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Exploratory Data Analysis for Machine Learning

4.6 Filled star Filled star Filled star Filled star Half star
Based on 2,048 ratings
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Joseph Santarcangelo and Svitlana (Lana) Kramar

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

Read more

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.

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What's inside

Syllabus

A Brief History of Modern AI and its Applications
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.
Read more

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Introduces learners to the foundational concepts of Machine Learning, preparing them for further study in the IBM Machine Learning Professional Certificate
Provides hands-on experience with real-world data, equipping learners with practical skills for data analysis and Machine Learning
Focuses on data retrieval, cleaning, feature engineering, and hypothesis testing, building a solid foundation for data analysis
Designed for aspiring data scientists seeking to apply Machine Learning and Artificial Intelligence in business settings
Taught by experienced instructors, Joseph Santarcangelo and Svitlana (Lana) Kramar, known for their contributions to Machine Learning

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Reviews summary

Practical data preparation for machine learning

According to students, this course offers a highly practical and foundational approach to Exploratory Data Analysis for Machine Learning. Learners consistently praise its emphasis on hands-on labs and coding exercises, which effectively build skills in data cleaning, feature engineering, and preparing data for ML models. While many find the instructor's explanations clear and the course structure effective for aspiring data scientists, some reviewers with prior experience suggest the content, particularly in inferential statistics and hypothesis testing, can feel superficial or too basic. Overall, it's considered excellent for beginners seeking real-world data preparation skills.
Most learners find the instructor's explanations clear and easy to follow.
"The instructor did a decent job, and the concepts were explained clearly."
"The instructor explains everything in a very digestible manner."
"Perfect for beginners who want to understand EDA from a practical perspective. The instructor is very clear and guides you through complex topics."
Delivers essential skills in data retrieval, cleaning, and feature engineering.
"The emphasis on data quality and preparation is crucial and well-covered. This is a must-take for anyone serious about a career in ML."
"Getting data ready for ML is a huge hurdle, and this course equips you with the fundamental skills to overcome it."
"Phenomenal course! Covered data cleaning, feature engineering, and visualization techniques effectively. The hands-on parts are key."
Reinforces concepts through hands-on Python exercises and real-world scenarios.
"The hands-on labs were incredibly helpful, especially the feature engineering and data cleaning modules."
"The best part about this course is its practical approach. I loved how they integrated labs after each major concept."
"I found the optional 'HONORS project' a great opportunity to apply all the skills, although it could use a bit more guidance or example solutions."
The inferential statistics module could benefit from more detailed coverage.
"Some parts felt a bit rushed, particularly the inferential statistics and hypothesis testing section."
"I had to supplement with outside resources for a deeper understanding of stats."
"The section on inferential statistics was a bit brief for my liking, but overall, it taught me valuable skills for my data science journey."
Content may feel basic for experienced learners, but is solid for beginners.
"Honestly, I was quite disappointed. The course content felt too superficial given the title. It barely scratches the surface..."
"If you're an absolute beginner, it's probably fine, but for someone with some experience, it might feel slow."
"Waste of time if you're not an absolute beginner. The content is extremely basic and doesn't align with what an 'aspiring data scientist' needs."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Exploratory Data Analysis for Machine Learning with these activities:
Solve introductory Python exercises
Reinforce your Python skills by solving introductory exercises, solidifying your understanding of data types, operators, and control flow.
Browse courses on Python Basics
Show steps
  • Run simple Python programs to print values and perform basic operations.
  • Work through exercises involving variables, data types, and operators.
  • Practice using conditional statements and loops to control program flow.
Review Calculus textbook
Reviewing a calculus textbook will provide foundational knowledge necessary for understanding machine learning concepts.
View Algebra and Trigonometry on Amazon
Show steps
  • Read Chapter 1: Limits and Continuity
  • Practice solving problems from Section 1.1
  • Complete the practice quiz at the end of Section 1.2
Read Introduction to Machine Learning, 4th Edition
Supplement the course content with a detailed overview of Machine Learning to reinforce your understanding of the concepts
View Machine Learning, revised and updated edition on Amazon
Show steps
  • Read the book
  • Take notes
  • Complete the exercises
12 other activities
Expand to see all activities and additional details
Show all 15 activities
Review linear regression
Brush up on the fundamentals of linear regression to enhance your comprehension of supervised learning models.
Show steps
  • Revisit the concept of linear equations and their graphical representation.
  • Recall the formula for calculating the slope and intercept of a line.
  • Practice fitting a linear model to a dataset using a statistical software package.
Follow tutorials on Python for Data Science
Guided tutorials will provide hands-on practice with Python, a programming language commonly used in machine learning.
Browse courses on Python
Show steps
  • Sign up for a free account on Codecademy
  • Enroll in the 'Python for Data Science' course
  • Complete the first three modules
Complete Python coding exercises
Exercise your understanding of programming on a Python development environment to prepare yourself to jump into the course content
Browse courses on Python
Show steps
  • Find online Python coding exercises
  • Complete the exercises
  • Review the solutions
Create a machine learning project
Solidify your knowledge and skills by practicing your understanding of programming on a Python development environment, and in Calculus, Linear Algebra, Probability, and Statistics
Browse courses on Linear Algebra
Show steps
  • Identify a dataset and a business problem
  • Load, clean, and explore the data
  • Build a machine learning model
  • Evaluate the model's performance
  • Deploy the model
Join a study group to discuss machine learning concepts
Participating in a study group will provide opportunities to collaborate with others and deepen your understanding of machine learning.
Browse courses on Machine Learning
Show steps
  • Find a study group or create your own
  • Meet regularly to discuss course materials
  • Work together on assignments and projects
Practice data cleaning and feature engineering
Practice drills will reinforce data cleaning and feature engineering techniques, which are essential for preparing data for machine learning algorithms.
Browse courses on Data Cleaning
Show steps
  • Download the 'UCI Machine Learning Repository' dataset
  • Clean the dataset by removing duplicate rows and missing values
  • Create new features by combining existing features
Follow a Machine Learning tutorial
Expand your knowledge by diving into tutorials outside of the course to enhance your understanding of Machine Learning
Browse courses on Machine Learning
Show steps
  • Find a Machine Learning tutorial
  • Follow the tutorial
  • Apply what you learned
Build a machine learning model to predict customer churn
Building a machine learning model will provide practical experience with the entire machine learning pipeline.
Browse courses on Machine Learning
Show steps
  • Collect customer data from your CRM system
  • Clean and prepare the data
  • Train a machine learning model to predict customer churn
  • Evaluate the performance of the model
Create a presentation on the applications of machine learning
Creating a presentation will help you synthesize your understanding of the applications of machine learning and communicate it effectively.
Browse courses on Machine Learning
Show steps
  • Research different applications of machine learning
  • Develop a presentation outline
  • Create slides that are visually appealing and informative
  • Practice presenting your slides
Write a blog post about Machine Learning
Solidify your understanding of Machine Learning and enhance your communication skills by writing a blog post summarizing your key takeaways from the course
Browse courses on Machine Learning
Show steps
  • Research the topic
  • Write the blog post
  • Choose a topic
  • Publish the blog post
Join a study group
Enhance your understanding of the course material, share knowledge, and receive support by participating in a study group
Browse courses on Machine Learning
Show steps
  • Find a study group
  • Attend study group meetings
  • Participate in discussions
Participate in a Machine Learning competition
Challenge yourself, apply your skills, and potentially win prizes by participating in a Machine Learning competition related to the course
Browse courses on Machine Learning
Show steps
  • Find a Machine Learning competition
  • Register for the competition
  • Build a model
  • Submit your model

Career center

Learners who complete Exploratory Data Analysis for Machine Learning will develop knowledge and skills that may be useful to these careers:
Artificial Intelligence Engineer
Artificial intelligence engineers design, develop, and maintain artificial intelligence systems. This course helps build a foundation for building artificial intelligence systems by teaching you how to work with data, clean it, feature engineer it, and have it ready for preliminary analysis and hypothesis testing.
See salaries and explore the career path for Artificial Intelligence Engineer
Machine Learning Engineer
Machine learning engineers build and maintain the machine learning models that power artificial intelligence applications. This course helps build a foundation for building machine learning models by teaching you how to work with data, clean it, feature engineer it, and have it ready for preliminary analysis and hypothesis testing.
See salaries and explore the career path for Machine Learning Engineer
Statistician
Statisticians collect, analyze, interpret, and present data. The Exploratory Data Analysis for Machine Learning course may be useful for aspiring statisticians who want to learn how to use data to solve problems.
See salaries and explore the career path for Statistician
Data Engineer
Data engineers design, build, and maintain the infrastructure that stores and processes data. The Exploratory Data Analysis for Machine Learning course may be useful for aspiring data engineers who want to learn how to clean and prepare data for analysis.
See salaries and explore the career path for Data Engineer
Data Analyst
Data analysts collect, analyze, interpret, and present data. The Exploratory Data Analysis for Machine Learning course may be useful for aspiring data analysts who want to learn how to retrieve data, clean it, and explore it for insights.
See salaries and explore the career path for Data Analyst
Software Developer
Software developers design, develop, and maintain software applications. The Exploratory Data Analysis for Machine Learning course may be useful for software developers who want to learn how to use data to improve the software they develop.
See salaries and explore the career path for Software Developer
Business Analyst
Business analysts help businesses understand their data and make better decisions. This course may be useful for aspiring business analysts who want to learn how to use data to solve business problems.
See salaries and explore the career path for Business Analyst
Data Architect
Data architects design and build the systems that store and process data. The Exploratory Data Analysis for Machine Learning course may be useful for aspiring data architects who want to learn how to design and build systems that can handle large amounts of data.
See salaries and explore the career path for Data Architect
Data Scientist
A data scientist analyzes data using scientific methods to draw conclusions from it and communicate the results in a way that supports decision-making. The Exploratory Data Analysis for Machine Learning course may be useful for aspiring data scientists who want to learn how to delve into data and model it, a foundational skill for data scientists.
See salaries and explore the career path for Data Scientist
Operations Research Analyst
Operations research analysts use mathematical and analytical techniques to solve business problems. The Exploratory Data Analysis for Machine Learning course may be useful for aspiring operations analysts who want to learn how to use data to improve business processes.
See salaries and explore the career path for Operations Research Analyst
Database Administrator
Database administrators maintain the databases that store data. The Exploratory Data Analysis for Machine Learning course may be useful for aspiring database administrators who want to learn how to manage and maintain databases.
See salaries and explore the career path for Database Administrator
Financial Analyst
Financial analysts use data to make investment recommendations. The Exploratory Data Analysis for Machine Learning course may be useful for aspiring financial analysts who want to learn how to use data to make better investment decisions.
See salaries and explore the career path for Financial Analyst
Software Engineer
Software engineers design, develop, and maintain software applications. The Exploratory Data Analysis for Machine Learning course may be useful for software engineers who want to learn how to use data to improve the software they develop.
See salaries and explore the career path for Software Engineer
Market Researcher
Market researchers collect, analyze, and interpret data to understand customer needs and preferences. The Exploratory Data Analysis for Machine Learning course may be useful for aspiring market researchers who want to learn how to use data to make better marketing decisions.
See salaries and explore the career path for Market Researcher
Product Manager
Product managers develop and manage products. The Exploratory Data Analysis for Machine Learning course may be useful for aspiring product managers who want to learn how to use data to make better decisions about product development.
See salaries and explore the career path for Product Manager

Reading list

We've selected nine books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Exploratory Data Analysis for Machine Learning.
Cover image
Tidy Modeling with R
Save
Provides a comprehensive overview of the process of predictive modeling, from data preparation to model evaluation. It valuable resource for anyone who wants to learn about the fundamentals of predictive modeling or who wants to improve their skills in this area.
Tidy Modeling with R: A Framework for Modeling in...
Paperback
$$
Tidy Modeling with R
Kindle Edition
$$
Cover image
The Elements of Statistical Learning
Save
Classic textbook on statistical learning. It covers a wide range of topics, from linear regression to support vector machines. It valuable resource for anyone who wants to learn about the theoretical foundations of statistical learning.
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Kindle Edition
$$$
Cover image
Hands-On Machine Learning with Scikit-Learn, Keras,...
Save
Practical guide to machine learning using Python. It covers a wide range of topics, from data preparation to model evaluation. It valuable resource for anyone who wants to learn about the practical aspects of machine learning using Python.
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Kindle Edition
$$
Cover image
Machine Learning with R
Save
Comprehensive overview of machine learning using R. It covers a wide range of topics, from data preparation to model evaluation. It valuable resource for anyone who wants to learn about the practical aspects of machine learning using R.
Machine Learning with R - Fourth Edition: Learn...
Paperback
$$
Machine Learning with R - Second Edition: Expert...
Kindle Edition
$$
Machine Learning with R - Third Edition: Expert...
Paperback
$$
(Italiano) Machine Learning con R: Conoscere le tecniche per...
Kindle Edition
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R: Unleash Machine Learning Techniques
Kindle Edition
$$$
Machine Learning with R: Learn How to Use R to...
Paperback
$$
(中文) Machine learning and R language (3rd edition of the...
Paperback
$$
Cover image
Data Mining
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Comprehensive overview of data mining. It covers a wide range of topics, from data preparation to data visualization. It valuable resource for anyone who wants to learn about the foundations of data mining.
Data Mining: Concepts and Techniques (The Morgan...
Paperback
$$$
Data Mining: Concepts and Techniques (The Morgan...
Kindle Edition
$$$
Cover image
Statistical and Machine-Learning Data Mining:
Save
Comprehensive overview of statistical methods for machine learning. It covers a wide range of topics, from probability to Bayesian statistics. It valuable resource for anyone who wants to learn about the theoretical foundations of machine learning.
Statistical and Machine-Learning Data Mining::...
Kindle Edition
$$
Statistical and Machine-Learning Data Mining:...
Hardcover
$$$
(中文) Statistical Mining and Machine Learning: Big Data...
Paperback
$$$
Cover image
Machine Learning in Action
Save
Practical guide to machine learning. It covers a wide range of topics, from data preparation to model evaluation. It valuable resource for anyone who wants to learn about the practical aspects of machine learning.
Machine Learning in Action
Paperback
$$
(中文) 机器学习实战 (图灵程序设计丛书 72) (Chinese Edition)
Kindle Edition
$
Cover image
The Molecular Basis of Cancer
Save
Practical guide to machine learning. It covers a wide range of topics, from data preparation to model evaluation. It valuable resource for anyone who wants to learn about the practical aspects of machine learning.
The Molecular Basis of Cancer
Hardcover
$$$$
The Molecular Basis of Cancer: Expert Consult -...
Hardcover
$$$$
Molecular Basis of Cancer
Hardcover
$$$
The Molecular Basis of Cancer
Kindle Edition
$$$$
Cover image
Deep Learning
Save
Comprehensive overview of deep learning. It covers a wide range of topics, from the basics of deep learning to the latest advances in the field. It valuable resource for anyone who wants to learn about the theoretical foundations of deep learning.
Deep Learning
Paperback
$$$
Deep Learning (Adaptive Computation and Machine...
Hardcover
$$$
Deep Learning (Adaptive Computation and Machine...
Kindle Edition
$$$

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Course image
Rating
4.6 Filled star Filled star Filled star Filled star Half star
Effort
Approx. 14 hours
Level
Intermediate
Via
Coursera
Institution
IBM
Instructors
Joseph Santarcangelo
Svitlana (Lana) Kramar
Language
English
Transcripts
Español Français Português Deutsch Italiano 中文 العربية 한국어 日本語 हिंदी Nederlands Svenska Pусский Türkçe Polski Ελληνικά українська мова magyar nyelv Bahasa Indonesia ไทย қазақша اُردُو, فارسی বাংলা پښتو آذربایجان دیلی
This course belongs to a collection called IBM Introduction to Machine Learning. It's comprised of four courses:
  1. Supervised Machine Learning: Regression
  2. Exploratory Data Analysis for Machine Learning (viewing)
  3. Supervised Machine Learning: Classification
  4. Unsupervised Machine Learning
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Introduces learners to the foundational concepts of Machine Learning, preparing them for further study in the IBM Machine Learning Professional Certificate
Provides hands-on experience with real-world data, equipping learners with practical skills for data analysis and Machine Learning
Focuses on data retrieval, cleaning, feature engineering, and hypothesis testing, building a solid foundation for data analysis
Designed for aspiring data scientists seeking to apply Machine Learning and Artificial Intelligence in business settings
Taught by experienced instructors, Joseph Santarcangelo and Svitlana (Lana) Kramar, known for their contributions to Machine Learning
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