Data science courses

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

Take your Power BI visualizations up a level with the skills you already have. Learn alternative data storytelling techniques to simply building dashboards.

Discover how to extract business value from AI. Learn to scope opportunities for AI, create POCs, implement solutions, and develop an AI strategy.

Consolidate and extend your knowledge of Python data types such as lists, dictionaries, and tuples, leveraging them to solve Data Science problems.

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

Learn the power of deep learning in PyTorch. Build your first neural network, adjust hyperparameters, and tackle classification and regression problems.

Learn how to implement and schedule data engineering workflows.

This introductory course will help you hone the skills to build effective, performant, and reliable data pipelines.

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

Learn about string manipulation and become a master at using regular expressions.

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

Enhance your reports with trend analysis techniques such as time series, decomposition trees, and key influencers.

Learn the fundamentals of working with big data with PySpark.

Explore data ethics with this comprehensive introductory course, covering principles, AI ethics, and practical skills to ensure responsible data use.

Discover the different ways you can enhance your Power BI data importing skills.

Learn how to build and tune predictive models and evaluate how well they'll perform on unseen data.

Discover how to make better business decisions by applying practical data frameworks—no coding required.

In this course, you will use T-SQL, the flavor of SQL used in Microsoft's SQL Server for data analysis.

Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow.

In this course, you will be introduced to unsupervised learning through techniques such as hierarchical and k-means clustering using the SciPy library.

Learn how to write unit tests for your Data Science projects in Python using pytest.

Leverage your Python and SQL knowledge to create an ETL pipeline to ingest, transform, and load data into a database.

Discover how MLOps can take machine learning models from local notebooks to functioning models in production that generate real business value.

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

Learn how to analyze data with spreadsheets using functions such as SUM(), AVERAGE(), and VLOOKUP().

In this course you will learn the basics of machine learning for classification.

In this course you will learn the details of linear classifiers like logistic regression and SVM.

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 how to deploy and maintain assets in Power BI. You’ll get to grips with the Power BI Service interface and key elements in it like workspaces.