Overview

Google, IBM & Meta Certificates – 40% Off

One plan covers every Professional Certificate on Coursera.

Build the data preparation skills you need to turn raw, messy data into clean, model-ready datasets. In this course, you’ll develop practical experience used in roles such as data analyst, junior data scientist, machine learning analyst, business analyst, and analytics engineer. You’ll work through the process of ingesting data from files, databases, and APIs, auditing data quality, performing exploratory data analysis, and creating visualizations that help you understand what the data needs before modeling begins. This is a non-traditional, skill-based learning experience organized around real workplace tasks instead of a fixed lecture sequence. It’s designed to reflect responsibilities you may see in job descriptions, from combining data from multiple sources and diagnosing data quality issues to preparing training, validation, and test datasets for machine learning workflows. You can personalize your path based on what you already know, focus on the skills you need most, and skip content when it’s not necessary. The course curates high-quality lessons from expert instructors, selecting the strongest content for each skill so you can build practical, career-relevant data preparation experience. By the end, you’ll be able to ingest and assess raw data, clean missing and inconsistent values, detect and treat outliers, engineer meaningful features, and prepare properly split, scaled, normalized, and encoded datasets for analysis and modeling. This course is a strong fit if you already have basic experience with data analysis, spreadsheets, SQL, Python, or introductory machine learning concepts.

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: Ingest and Audit Raw Data for Modeling Readiness

Use this module to build the skills for the job task Ingest and Audit Raw Data for Modeling Readiness. You’ll learn how to load and combine structured data from multiple file formats, examine dataset structure, and use summary statistics and visualizations to understand key patterns and potential issues. Review the lessons that match the skills you want to strengthen before completing the related graded assessment.

Job Task 2: Prepare a Clean Analysis-Ready Dataset

Use this module to build the skills for the job task Prepare a Clean Analysis-Ready Dataset. You’ll learn how to identify and address common data quality issues, handle missing numeric values with appropriate techniques, and detect and treat outliers using sound statistical methods. Review the lessons that match the skills you want to strengthen before completing the related graded assessment.

Job Task 3: Engineer Features and Build a Model-Ready Dataset

Use this module to build the skills for the job task Engineer Features and Build a Model-Ready Dataset. You’ll learn how to transform existing variables into meaningful new features, apply preprocessing techniques such as encoding and scaling when appropriate, and create training, validation, and test splits with proper data isolation. 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.