Exploratory Data Analysis for Machine Learning

Exploratory Data Analysis for Machine Learning

This course is part of multiple programs.

Instructors: Joseph Santarcangelo

Access provided by Taipei Medical University & IOOOI Ally

186,925 already enrolled

5 modules

Gain insight into a topic and learn the fundamentals.

2,546 reviews

Intermediate level

Some related experience required

Flexible schedule

1 week at 10 hours a week

Learn at your own pace

95%

Most learners liked this course

5 modules

Gain insight into a topic and learn the fundamentals.

2,546 reviews

Intermediate level

Some related experience required

Flexible schedule

1 week at 10 hours a week

Learn at your own pace

95%

Most learners liked this course

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

12 assignments¹

AI Graded see disclaimer

Taught in English

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

Build your subject-matter expertise

This course is available as part of

When you enroll in this course, you'll also be asked to select a specific program.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 5 modules in this course

This first course in the IBM Machine Learning Professional Certificate introduces you to Machine Learning and the content of the professional certificate. In this course you will realize the importance of good, quality data. You will learn common techniques to retrieve your data, clean it, apply feature engineering, and have it ready for preliminary analysis and hypothesis testing.

By the end of this course you should be able to: Retrieve data from multiple data sources: SQL, NoSQL databases, APIs, Cloud Describe and use common feature selection and feature engineering techniques Handle categorical and ordinal features, as well as missing values Use a variety of techniques for detecting and dealing with outliers Articulate why feature scaling is important and use a variety of scaling techniques Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Machine Learning and Artificial Intelligence in a business setting. What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Calculus, Linear Algebra, Probability, and Statistics.

Artificial Intelligence is not new, but it is new in a sense that it is easier than ever to get started using Machine Learning in business settings. In this module, we will go over a quick introduction to AI and Machine Learning and we will visit a brief history of the modern AI. We will also explore some of the current applications of AI and Machine Learning for you, to think about how you want to leverage them in your day to day business practice or personal projects.

What's included

8 videos2 readings3 assignments1 discussion prompt

8 videos Total 47 minutes

Course Introduction 2 minutes
Introduction to Artificial Intelligence and Machine Learning 6 minutes
Machine Learning and Deep Learning 10 minutes
History of AI 8 minutes
History of Machine Learning and Deep Learning 5 minutes
Modern AI 7 minutes
Applications 3 minutes
Machine Learning Workflow 7 minutes

2 readings Total 7 minutes

Course Prerequisites 4 minutes
Review 3 minutes

3 assignments Total 40 minutes

Practice Quiz: Artificial Intelligence and Machine Learning 5 minutes
Practice Quiz: Modern AI Applications and Workflows 5 minutes
Graded Quiz: Module 1 - Modern AI and its Applications 30 minutes

1 discussion prompt Total 10 minutes

Optional: Say hi or reach out for help 10 minutes

Good data is the fuel that powers Machine Learning and Artificial Intelligence. In this module, you will learn how to retrieve data from different sources, how to clean it to ensure its quality.

What's included

7 videos3 readings3 assignments3 app items

7 videos Total 43 minutes

Retrieving Data from CSV and JSON Files 5 minutes
Retrieving Data from Databases, APIs, and the Cloud 9 minutes
[Optional] Lab Solution: Reading Data Jupyter Notebook - Part A 7 minutes
[Optional]Lab Solution: Reading in Database Files - Part B 5 minutes
Data Cleaning 6 minutes
Handling Missing Values and Outliers 7 minutes
Handling Missing Values and Outliers using Residuals 4 minutes

3 readings Total 7 minutes

[Optional] Download Assets for Lab: Reading Data in Database Files - Part A 2 minutes
[Optional] Download Assets for Lab: Reading Data in Jupyter Notebook - Part B 2 minutes
Summary/Review 3 minutes

3 assignments Total 44 minutes

Practice Quiz: Retrieving Data 9 minutes
Practice Quiz: Data Cleaning 5 minutes
Graded Quiz: Module 2 - Retrieving Data and Cleaning Data 30 minutes

3 app items Total 85 minutes

Demo Lab: Reading Data in Database Files - Part A 20 minutes
Demo Lab: Reading Data in Jupyter Notebook - Part B 20 minutes
Practice Lab: Data Cleaning 45 minutes

In this module you will learn how to conduct exploratory analysis to visually confirm it is ready for machine learning modeling by feature engineering and transformations.

What's included

15 videos3 readings3 assignments4 app items

15 videos Total 104 minutes

Introduction to Exploratory Data Analysis (EDA) 6 minutes
EDA with Visualization 6 minutes
Grouping Data for EDA 6 minutes
[Optional]Solution: EDA Notebook - Part 1 6 minutes
[Optional]Solution: EDA Notebook - Part 2 11 minutes
[Optional]Solution: EDA Notebook - Part 3 7 minutes
[Optional]Solution: EDA Notebook - Part 4 8 minutes
Feature Engineering and Variable Transformation - Background 3 minutes
Variable Transformation 5 minutes
Feature Encoding 4 minutes
Feature Scaling 5 minutes
Common Variable Transformations in Python 3 minutes
[Optional] Solution: Feature Engineering Lab - Part 1 12 minutes
[Optional] Solution: Feature Engineering Lab - Part 2 13 minutes
[Optional] Solution: Feature Engineering Lab - Part 3 10 minutes

3 readings Total 14 minutes

[Optional] Download Assets for Lab: Exploratory Data Analysis Lab 2 minutes
[Optional] Download Assets for Lab: Feature Engineering Demo 2 minutes
Summary/Review 10 minutes

3 assignments Total 40 minutes

Practice Quiz: Exploratory Data Analysis 5 minutes
Practice Quiz: Feature Engineering and Variable Transformation 5 minutes
Graded Quiz: Module 3 - Exploratory Data Analysis and Feature Engineering 30 minutes

4 app items Total 135 minutes

Demo Lab: Exploratory Data Analysis 30 minutes
Practice Lab: Exploratory Data Analysis 30 minutes
Demo Lab: Feature Engineering 30 minutes
Practice Lab: Feature Engineering 45 minutes

Inferential statistics and hypothesis testing are two types of data analysis often overlooked at early stages of analyzing your data. They can give you quick insights about the quality of your data. They also help you confirm business intuition and help you prescribe what to analyze next using Machine Learning. This module looks at useful definitions and simple examples that will help you get started creating hypothesis around your business problem and how to test them.

What's included

15 videos2 readings3 assignments2 app items1 discussion prompt

15 videos Total 83 minutes

Estimation and Inference - Introduction 6 minutes
Estimation and Inference - Example 5 minutes
Estimation and Inference - Parametric vs. Non-Parametric 5 minutes
Estimation and Inference - Commonly Used Distributions 5 minutes
Frequentist vs. Bayesian Statistics 6 minutes
Introduction to Hypothesis 2 minutes
Hypothesis Testing Example 2 minutes
Bayesian Interpretation of Hypothesis Testing Example 4 minutes
Type 1 vs Type 2 Error 5 minutes
Hypothesis Testing Terminology 4 minutes
Significance Level and P-Values 7 minutes
Significance Level and P-Values and the F Statistic 5 minutes
[Optional] Hypothesis Testing Demo - Part 1 6 minutes
[Optional] Hypothesis Testing Demo - Part 2 11 minutes
Correlation vs Causation 9 minutes

2 readings Total 5 minutes

[Optional] Download Assets for Lab: Hypothesis Testing Demo 2 minutes
Summary/Review 3 minutes

3 assignments Total 40 minutes

Practice Quiz: Estimation and Inference, and Hypothesis Testing 5 minutes
Practice Quiz: Hypothesis Testing 5 minutes
Graded Quiz: Module 4 - Inferential Statistics and Hypothesis Testing 30 minutes

2 app items Total 50 minutes

Demo Lab: Hypothesis Testing 20 minutes
Practice Lab: Hypothesis Testing 30 minutes

1 discussion prompt Total 10 minutes

Optional Brainstorming 10 minutes

In this assignment, you will apply the skills learned throughout the course to analyze a dataset of your choice, either from the course materials or an external source. You will perform data cleaning, feature engineering, exploratory data visualization, and hypothesis testing to derive meaningful insights. Upon completion, your work will be evaluated automatically by an AI grading tool.

What's included

4 readings1 app item

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructors

Instructor ratings

(795 ratings)

Joseph Santarcangelo

IBM

37 Courses 2,392,269 learners

Svitlana (Lana) Kramar

IBM

3 Courses 205,694 learners

Offered by

IBM

Why people choose Coursera for their career

Felipe M.

Learner since 2018

"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."

Jennifer J.

Learner since 2020

"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."

Larry W.

Learner since 2021

"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."

Chaitanya A.

"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

Learner reviews

5 stars
72.99%
4 stars
19.19%
3 stars
4.43%
2 stars
1.84%
1 star
1.53%

Showing 3 of 2546

Reviewed on Nov 4, 2022

Good introduction to the workflow in EDA for ML. I appreciate the code examples that provide a useful reference to code syntax and some practice with EDA.

Reviewed on Apr 23, 2024

The course includes hands-on exercises that allows us to apply the learned EDA techniques to real-world data. This practical approach helps solidify my understanding.

Reviewed on Feb 25, 2023

This course was amazing. I always assumed that EDA was the challenging part of ML, But in this course I found it so cool. can't wait for the next course.

View more reviews