Data science courses

This course is an introduction to linear algebra, one of the most important mathematical topics underpinning data science.

Learn to perform linear and logistic regression with multiple explanatory variables.

In this course, youll learn the basics of relational databases and how to interact with them.

Parse data in any format. Whether its flat files, statistical software, databases, or data right from the web.

Learn all about the advantages of Bayesian data analysis, and apply it to a variety of real-world use cases!

In this course, students will learn to write queries that are both efficient and easy to read and understand.

Learn to manipulate and analyze flexibly structured data with MongoDB.

Learn how to detect fraud using Python.

Learn to use essential Bioconductor packages for bioinformatics using datasets from viruses, fungi, humans, and plants!

This course will show you how to integrate spatial data into your Python Data Science workflow.

Visualize seasonality, trends and other patterns in your time series data.

Learn about MLOps architecture, CI/CD/CM/CT techniques, and automation patterns to deploy ML systems that can deliver value over time.

In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.

Learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.

Detect anomalies in your data analysis and expand your Python statistical toolkit in this four-hour course.

Begin your journey with Scala, a popular language for scalable applications and data engineering infrastructure.

In this course, youll learn how to import and manage financial data in Python using various tools and sources.

Learn how to make attractive visualizations of geospatial data in Python using the geopandas package and folium maps.

Learn powerful command-line skills to download, process, and transform data, including machine learning pipeline.

Learn the core techniques necessary to extract meaningful insights from time series data.

Master time series data manipulation in R, including importing, summarizing and subsetting, with zoo, lubridate and xts.

Master SQL Server programming by learning to create, update, and execute functions and stored procedures.

Learn how to use Python scripts in Power BI for data prep, visualizations, and calculating correlation coefficients.

This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.

Learn the most important functions for manipulating, processing, and transforming data in SQL Server.

Learn to build recommendation engines in Python using machine learning techniques.

Learn how to segment customers in Python.

Analyze text data in R using the tidy framework.

Learn fundamental probability concepts like random variables, mean and variance, probability distributions, and conditional probabilities.

Learn basic business modeling including cash flows, investments, annuities, loan amortization, and more using Google Sheets.