Data science courses

Learn how to build your own SQL reports and dashboards, plus hone your data exploration, cleaning, and validation skills.

Learn how to perform financial analysis in Power BI or apply any existing financial skills using Power BI data visualizations.

Build the foundation you need to think statistically and to speak the language of your data.

Prepare for your next coding interviews in Python.

Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.

Navigate and use the extensive repository of models and datasets available on the Hugging Face Hub.

Discover how Marketing Analysts use data to understand customers and drive business growth.

Discover how to become a data defender and keep data safe and secure with this beginner-friendly interactive course.

Learn to tame your raw, messy data stored in a PostgreSQL database to extract accurate insights.

Build up your pandas skills and answer marketing questions by merging, slicing, visualizing, and more!

You will investigate a dataset from a fictitious company called Databel in Tableau, and need to figure out why customers are churning.

Learn how and when to use hypothesis testing in R, including t-tests, proportion tests, and chi-square tests.

Learn how to clean data with Apache Spark in Python.

Learn to write faster R code, discover benchmarking and profiling, and unlock the secrets of parallel programming.

Master data preparation, cleaning, and analysis in Alteryx Designer, whether you are a new or seasoned analyst.

Master AWS security, governance, and cost optimization to prepare for the Cloud Practitioner certification.

Take your R skills up a notch by learning to write efficient, reusable functions.

Bash scripting allows you to build analytics pipelines in the cloud and work with data stored across multiple files.

Reshape DataFrames from a wide to long format, stack and unstack rows and columns, and wrangle multi-index DataFrames.

Improve data literacy skills by analyzing remote working policies.

Learn how to prepare credit application data, apply machine learning and business rules to reduce risk and ensure profitability.

In this conceptual course (no coding required), you will learn about the four major NoSQL databases and popular engines.

Learn essential data structures such as lists and data frames and apply that knowledge directly to financial examples.

Learn to start developing deep learning models with Keras.

Expand your Google Sheets vocabulary by diving deeper into data types, including numeric data, logical data, and missing data.

Master sampling to get more accurate statistics with less data.

Build on top of your Python skills for Finance, by learning how to use datetime, if-statements, DataFrames, and more.

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

Learn to use facets, coordinate systems and statistics in ggplot2 to create meaningful explanatory plots.

Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.