Every successful machine learning project starts with one essential skill: preparing the data. In this Specialization, you’ll build the practical foundation behind real data science and AI work—cleaning messy datasets, transforming raw information into usable features, checking data quality, and getting data ready for predictive modeling.
You’ll work on the kinds of tasks data professionals do every day, including combining datasets, handling missing and inconsistent values, diagnosing data quality issues, preparing training and test sets, and building supervised machine learning models for classification, regression, forecasting, and tabular prediction problems. These are the skills that help you move from “working with data” to contributing to higher-impact analytics, machine learning, and AI projects.
Unlike a traditional course sequence, this skill path is organized around real workplace tasks and career-relevant skills. You can check what you already know, focus on the areas that matter most for your goals, and learn through curated lessons selected from expert instructors across the platform. Whether you’re preparing for a data analyst, analytics engineer, junior data scientist, machine learning analyst, or AI practitioner role, this path helps you build the hands-on confidence to prepare reliable data and apply machine learning in practical ways.
Applied Learning Project
You’ll complete authentic, job-inspired projects that mirror real responsibilities in data and machine learning roles, from cleaning and joining datasets to investigating quality issues and preparing model-ready features.
By the end, you’ll have practical examples that show how you can turn raw data into trusted inputs for analysis and machine learning—work you can translate into portfolio stories, interview examples, and career conversations.
