Data, AI, and Cloud Courses

Master skills that matter

675 Courses

Course

Marketing Analytics: Predicting Customer Churn in Python

IntermediateSkill Level
4.5+
297

Learn how to use Python to analyze customer churn and build a model to predict it.

Exploratory Data Analysis

Course

Introduction to Amazon Bedrock

IntermediateSkill Level
4.5+
293

Learn to use Amazon Bedrock to access foundation AI models and build with AI - without managing complex infrastructure.

Artificial Intelligence

Course

Create Engaging Video with Google Vids

BasicSkill Level
4.7+
292

Create and refine videos faster with Gemini in Google Vids. Use AI-powered storyboarding and content generation to produce polished videos with ease.

Cloud

Course

Inference for Categorical Data in R

AdvancedSkill Level
4.4+
290

In this course youll learn how to leverage statistical techniques for working with categorical data.

Probability & Statistics

Course

Gemini in Google Drive

BasicSkill Level
4.7+
289

Organize and manage files with Gemini in Google Drive. Use AI-powered search to quickly find information, streamline collaboration, and boost productivity.

Artificial Intelligence

Course

Gemini in Google Slides

BasicSkill Level
4.6+
289

Create impactful presentations faster with Gemini in Google Slides. Use AI-powered design and suggestions to build professional, engaging slides in minutes.

Artificial Intelligence

Course

Plan and Implement a Data Analytics Environment with Microsoft Fabric

BasicSkill Level
4.5+
286

Learn how to set up and manage your Microsoft Fabric infrastructure.

Other

Course

Introduction to Redshift

IntermediateSkill Level
4.6+
285

Master Amazon Redshifts SQL, data management, optimization, and security.

Data Engineering

Course

Case Study: Competitor Sales Analysis in Power BI

IntermediateSkill Level
4.6+
284

This Power BI case study follows a real-world business use case where you will apply the concepts of ETL and visualization.

Data Visualization

Course

Case Study: Building Software in Python

AdvancedSkill Level
4.6+
284

Build real-world applications with Python—practice using OOP and software engineering principles to write clean and maintainable code.

Software Development

Course

Customer Analytics and A/B Testing in Python

IntermediateSkill Level
4.4+
280

Learn how to use Python to create, run, and analyze A/B tests to make proactive business decisions.

Probability & Statistics

Course

Introduction to DataLab

BasicSkill Level
4.5+
278

Learn the fundamentals of using DataLab, an AI-powered data notebook for data analysis and exploration.

Reporting

Course

Gemini in Google Meet

BasicSkill Level
4.7+
277

Enhance virtual meetings with Gemini in Google Meet. Leverage AI-driven summaries, notes, and tools to make every meeting more efficient and actionable.

Artificial Intelligence

Course

Marketing Analytics in Tableau

IntermediateSkill Level
4.7+
272

Master marketing analytics using Tableau. Analyze performance, benchmark metrics, and optimize strategies across channels.

Data Preparation

Course

Modeling with tidymodels in R

IntermediateSkill Level
4.6+
271

Learn to streamline your machine learning workflows with tidymodels.

Machine Learning

Course

Factor Analysis in R

AdvancedSkill Level
4.6+
269

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

Probability & Statistics

Course

A/B Testing in R

IntermediateSkill Level
4.3+
268

Learn the basics of A/B testing in R, including how to design experiments, analyze data, predict outcomes, and present results through visualizations.

Probability & Statistics

Course

Statistical Simulation in Python

IntermediateSkill Level
4.8+
264

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

Probability & Statistics

Course

Working with the OpenAI Responses API

IntermediateSkill Level
4.6+
263

Build smart, interactive, and reliable AI applications easier than ever before with the OpenAI Responses API and GPT-5.

Artificial Intelligence

Course

Azure API Management

IntermediateSkill Level
4.4+
263

Learn to create, secure, and manage APIs with Azure API Management through hands-on practice.

Cloud

Course

Analyzing Survey Data in R

IntermediateSkill Level
4.4+
261

Learn survey design using common design structures followed by visualizing and analyzing survey results.

Probability & Statistics

Course

MLOps for Business

BasicSkill Level
4.8+
257

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

Machine Learning

Course

Introduction to Scala

IntermediateSkill Level
4.7+
257

Begin your journey with Scala, a popular language for scalable applications and data engineering infrastructure.

Software Development

Course

Data Transformation in KNIME

BasicSkill Level
4.6+
256

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

Data Preparation

Course

Building Marketing Workflows with n8n

BasicSkill Level
4.8+
254

Build marketing workflows in n8n using AI agents. Automate campaign strategy, conversion optimization, and lead generation from scratch.

Artificial Intelligence

Course

Introduction to Testing in Java

AdvancedSkill Level
4.7+
254

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

Software Development

Course

Statistical Thinking in Python (Part 2)

IntermediateSkill Level
4.5+
254

Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing.

Probability & Statistics

Course

Spoken Language Processing in Python

AdvancedSkill Level
4.7+
252

Learn how to load, transform, and transcribe speech from raw audio files in Python.

Data Manipulation

Course

Inference for Linear Regression in R

AdvancedSkill Level
4.4+
252

In this course youll learn how to perform inference using linear models.

Probability & Statistics

Course

Introduction to Business Valuation

BasicSkill Level
4.8+
251

Learn business valuation with real-world applications and case studies using discounted cash flows (DCF).

Applied Finance

FAQs

What is data science?

Data science is an area of expertise focused on gaining information from data. Using programming skills, scientific methods, algorithms, and more, data scientists analyze data to form actionable insights.

How can I learn data science?

You’ll need to learn a programming language such as Python or R and master the principles of math and statistics. Knowledge of data analysis methods and data science tools is also essential. There are many ways to learn data science. As well as formal means of education, such as a degree or university study, there are plenty of other resources to help you learn at your own pace. As well as online courses and tutorials, there are books, videos, and more.

What skills are required for data science?

As well as knowledge of mathematics and statistics, data scientists need programming skills in languages such as Python, R, and SQL. Additionally, data science requires the ability to work with large data sets, knowledge of data visualization, data wrangling, and database management. Skills in machine learning and deep learning can also be useful.

What can I use data science for?

In a professional capacity, almost every industry can use data science to some degree. Healthcare organizations use data science to detect and cure diseases, while finance companies use it to detect and prevent fraud. All kinds of industries use data science for marketing, such as building recommendation systems and analyzing customer churn.

Is data science a good career?

Yes, data science is among the fastest-growing sectors in the US and worldwide. It’s also one of the best-paid careers out there. According to data from Payscale, experience data scientists earn an average of $97,609 and have a satisfaction rating of four stars out of five in the US.

Is it difficult to become a data scientist?

There are a few things to consider here. First, data science degrees can be competitive to get onto, often requiring consistently high grades. Similarly, many of the skills required for data science require a lot of study and patience. It can take several months to master all of the necessary basics, as well as a lot of practical experience to secure an entry-level position.

Does data science require coding?

Yes, you’ll need some coding experience in languages such as Python, R, SQL, Java, and C/C++. However, due to its relatively simple syntax, Python programming language is often the preferred choice among newcomers.

How long does it take to become a data scientist?

For a person with no prior coding experience and/or mathematical background, it can typically take 7 to 12 months of intensive studies to be at the level of an entry-level data scientist. However, it is important to remember that learning only the theoretical basis of data science may not make you a real data scientist.

What topics can I study within data science?

Once you’ve mastered the foundations of data science, you can then specialize in a variety of areas, including machine learning, artificial intelligence, big data analysis, business analytics and intelligence, data mining, and more.