Data, AI, and Cloud Courses

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

In this course, youll learn how to collect Twitter data and analyze Twitter text, networks, and geographical origin.

Discover how to analyze and visualize baseball data using Power BI. Create scatter plots, tornado charts, and gauges to bring baseball insights alive.

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

Analyze text data in R using the tidy framework.

Leverage the power of Python and PuLP to optimize supply chains.

Explore ways to work with date and time data in SQL Server for time series analysis

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

Leverage the power of tidyverse tools to create publication-quality graphics and custom-styled reports that communicate your results.

Shift to an MLOps mindset, enabling you to train, document, maintain, and scale your machine learning models to their fullest potential.

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

Orchestrate data using unions, joins, parsing, and performance optimization in Alteryx.

Learn to design and run your own Monte Carlo simulations using Python!

This course will equip you with the skills to analyze, visualize, and make sense of networks using the NetworkX library.

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

Manage the complexity in your code using object-oriented programming with the S3 and R6 systems.

Learn to solve real-world optimization problems using Pythons SciPy and PuLP, covering everything from basic to constrained and complex optimization.

In this course youll learn to use and present logistic regression models for making predictions.

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

Julia is a new programming language designed to be the ideal language for scientific computing, machine learning, and data mining.

Learn how to approach and win competitions on Kaggle.

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

Learn how to manipulate data and create machine learning feature sets in Spark using SQL in Python.

Learn the fundamentals of exploring, manipulating, and measuring biomedical image data.

Learn how to identify, analyze, remove and impute missing data in Python.

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

Practice Power BI with our healthcare case study. Analyze data, uncover efficiency insights, and build a dashboard.

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

Learn about the challenges of monitoring machine learning models in production, including data and concept drift, and methods to address model degradation.

In this course you will learn to fit hierarchical models with random effects.