinese subtitles are available. Sometimes the translation of professional terms are not accurate enough, though.

Skill Track: Data Literacy Fundamentals (Theoretical)

  1. Data Science for Everyone
  2. Machine Learning for Everyone
  3. Data Visualization for Everyone
  4. Data Engineering for Everyone
  5. Cloud Computing for Everyone

Note: No coding/programming involved.

Skill Track: Spreadsheet Fundamentals

  1. Data Analysis in Spreadsheets
  2. Intermediate Spreadsheets
  3. Pivot Tables in Spreadsheets
  4. Data Visualization in Spreadsheets

Skill Track: Intermediate Spreadsheets

  1. Introduction to Statistics in Spreadsheets
  2. Error and Uncertainty in Spreadsheets
  3. Marketing Analytics in Spreadsheets

Note: PDF materials for Spreadsheets courses are not available. Links to Google Spreadsheets are provided. Downloaded files are not compatible with Microsoft Excel.

Career Track: Data Analyst with R

No. Course Packages Notes
1 Introduction to R / No videos
2 Intermediate R /
3 Introduction to the Tidyverse dplyr/ggplot2
4 Data Manipulation with dplyr dplyr
5 Joining Data with dplyr dplyr Insufficient dataset information for exercises
6 Introduction to Data Visualization with ggplot2 ggplot2
7 Reporting with R Markdown knitr/dplyr/ggplot/CSS (*new)
8 Data Manipulation with data.table in R data.table
9 Joining Data with data.table in R data.table
10 Introduction to Importing Data in R utils/readr/data.table/
11 Intermediate Importing Data in R DBI/readr/utils/gdata/
12 Cleaning Data in R assertive/dplyr/stringr/forcats/
13 Exploratory Data Analysis in R dplyr/ggplot2
14 Case Study: Exploratory Data Analysis in R dplyr/ggplot2/broom/tidyr/purrr
15 Introduction to Statistics in R dplyr/ggplot2 (*new)
16 Categorical Data in the Tidyverse dplyr/tidyr/forcats/stringr/ggplot2
17 Introduction to SQL /
18 Introduction to Relational Databases in SQL /
19 Joining Data in SQL / Doesn’t keep results from previous steps


  • Instructions contain links to RDocumentation pages.

  • Comprehensive tasks are broken into smaller steps. In most cases, results will be kept for the following steps.

  • You might want to take Categorical Data in the Tidyverse; String Manipulation with stringr in R and Working with Dates and Times in R before taking Cleaning Data in R, and then enhance skills with Dealing with Missing Data and Importing & Cleaning Data in R: Case Studies.

  • You might want to get a glimpse of SQL before taking Intermediate Importing Data in R.

  • In the new version, Correlation and Regression in R and Communicating with Data in the Tidyverse have been removed from this track and replaced with two new and updated courses: Reporting with R Markdown and Introduction to Statistics in R.

R Courses

Course Packages Note
Introduction to Data in R dplyr/ggplot2
Introduction to Text Analysis in R dplyr/tidyr/tidytext/ggplot2/
String Manipulation with stringr in R stringr/rebus/stringi
Intermediate Regular Expressions in R stringr/glue/tidyr/stringdist/fuzzyjoin
Working with Data in the Tidyverse readr/dplyr/janitor/tidyr/ggplot2/
Briefly mentioned three packages in the last chapter
Working with Dates and Times in R lubridate/hms/dplyr/ggplot2
Working with Web Data in R pageviews/birdnik/httr/
Dealing With Missing Data in R naniar/dplyr/tidyr/ggplot2
Importing & Cleaning Data in R: Case Studies dplyr/tidyr/stringr/lubridate/
Modeling with Data in the Tidyverse dplyr/ggplot2/moderndive/
Correlation and Regression in R ggplot2/dplyr/broom
Communicating with Data in the Tidyverse dplyr/ggplot2/forcats/knitr/
Visualization Best Practices in R dplyr/ggplot2/ggbeeswarm/ggridges Copy-and-paste assignments
Data Visualization in R Base R/MASS/aplpack/corrplot/rpart Exercises do not provide instructions
Introduction to Writing Functions in R dplyr/ggplot/zeallot/broom/magrittr
Visualizing Geospatial Data in R ggplot2/ggmap
Writing Efficient R Code microbenchmark/benchmarkme/
Text Mining with Bag-of-Words in R
Sentiment Analysis in R

Career Track: Data Analyst with SQL Server

  1. Introduction to SQL Server
  2. Introduction to Relational Databases in SQL
  3. Intermediate SQL Server
  4. Time Series Analysis in SQL Server
  5. Functions for Manipulating Data in SQL Server
  6. Database Design
  7. Hierarchical and Recursive Queries in SQL Server (*removed)
  8. Transactions and Error Handling in SQL Server
  9. Writing Functions and Stored Procedures in SQL Server
  10. Building and Optimizing Triggers in SQL Server
  11. Improving Query Performance in SQL Server

Note: Hierarchical and Recursive Queries in SQL Server has been removed from this track’s new version.

Other Skill Tracks

  • Data Skills for Business (5 Courses)

  • Importing & Cleaning Data with R (4 Courses)

  • Data Manipulation with R (5 Courses)

  • Tidyverse Fundamentals with R (5 Courses)

  • Data Visualization with R (3 Courses)

  • Text Mining with R (2/4 Courses)

  • SQL Fundamentals (5 Courses)

  • SQL for Database Administrators (4 Courses)

  • SQL Server Fundamentals (5 Courses)

  • SQL Server Toolbox (4 Courses)

  • SQL Server for Database Administrators (6 Courses)

  • SQL for Business Analysts (2/5 Courses)

Reference Books

Basic Knowledge

  • Data Literacy (2015)

  • The Data Journalism Handbook (2012)

  • Practical Statistics for Data Scientists (2017)

R & Tidyverse Fundamentals

  • R Cookbook (2011)

  • R for Data Science (2016)

  • Advanced R (2014)

  • R Data Science Quick Reference (2019)

Visualization & Communication

  • R Graphics Cookbook (2013)

  • ggplot2: Second Edition (2016)

  • R Markdown (2018)

  • How Charts Lie (2019)