Good news! We're updating the Data Science Career Path to a new and improved Data Scientist Career Path.
Data science is all about using data to identify pathways to improvement — and our data science content is no exception! We took a hard look at our Data Science Career Path and identified several ways we could make the new Data Scientist Career Path a better experience for you.
We're adding several projects, articles, and curated resources — including books and documentation — to introduce and scaffold important concepts in data science. The Python unit has all-new projects focused on real-world data science problems. We're releasing a brand-new content type: the Portfolio Project, which can help you move off the Codecademy platform and develop a portfolio that will make you stand out to prospective employers. Additionally, we've rearranged some of the content so that it better reflects the data science workflow. Finally, we've integrated connective tissue to clarify what you're covering in each unit and how it relates to your overall Data Scientist Career Path journey.
How does this impact you?
If you've already started or completed the Data Science Path, you will see some new content added to the Path.
You won’t lose progress on the coursework that you’ve already completed; however, you may notice that your overall progress percentage has decreased, as there is a greater amount of content included in the improved Path.
The Path will be added to the catalog on Tuesday, October 20, 2020.
Take a look below to get an overview of the updates.
Updates in the Data Scientist Career Path
New content covering:
- Introduction to Data Science
- The Data Science Process
- Data Science Applications
- Introducing Jupyter Notebook
- Setting up Jupyter Notebook
- Getting Started with Jupyter
- Getting More out of Jupyter Notebook
- Getting Started with Git and GitHub Desktop
- Python Strings
- Python Classes
- Python Modules
- Python Files
- Data Acquisition
- Relational Databases for Data Science/Analysis
- Python with Databases
- What Are NumPy and Pandas
- Introduction to Data Wrangling and Tidying
- The Central Limit Theorem
- Introduction To Hypothesis Testing
- Best Practices for Data Visualization
- Communicating Data Science Findings
- Text Preprocessing
- Word Embeddings
- Principal Component Analysis
- What Is Deep Learning?
- The Dangers of the Black Box
Python projects focused on real-world medical insurance data:
- Python Syntax
- Python Functions
- Python Control Flow
- Python Lists
- Python Loops
- Python Strings
- Python Dictionaries
- Python Classes
Open-ended Portfolio Projects:
- Data Visualization
- Data Analysis
- Machine Learning
- Final Portfolio Project
Curated resources throughout:
Removing some less relevant and repetitive content:
Note: Although this content is being removed from our Data Scientist Path, it will still be available in other Paths and courses at the following links.
- Create a Table
- Usage Funnels
- User Churn
- Marketing Attribution
- Attribution Queries
- Python Projects (unrelated to Data Science)
- Learn Statistics with NumPy
- Artificial Intelligence Decision Making: Minimax
- Parsing with Regular Expressions
We're updating our Data Scientist Career Path so that it's an even better learning experience. Starting on October 20, 2020, it will cover additional important topics and makes sure to reinforce all of that learning with engaging cumulative projects.