DataCamp Courses Summary
Last updated on：July 1, 2022
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)
- Data Science for Everyone
- Machine Learning for Everyone
- Data Visualization for Everyone
- Data Engineering for Everyone
- Cloud Computing for Everyone
Note: No coding/programming involved.
Skill Track: Spreadsheet Fundamentals
- Data Analysis in Spreadsheets
- Intermediate Spreadsheets
- Pivot Tables in Spreadsheets
- Data Visualization in Spreadsheets
Skill Track: Intermediate Spreadsheets
- Introduction to Statistics in Spreadsheets
- Error and Uncertainty in Spreadsheets
- 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
|1||Introduction to R||/||No videos|
|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.
|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
- Introduction to SQL Server
- Introduction to Relational Databases in SQL
- Intermediate SQL Server
- Time Series Analysis in SQL Server
- Functions for Manipulating Data in SQL Server
- Database Design
- Hierarchical and Recursive Queries in SQL Server (*removed)
- Transactions and Error Handling in SQL Server
- Writing Functions and Stored Procedures in SQL Server
- Building and Optimizing Triggers in SQL Server
- Improving Query Performance in SQL Server
Note: Hierarchical and Recursive Queries in SQL Server has been removed from this track’s new version.
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)
Data Literacy (2015)
The Data Journalism Handbook (2012)
Practical Statistics for Data Scientists (2017)
R Cookbook (2011)
R for Data Science (2016)
Advanced R (2014)
R Data Science Quick Reference (2019)
R Graphics Cookbook (2013)
ggplot2: Second Edition (2016)
R Markdown (2018)
How Charts Lie (2019)