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Data Science Skill Paths

Each Skill Path is a curated sequence of lessons, teaching you everything you need to know on the most popular data topics.

SQL Fundamentals


Learn how to extract data from databases using SQL.

  • How to use SQL to explore and extract data
  • How to use SQL to organize data
  • How to write SQL queries to find summary statistics
  • How to query across multiple tables
  • How to answer business questions using SQL

Machine Learning Introduction with Python


Learn how machine learning can help you make predictions with data.

  • The basics of machine learning, including avoiding common pitfalls and evaluating model performance
  • Common techniques like k-nearest neighbours, k-means clustering, and decision trees
  • Foundational mathematics for machine learning, including calculus and linear algebra

Machine Learning Intermediate with Python


Learn more advanced machine learning techniques with Python.

  • How to work with neural networks
  • The basics of building a machine learning project from start to finish
  • How to build a machine learning model and make your first Kaggle submission
  • How to select the best algorithm and tune your model for best performance

Data Visualization with R


Learn how to use R for data visualization.

  • How to visualize changes overtime with line graphs
  • How to use histograms to understand data distributions
  • How to compare groups using bar charts and box plots
  • How to understand relationships between variables using scatter plots

Data Analysis and Visualization with Python


Learn how Python and Pandas simplify data analysis and visualization.

  • How to use Python libraries Pandas and NumPy for data analysis 
  • How to use the most common data visualization techniques to explore data
  • How to use data visualization to convey insights and tell a story
  • Techniques for manipulating and cleaning data

APIs and Web Scraping with R


Learn how to acquire data from APIs and the web with R.

  • How to query external data sources with an API using R
  • The basics of scraping data from the web

Python Basics for Data Analysis


Introduce yourself to the python programming language.

  • Fundamentals of programming in Python
  • How to use Jupyter notebooks
  • How to work with text, date, and time data
  • Basics of object-oriented programming in Python

Probability and Statistics with Python


Learn probability and statistics for more robust data analysis.

  • Basics of statistical analysis, including sampling, working with variables, and understanding frequency distribution tables
  • How to summarize a distribution's measures of central tendency and variability 
  • The fundamentals of probability and how to use them for analysis 
  • How to create and test hypotheses using significance testing

R Basics for Data Analysis


Introduce yourself to the R programming language.

  • The fundamentals of programming in R
  • How to use control and flow iteration
  • How to work with and create your own functions
  • The basics of working with strings and dates

APIs and Web Scraping with Python


Learn how to acquire data from APIs and the web with Python.

  • How to query external data sources with an API using Python
  • The basics of scraping data from the web

Probability and Statistics with R


Learn probability and statistics for more robust data analysis.

  • The basics of statistical analysis, including sampling, working with variables, and understanding frequency distribution tables
  • How to summarize a distribution's measures of central tendency and variability 
  • The fundamentals of probability and how to use them for analysis 
  • How to create and test hypotheses using significance testing

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