On September 19 2024, DataCamp will make some improvements to our content catalog, including renaming, updating, and archiving courses, projects, and tracks.
Course Deprecations
No courses will be deprecated this quarter.
Course Renames
No courses will be renamed this quarter.
Track Renames
# | Track ID | Track Title | New Track Title |
1. | 16 | Data Analyst with Python | Data Analyst in Python |
2. | 5 | Data Analyst with R | Data Analyst in R |
3. | 2162 | Machine Learning Scientist with Python | Machine Learning Scientist in Python |
4. | 2014 | Machine Learning Scientist with R | Machine Learning Scientist in R |
5. | 1946 | Statistician with R | Statistician in R |
6. | 20 | Quantitative Analyst with R | Quantitative Analyst in R |
7. | 24 | Machine Learning Fundamentals with Python | Machine Learning Fundamentals in Python |
8. | 14 | Data Manipulation with Python | Data Manipulation in Python |
9. | 9 | Data Manipulation with R | Data Manipulation in R |
10. | 1206 | Statistics Fundamentals with Python | Statistics Fundamentals in Python |
11. | 10 | Statistics Fundamentals with R | Statistics Fundamentals in R |
12. | 1210 | Data Visualization with Python | Data Visualization in Python |
13. | 8 | Data Visualization with R | Data Visualization in R |
14. | 28 | Time Series with Python | Time Series in Python |
15. | 15 | Time Series with R | Time Series in R |
16. | 13 | Importing & Cleaning Data with Python | Importing & Cleaning Data in Python |
17. | 7 | Importing & Cleaning Data with R | Importing & Cleaning Data in R |
18. | 2491 | Marketing Analytics with Python | Marketing Analytics in Python |
19. | 1208 | Marketing Analytics with R | Marketing Analytics in R |
20. | 1899 | Image Processing with Python | Image Processing in Python |
21. | 27 | Shiny Fundamentals with R | Shiny Fundamentals in R |
22. | 49 | Tidyverse Fundamentals with R | Tidyverse Fundamentals in R |
23. | 30 | Big Data with R | Big Data in R |
24. | 1209 | Statistical Inference with R | Statistical Inference in R |
25. | 25 | Text Mining with R | Text Mining in R |
26. | 506 | Network Analysis with R | Network Analysis in R |
Career Track Changes
Machine Learning Engineer
Current Track:
- MLOps Concepts
- End-to-End Machine Learning
- Developing Machine Learning Models for Production
- Project: Predictive Modeling for Agriculture
- Introduction to Shell
- MLOps Deployment and Lifecycling
- Introduction to MLflow
- Project: Predicting Temperature in London
- Fully Automated MLOps
- ETL and ELT in Python
- Monitoring Machine Learning Concepts
- Monitoring Machine Learning in Python
- Introduction to Docker
- CI/CD for Machine Learning
New Track:
- MLOps Concepts
- End-to-End Machine Learning
- Project: Predictive Modeling for Agriculture
- Introduction to Shell
- MLOps Deployment and Lifecycling
- Introduction to MLflow
- Project: Predicting Temperature in London
- ETL and ELT in Python
- Introduction to Data Versioning with DVC
- Monitoring Machine Learning Concepts
- Monitoring Machine Learning in Python
- Introduction to Docker
- CI/CD for Machine Learning
Removed Content
# | Content ID | Content Title | Instructor Name |
1. | 30380 | Developing Machine Learning Models for Production | Sinan Ozdemir |
2. | 30016 | Fully Automated MLOps | DataCamp |
Added Content
# | Content ID | Content Title | Instructor Name |
1. | 35486 | Introduction to Data Versioning with DVC | DataCamp |
Professional Data Engineer
Current Track:
- Understanding Modern Data Architecture
- Introduction to Shell
- Containerization and Virtualization Concepts
- Introduction to dbt
- Introduction to Object-Oriented Programming in Python
- Introduction to NoSQL
- DevOps Concepts
- Introduction to Testing in Python
- Project: Debugging a Sales Data Workflow
- Introduction to Docker
- Chapter: Getting to know PySpark
- Chapter: Manipulating data
- Chapter: Introduction to Big Data analysis with Spark
- Chapter: Programming in PySpark RDD’s
- Chapter: PySpark SQL & DataFrames
- Project: Cleaning an Orders Dataset with PySpark
- Chapter: Downloading Data on the Command Line
- Chapter: Data Pipeline on the Command Line
- Streaming Concepts
- Webinar: Impactful Data Engineering – with Datadog’s Wouter de Bie
New Track:
- Understanding Modern Data Architecture
- Introduction to Shell
- Containerization and Virtualization Concepts
- Introduction to dbt
- Introduction to Object-Oriented Programming in Python
- Introduction to NoSQL
- DevOps Concepts
- Introduction to Testing in Python
- Project: Debugging a Sales Data Workflow
- Introduction to Docker
- Chapter: Getting to know PySpark
- Chapter: Manipulating data
- Chapter: Introduction to Big Data analysis with Spark
- Chapter: Programming in PySpark RDD’s
- Chapter: PySpark SQL & DataFrames
- Project: Cleaning an Orders Dataset with PySpark
- Chapter: Downloading Data on the Command Line
- Chapter: Data Pipeline on the Command Line
- Streaming Concepts
- Introduction to Kafka
- Introduction to Kubernetes
- Webinar: Impactful Data Engineering – with Datadog’s Wouter de Bie
Removed Content
N/A
Added Content
# | Content ID | Content Title | Instructor Name |
1. | 36655 | Introduction to Kafka | DataCamp |
2. | 35597 | Introduction to Kubernetes | DataCamp |
Data Engineer in Python
Current Track:
- Understanding Cloud Computing
- Introduction to Python for Developers
- Intermediate Python for Developers
- Introduction to Importing Data in Python
- Intermediate Importing Data in Python
- Cleaning Data in Python
- Project: Cleaning Bank Marketing Campaign Data
- Writing Efficient Python Code
- Streamlined Data Ingestion with pandas
- Introduction to Git
- Software Engineering Principles in Python
- Project: Performing a Code Review
- ETL and ELT in Python
- Introduction to Airflow in Python
- Project: Building a Retail Data Pipeline
New Track:
- Understanding Cloud Computing
- Introduction to Python for Developers
- Intermediate Python for Developers
- Introduction to Importing Data in Python
- Intermediate Importing Data in Python
- Cleaning Data in Python
- Project: Cleaning Bank Marketing Campaign Data
- Writing Efficient Python Code
- Streamlined Data Ingestion with pandas
- Introduction to Git
- Software Engineering Principles in Python
- Project: Performing a Code Review
- Containerization and Virtualization Concepts
- ETL and ELT in Python
- Introduction to Airflow in Python
- Project: Building a Retail Data Pipeline
Removed Content
N/A
Added Content
# | Content ID | Content Title | Instructor Name |
1. | 36462 | Containerization and Virtualization Concepts | DataCamp |
Python Developer
Current Track:
- Introduction to Testing in Python
- Writing Efficient Python Code (Chapters 1-3)
- Introduction to Git
- Developing Python Packages
- Web Scraping in Python
- Project: Building a Calorie Intake Calculator
- Data Structures and Algorithms in Python
New Track:
- Introduction to Testing in Python
- Writing Efficient Python Code (Chapters 1-3)
- Introduction to Git
- Intermediate Object-Oriented Programming in Python
- Developing Python Packages
- Web Scraping in Python
- Project: Building a Calorie Intake Calculator
- Data Structures and Algorithms in Python
Removed Content
N/A
Added Content
# | Content ID | Content Title | Instructor Name |
1. | 36306 | Intermediate Object-Oriented Programming in Python | DataCamp |
Associate Data Scientist in R
Current Track:
- Introduction to R
- Intermediate R
- Introduction to the Tidyverse
- Data Manipulation with dplyr
- Project: Dr. Semmelweis and the Importance of Handwashing
- Joining Data with dplyr
- Introduction to Statistics in R
- Introduction to Data Visualization with ggplot2
- Intermediate Data Visualization with ggplot2
- Assessment: Data Manipulation with R
- Data Communication Concepts
- Introduction to Importing Data in R
- Cleaning Data in R
- Project: Exploring Airbnb Market Trends
- Working with Dates and Times in R
- Assessment: Importing & Cleaning Data with R
- Introduction to Writing Functions in R
- Assessment: R Programming
- Exploratory Data Analysis in R
- Introduction to Regression in R
- Project: Modeling Car Insurance Claim Outcomes
- Intermediate Regression in R
- Sampling in R
- Hypothesis Testing in R
- Project: Hypothesis Testing in Men’s and Women’s Soccer Matches
- Experimental Design in R
- Assessment: Statistics Fundamentals with R
- Supervised Learning in R: Classification
- Supervised Learning in R: Regression
- Unsupervised Learning in R
New Track:
- Introduction to R
- Intermediate R
- Introduction to the Tidyverse
- Data Manipulation with dplyr
- Project: Analyze the Popularity of Programming Languages
- Joining Data with dplyr
- Introduction to Statistics in R
- Introduction to Data Visualization with ggplot2
- Intermediate Data Visualization with ggplot2
- Assessment: Data Manipulation with R
- Data Communication Concepts
- Introduction to Importing Data in R
- Cleaning Data in R
- Project: Exploring Airbnb Market Trends
- Working with Dates and Times in R
- Assessment: Importing & Cleaning Data with R
- Introduction to Writing Functions in R
- Assessment: R Programming
- Exploratory Data Analysis in R
- Introduction to Regression in R
- Project: Modeling Car Insurance Claim Outcomes
- Intermediate Regression in R
- Sampling in R
- Hypothesis Testing in R
- Project: Hypothesis Testing in Men’s and Women’s Soccer Matches
- Experimental Design in R
- Assessment: Statistics Fundamentals with R
- Supervised Learning in R: Classification
- Supervised Learning in R: Regression
- Unsupervised Learning in R
Removed Content
# | Content ID | Content Title | Instructor Name |
1 | 1991 | Dr. Semmelweis and the Importance of Handwashing | DataCamp |
Added Content
# | Content ID | Content Title | Instructor Name |
1 | 2557 | Analyze the Popularity of Programming Languages | DataCamp |
Data Analyst with R
Current Track:
- Introduction to R
- Introduction to the Tidyverse
- Data Manipulation with dplyr
- Project: Dr. Semmelweis and the Importance of Handwashing
- Joining Data with dplyr
- Introduction to Statistics in R
- Introduction to Data Visualization with ggplot2
- Assessment: Data Manipulation with R
- Exploratory Data Analysis in R
- Sampling in R
- Hypothesis Testing in R
- Project: Hypothesis Testing in Men’s and Women’s Soccer Matches
New Track:
- Introduction to R
- Introduction to the Tidyverse
- Data Manipulation with dplyr
- Project: Analyze the Popularity of Programming Languages
- Joining Data with dplyr
- Introduction to Statistics in R
- Introduction to Data Visualization with ggplot2
- Assessment: Data Manipulation with R
- Exploratory Data Analysis in R
- Sampling in R
- Hypothesis Testing in R
- Project: Hypothesis Testing in Men’s and Women’s Soccer Matches
Removed Content
# | Content ID | Content Title | Instructor Name |
1 | 1991 | Dr. Semmelweis and the Importance of Handwashing | DataCamp |
Added Content
# | Content ID | Content Title | Instructor Name |
1 | 2557 | Analyze the Popularity of Programming Languages | DataCamp |
R Developer
Current Track:
- Introduction to R
- Data Manipulation with dplyr
- Project: Dr. Semmelweis and the Importance of Handwashing
- Writing Efficient R Code
- Working with Dates and Times in R
- String Manipulation with stringr in R
- Assessment: Data Manipulation with R
- Web Scraping in R
- Introduction to Writing Functions in R
- Introduction to Shell
- Introduction to Git
- Parallel Programming in R
- Defensive R Programming
- Developing R Packages
- Object-Oriented Programming with S3 and R6 in R
- Assessment: R Programming
New Track:
- Introduction to R
- Data Manipulation with dplyr
- Project: Analyze the Popularity of Programming Languages
- Writing Efficient R Code
- Working with Dates and Times in R
- String Manipulation with stringr in R
- Assessment: Data Manipulation with R
- Web Scraping in R
- Introduction to Writing Functions in R
- Introduction to Shell
- Introduction to Git
- Parallel Programming in R
- Defensive R Programming
- Developing R Packages
- Object-Oriented Programming with S3 and R6 in R
- Assessment: R Programming
Removed Content
# | Content ID | Content Title | Instructor Name |
1 | 1991 | Dr. Semmelweis and the Importance of Handwashing | DataCamp |
Added Content
# | Content ID | Content Title | Instructor Name |
1 | 2557 | Analyze the Popularity of Programming Languages | DataCamp |
Skill Track Changes
Python Data Fundamentals
Current Track:
- Introduction to Python
- Intermediate Python
- Project: Investigating Netflix Movies
- Introduction to Functions in Python
- Python Toolbox
- Assessment: Python Programming
New Track:
- Introduction to Python
- Intermediate Python
- Project: Investigating Netflix Movies
- Data Manipulation with pandas
- Joining Data with pandas
- Introduction to Data Visualization with Seaborn
- Introduction to Statistics in Python
- Exploratory Data Analysis in Python
- Project: Analyzing Crime in Los Angeles
Removed Content
# | Content ID | Content Title | Instructor Name |
1. | 1532 | Introduction to Functions in Python | DataCamp |
2. | 1531 | Python Toolbox | DataCamp |
3. | 1679 | Assessment: Python Programming | DataCamp |
Added Content
# | Content ID | Content Title | Instructor Name |
1. | 22066 | Data Manipulation with pandas | DataCamp |
2. | 22639 | Joining Data with pandas | DataCamp |
3. | 15192 | Introduction to Data Visualizaton with Seaborn | DataCamp |
4. | 25412 | Introduction to Statistics in Python | DataCamp |
5. | 30656 | Exploratory Data Analysis in Python | DataCamp |
6. | 1876 | Project: Analyzing Crime in Los Angeles | DataCamp |
Tidyverse Fundamentals with R
Current Track:
- Introduction to the Tidyverse
- Reshaping Data with tidyr
- Project: Dr. Semmelweis and the Importance of Handwashing
- Modeling with Data in the Tidyverse
- Communicating with Data in the Tidyverse
- Categorical Data in the Tidyverse
New Track:
- Introduction to the Tidyverse
- Reshaping Data with tidyr
- Project: Analyze the Popularity of Programming Languages
- Modeling with Data in the Tidyverse
- Communicating with Data in the Tidyverse
- Categorical Data in the Tidyverse
Removed Content
# | Content ID | Content Title | Instructor Name |
1 | 1991 | Dr. Semmelweis and the Importance of Handwashing | DataCamp |
Added Content
# | Content ID | Content Title | Instructor Name |
1 | 2557 | Analyze the Popularity of Programming Languages | DataCamp |
Alteryx Fundamentals
Current Track:
- Introduction to Alteryx
- Data Preparation in Alteryx
- Data Transformation in Alteryx
- Data Manipulation in Alteryx
- Case Study: Analyzing Sales Data in Alteryx
New Track:
- Introduction to Alteryx
- Data Preparation in Alteryx
- Data Transformation in Alteryx
- Data Manipulation in Alteryx
- Case Study: Analyzing Sales Data in Alteryx
- Case Study: Analyzing Fitness Data in Alteryx
Removed Content
N/A
Added Content
# | Content ID | Content Title | Instructor Name |
36546 | Case Study: Analyzing Fitness Data in Alteryx | Giniya Gupta |
Data Skills for Business
Current Track:
- Data Science for Business
- Introduction to Google Sheets
- Machine Learning for Business
- Data-Driven Decision-Making for Business
- Marketing Analytics for Business
- Understanding Artificial Intelligence
- Introduction to Data Science in Python
New Track:
- Introduction to Data Literacy
- Introduction to Data
- Data Governance Concepts
- Introduction to Data Ethics
- Data Management Concepts
- Understanding Artificial Intelligence
- Data Strategy
Removed Content
# | Content ID | Content Title | Instructor Name |
1 | 17602 | Data Science for Business | Datacamp |
2 | 29847 | Introduction to Google Sheets | Datacamp |
3 | 23080 | Machine Learning for Business | Datacamp |
4 | 25814 | Data-Driven decision-making for business | Ted Kwartler |
5 | 26720 | Marketing analytics for business | Sarah DeAtley |
6 | 13371 | Introduction to Data Science in Python | Datacamp |
Added Content
# | Content ID | Content Title | Instructor Name |
1 | 29478 | Introduction to Data Literacy | DataCamp |
2 | 29830 | Introduction to Data | DataCamp |
3 | 29911 | Data Governance Concepts | DataCamp |
4 | 31939 | Introduction to Data Ethics | DataCamp |
5 | 34147 | Data Management Concepts | DataCamp |
6 | 35244 (not live yet) | Data Strategy | DataCamp |
ChatGPT Fundamentals
Current Track:
- Introduction to ChatGPT
- Understanding Prompt Engineering
New Track:
- Introduction to ChatGPT
- Understanding Prompt Engineering
- Intermediate ChatGPT
Removed Content
N/A
Added Content
# | Content ID | Content Title | Instructor Name |
1 | 36814 | Intermediate ChatGPT | DataCamp |
Project Deprecations
Projects with Replacements
# | Project ID | Project Title | Replacement Project(s) | Replacement ID(s) |
1 | 10 | Exploring the History of Lego | Examining the History of Lego Sets | 2378 |
2 | 20 | Dr. Semmelweis and the Discovery of Handwashing (Python)(Guided) | Dr. Semmelweis and the Importance of Handwashing (Python) | 2526 |
3 | 166 | Visualizing Inequalities in Life Expectancy | Exploring Global Life Expectancy Trends | 2079 |
4 | 435 | Rise and Fall of Programming Languages (Guided) | Analyze the Popularity of Programming Languages | 2557 |
5 | 515 | Functions for Food Price Forecasts (Guided) | Food Price Forecasting with Functions | 2559 |
6 | 538 | Comparing Search Interest with Google Trends (Guided) | Understanding Search Interest with Google Trends | 2504 |
7 | 684 | Analyzing TV Data | Extracting TV Data Insights | In progress |
8 | 691 | TV, Halftime Shows, and the Big Game | Analyzing Super Bowl Viewership and Advertising | 2531 |
9 | 981 | Exploring the Evolution of Lego | Examining the History of Lego Sets | 2378 |
10 | 1008 | Functions for Food Price Forecasts (Unguided) | Food Price Forecasting with Functions | 2559 |
11 | 1168 | What and Where are the World’s Oldest Businesses (Guided)(SQL) | Uncovering the World's Oldest Businesses (SQL) | 2519 |
12 | 1174 | Rise and Fall of Programming Languages (Unguided) | Analyze the Popularity of Programming Languages | 2557 |
13 | 1383 | What and Where are the World’s Oldest Businesses (Guided)(Python) | Uncovering the World's Oldest Businesses (Python) | 2510 |
14 | 1441 | Analyzing American Baby Name Trends | Exploring Trends in American Baby Names | In progress |
Projects with Recommended Alternatives
# | Project ID | Project Title | Recommended Alternative | Alternative ID |
1 | 76 | A Network Analysis of Game of Thrones | Social Network Analysis | 2479 |
2 | 132 | Recreating John Snow’s Ghost Map | No replacement | N/A |
3 | 139 | Level Difficulty in Candy Crush Saga | Visualizing the History of Nobel Prize Winners | 1890 |
4 | 184 | Mobile Games A/B Testing with Cookie Cats | Hypothesis Testing with Men’s and Women’s Soccer Matches | 1611 |
5 | 449 | Classify Song Genres from Audio Data | No replacement | N/A |
6 | 496 | Predict Taxi Fares with Random Forests | Predict Energy Consumption | 2387 |
7 | 504 | Which Debts Are Worth the Bank’s Effort? | Will this Customer Purchase your Product? | 2470 |
8 | 619 | The Android App Market on Google Play (Guided) | Understanding Subscription Behaviors | 2474 |
9 | 648 | Find Movie Similarity from Plot Summaries | Reveal Categories Found in Data | 2472 |
10 | 695 | Comparing Cosmetics by Ingredients | No replacement | N/A |
11 | 727 | Analyze Your Runkeeper Fitness Data | No replacement | N/A |
12 | 738 | Modeling the Volatility of US Bond Yields | No replacement | N/A |
13 | 740 | Disney Movies and Box Office Success | Modeling Car Insurance Claim Outcomes | 1645 |
14 | 760 | Real-time Insights from Social Media Data | No replacement | N/A |
15 | 1197 | The Android App Market on Google Play (Unguided) | Understanding Subscription Behaviors | 2474 |
16 | 1234 | Writing Functions for Product Analysis | Data-Driven Product Management: Conducting a Market Analysis | 1684 |