Data Cleaning, Transformation, and Manipulation

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Data Cleaning, Transformation, and Manipulation

This course is part of multiple programs.

Instructor: Professionals from the Industry

Included with Learn more

5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Assemble analysis-ready tables with SQL joins and Python filtering.
Engineer clean numeric features with transforms, scaling, and standardization.
Prepare modeling-ready datasets: impute, encode, scale, and select features.

Skills you'll gain

Tools you'll learn

Pandas (Python Package)

Details to know

Shareable certificate

Add to your LinkedIn profile

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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

In Data Cleaning, Transformation, and Manipulation, you’ll learn to turn messy data into analysis- and modeling-ready datasets using Python (pandas) and SQL. This is a skill-based path organized around real workplace tasks. Each module mirrors responsibilities you see in job descriptions and focuses on the exact steps you’ll perform on the job.

You’ll begin with a quick skills check, then personalize your journey: double down on new topics, or skip what you already know. For each skill, you’ll review concise lessons curated from expert instructors with explanations and demos for filtering and subsetting, joins and merges, feature engineering, normalization, encoding, imputation, scaling, and feature selection. Then you will prove your skills in job-task assessments. By the end, you can assemble analysis-ready tables, engineer clean numeric features, and prepare a modeling-ready feature set for predictive modeling. These capabilities support roles like Data Analyst, Analytics Engineer, Business Intelligence Analyst, Data Scientist, or Machine Learning Engineer and help you handle everyday tasks such as combining datasets, cleaning and transforming columns, and delivering ready-to-train features.

Start here to learn how this skill-based course works and find your recommended starting point. You’ll take a short, ungraded diagnostic to check your current skills, then decide whether to go directly to the graded skill assessments or review targeted learning content first.

What's included

1 reading4 assignments

Use this module to build the skills for the job task Assemble an Analysis-Ready Table. You'll learn how to apply filtering and subsetting techniques to isolate the records you need in Python, and how to apply merging and joining operations in SQL to combine multiple datasets into a single analysis-ready table. Review the lessons that match the skills you want to strengthen before completing the related graded assessment.

What's included

3 videos1 reading3 assignments2 ungraded labs

3 videosTotal 19 minutes

Introduction to Pandas 4 minutes
Data Frames and Series 7 minutes
Selecting and Filtering 8 minutes

1 readingTotal 10 minutes

Recap10 minutes

3 assignmentsTotal 80 minutes

Applying Python for Data Analysis20 minutes
Knowledge Check: SQL Fundamentals Review30 minutes
Practice Your Skills: Assemble an analysis-ready table30 minutes

2 ungraded labsTotal 120 minutes

Ungraded Lab: Joins Practice Lab60 minutes
Ungraded Lab: Optimized SQL Join Generation Lab60 minutes

Use this module to build the skills for the job task Engineer Clean Numeric Features. You'll learn how to apply data transformation operations to derive new columns from existing data and how to apply normalization techniques such as min-max scaling and z-score standardization to put numeric features on a comparable scale for machine learning. Review the lessons that match the skills you want to strengthen before completing the related graded assessment.

What's included

6 videos1 reading3 assignments1 ungraded lab

6 videosTotal 26 minutes

Handling Missing Data 5 minutes
Data Cleaning and Preparation 6 minutes
Data Manipulation 5 minutes
Why Feature Scaling Matters in Machine Learning5 minutes
Scaling Your Data: Normalization with Min-Max Scaler3 minutes
Standardization with Z-Score Scaling + Impact on Model Performance3 minutes

1 readingTotal 6 minutes

What Is Feature Scaling and Why It Matters in Machine Learning6 minutes

3 assignmentsTotal 75 minutes

Your Turn! Practice Assignment 30 minutes
Knowledge Check: Feature Scaling Key Concepts15 minutes
Practice Your Skills: Engineer clean numeric features30 minutes

1 ungraded labTotal 60 minutes

Scaling Features for a Loan Approval Model60 minutes

Use this module to build the skills for the job task Prepare a Modeling-Ready Feature Set. You'll learn how to develop and prepare a complete feature set for predictive modeling, including handling missing data, encoding categorical variables, scaling numeric features, and selecting or reducing features so that the final dataset is ready to train a model. Review the lessons that match the skills you want to strengthen before completing the related graded assessment.

What's included

8 videos3 readings2 assignments3 ungraded labs

8 videosTotal 36 minutes

Why Data Preprocessing & Feature Engineering Matter So Much3 minutes
Why Missing Data Breaks Models: The Problem in Action4 minutes
How Missing Data Affects Model Accuracy — and What to Do About It5 minutes
Why ML Models Can't Handle Raw Categorical Data5 minutes
Types of Categorical Variables and How to Encode Them3 minutes
Label Encoding and Model Performance Comparison5 minutes
Why Feature Scaling Matters in Machine Learning5 minutes
Applying Feature Selection & PCA in Python5 minutes

3 readingsTotal 28 minutes

How to Handle Missing Data in ML Pipelines8 minutes
Why We Encode Categorical Data in Machine Learning10 minutes
Why and How We Select the Right Features10 minutes

2 assignmentsTotal 60 minutes

Data Preprocessing & Feature Engineering Mastery30 minutes
Practice Your Skills: Prepare a modeling-ready feature set30 minutes

3 ungraded labsTotal 180 minutes

Cleaning a Customer Purchase Dataset60 minutes
Transforming Categorical Data for a Salary Prediction Model60 minutes
Reducing Features for a House Price Prediction Model60 minutes

Review the skills you practiced and demonstrated in this course, then prepare to describe them in career-relevant ways. You’ll also explore recommended skill paths that can help you continue building related job-ready skills.

What's included

2 readings

Earn a career certificate

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

Instructor

Professionals from the Industry

513 Courses115,399 learners

Offered by

Coursera

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Frequently asked questions

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