Data, AI, and Cloud Courses

Learn how to access financial data from local files as well as from internet sources.

Learn to analyze and visualize network data with the igraph package and create interactive network plots with threejs.

Learn how to write effective tests in Java using JUnit and Mockito to build robust, reliable applications with confidence.

Learn how computers work, design efficient algorithms, and explore computational theory to solve real-world problems.

Learn how bonds work and how to price them and assess some of their risks using the numpy and numpy-financial packages.

Learn to read, explore, and manipulate spatial data then use your skills to create informative maps using R.

Learn how to produce interactive web maps with ease using leaflet.

Enhance your KNIME skills with our course on data transformation, column operations, and workflow optimization.

Learn tools and techniques to leverage your own big data to facilitate positive experiences for your users.

Leverage tidyr and purrr packages in the tidyverse to generate, explore, and evaluate machine learning models.

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

Learn how to import, clean and manipulate IoT data in Python to make it ready for machine learning.

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

Learn to streamline your machine learning workflows with tidymodels.

Learn how to use Python parallel programming with Dask to upscale your workflows and efficiently handle big data.

Step into the role of CFO and learn how to advise a board of directors on key metrics while building a financial forecast.

Discover what all of the DeepSeek hype was really about! Build applications using DeepSeeks R1 and V3 models.

Build AI teams that work together, automate workflows, and generate content with CrewAI.

Master core concepts about data manipulation such as filtering, selecting and calculating groupwise statistics using data.table.

Learn how to pull character strings apart, put them back together and use the stringr package.

Learn how to analyze business processes in R and extract actionable insights from enormous sets of event data.

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

Learn to solve increasingly complex problems using simulations to generate and analyze data.

Learn how to design, automate, and monitor scalable forecasting pipelines in Python.

Use survival analysis to work with time-to-event data and predict survival time.

Extract and visualize Twitter data, perform sentiment and network analysis, and map the geolocation of your tweets.

Automate data manipulation with KNIME, mastering merging, aggregation, database workflows, and advanced file handling.

Learn about MLOps, including the tools and practices needed for automating and scaling machine learning applications.

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

Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.