Data, AI, and Cloud Courses

In this course youll learn techniques for performing statistical inference on numerical data.

Learn how to transform raw data into clean, reliable models with dbt through hands-on, real-world exercises.

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

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

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

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

Test a chatbot that matches customers with ideal skincare products using your prompting skills for personalized results.

Learn how to run big data analysis using Spark and the sparklyr package in R, and explore Spark MLIb in just 4 hours.

This course is for R users who want to get up to speed with Python!

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

Analyze time series graphs, use bipartite graphs, and gain the skills to tackle advanced problems in network analytics.

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

Learn to set up a secure, efficient book recommendation app in Azure in this hands-on case study.

Learn how to create and assess measurement models used to confirm the structure of a scale or questionnaire.

Unlock the power of parallel computing in R. Enhance your data analysis skills, speed up computations, and process large datasets effortlessly.

Learn to work with Plain Old Java Objects, master the Collections Framework, and handle exceptions like a pro, with logging to back it all up!

Learn to build pipelines that stand the test of time.

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

Learn efficient techniques in pandas to optimize your Python code.

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

Learn to use R to develop models to evaluate and analyze bonds as well as protect them from interest rate changes.

Develop a better intuition for advanced probability, risk assessment, and simulation techniques to make data-driven business decisions with confidence.

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

Apply financial analysis in KNIME with real-world data, enhancing data preparation and workflow skills.

Explore HR data analysis in Tableau with this case study.

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

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

Dive into our Tableau case study on supply chain analytics. Tackle shipment, inventory management, and dashboard creation to drive business improvements.

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

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