Overview

Google, IBM & Meta Certificates – 40% Off

One plan covers every Professional Certificate on Coursera.

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.

Syllabus

Start Here: Get Oriented and Check Your Skills

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.

Job Task 1: Assemble an analysis-ready table

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.

Job Task 2: Engineer clean numeric features

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.

Job Task 3: Prepare a modeling-ready feature set

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.

Wrap Up: Review Your Skill Achievement and Choose Your Next Path

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.