Free Data Analytics Course

Data insights have become the key driver in many business decisions that drive growth, customer retention, cost reduction, operational efficiencies, and many other levers. In this course, you will learn to think like a data analyst and get experience in tools they use daily. Units include: 

• What to expect from this course

• Developing a study plan

This unit introduces the types of data, various data preparation techniques, the use of various summary statistics to describe data, the use of visualization to understand data in ways that numbers can’t explain, and getting started with various analytics toolsets. Units include:

• Data types

• Data preparation

• Analysis methodology

• Data visualization

• Analytics toolset

Starting from a problem statement to building a persuasive and accurate narrative needs deep analysis of data and a structured approach. Frameworks such as HDIEP (Hypothesis, Data Sourcing, Exploration, Insights, and Presentation) help an analyst focus on the end goal without getting lost in the complexity of the problem. Units include:

• Developing a hypothesis

• Data sourcing

• Exploration

• Developing insights

• Presenting your findings

There are many visualization tools and more than 20+ visualization techniques. For typical data analysis, we may not use more than 3-5 techniques. However, it's good to know the full stack of visualization techniques that are available.

In this module, you’ll get hands-on experience with today’s most popular data visualization tools including:

• Microsoft Excel introduction and key functions

• Power BI

• Tableau

• Looker

Get acquainted with some of the most popular programming languages for data analysis. In this module, you’ll familiarize yourself with coding tools and programs, allowing you to build more complex models that require mathematical computations and custom algorithms. Get hands-on experience with languages including:

• SQL

• Python

• R (Optional)

Probability and statistics provide many mathematical tools to model data. In this module, you’ll learn to make sense of data and derive insights, helping you build a comprehensive data picture. Units include:

• Discrete and continuous distribution

• Distribution parameters

• Conditional probability

• Hypothesis testing

• A/B testing

Machine learning is evolving with new applications and greater innovation. In recent times, with advancements in computer vision, language models, and deep learning methods, AI is all around us.

This power-packed module covers the basics of supervised learning methods, particularly focusing on classification and regression methods. Units include:

• Overview of statistical learning

• Classification

• Resampling methods

• Tree-based methods

• Deep learning

• Survival analysis and censored data

• Unsupervised learning

While you have learned many concepts throughout the course, now it's time to work on a real problem that you can add to your portfolio. You'll follow the process below:

• Identify a problem

• Size the impact

• Develop your approach

• Deploy/Implement