Data science courses

Explore latent variables, such as personality, using exploratory and confirmatory factor analyses.

Get ready to categorize! In this course, you will work with non-numerical data, such as job titles or survey responses, using the Tidyverse landscape.

Gain a clear understanding of GDPR principles and how to set up GDPR-compliant processes in this comprehensive course.

Learn to use the Census API to work with demographic and socioeconomic data.

Discover how to analyze and visualize baseball data using Power BI. Create scatter plots, tornado charts, and gauges to bring baseball insights alive.

Learn about the difference between batching and streaming, scaling streaming systems, and real-world applications.

Explore the Stanford Open Policing Project dataset and analyze the impact of gender on police behavior using pandas.

Dive into the world of digital transformation and equip yourself to be an agent of change in a rapidly evolving digital landscape.

Prepare for your next statistics interview by reviewing concepts like conditional probabilities, A/B testing, the bias-variance tradeoff, and more.

From customer lifetime value, predicting churn to segmentation - learn and implement Machine Learning use cases for Marketing in Python.

Practice your Shiny skills while building some fun Shiny apps for real-life scenarios!

Learn about experimental design, and how to explore your data to ask and answer meaningful questions.

Learn sentiment analysis by identifying positive and negative language, specific emotional intent and making compelling visualizations.

Learn how to ensure clean data entry and build dynamic dashboards to display your marketing data.

Diagnose, visualize and treat missing data with a range of imputation techniques with tips to improve your results.

Leverage the tools in the tidyverse to generate, explore and evaluate machine learning models.

Explore a range of programming paradigms, including imperative and declarative, procedural, functional, and object-oriented programming.

Gain an overview of all the skills and tools needed to excel in Natural Language Processing in R.

Master data fluency! Learn skills for individuals and organizations, understand behaviors, and build a data-fluent culture.

Work with risk-factor return series, study their empirical properties, and make estimates of value-at-risk.

Learn to develop R packages and boost your coding skills. Discover package creation benefits, practice with dev tools, and create a unit conversion package.

In ecommerce, increasing sales and reducing costs are key. Analyze data from an online pet supply company using Power BI.

In this Power BI case study you’ll play the role of a junior trader, analyzing mortgage trading and enhancing your data modeling and financial analysis skills.

You will use Net Revenue Management techniques in Excel for a Fast Moving Consumer Goods company.

Learn how to load, transform, and transcribe speech from raw audio files in Python.

Learn to work with time-to-event data. The event may be death or finding a job after unemployment. Learn to estimate, visualize, and interpret survival models!

Learn how to work with streaming data using serverless technologies on AWS.

GAMs model relationships in data as nonlinear functions that are highly adaptable to different types of data science problems.

Learn about the challenges of monitoring machine learning models in production, including data and concept drift, and methods to address model degradation.

Learn the principles of feature engineering for machine learning models and how to implement them using the R tidymodels framework.