Data Preparation & Applied Machine Learning

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.

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.

Build practical data quality and monitoring skills that help you assess source systems, prepare data for ingestion, and diagnose quality issues across pipelines. In this course, you’ll develop hands-on experience used in roles such as data analyst, analytics engineer, data engineer, business intelligence analyst, and data quality analyst. You’ll work on evaluating source data for availability, structure, and quality, then apply parsing and transformation logic to harmonize data from different formats so it can move more reliably into downstream workflows. 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 reviewing source readiness and preparing ingestion logic to investigating pipeline failures and recommending remediation steps. 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 quality experience. By the end, you’ll be able to assess source systems for ingestion readiness, implement parsing and transformation logic for structured data ingestion, analyze data profiles and pipeline outputs to identify quality issues, and perform root cause analysis by tracing data lineage to diagnose and explain failures. This course is a strong fit if you already have basic experience with data workflows, SQL, ETL concepts, or working with structured datasets.