Overview

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This course teaches you how to transform real-world datasets into reliable analytical assets through practical, reproducible data-cleaning techniques. You’ll learn how to evaluate categorical features and select optimal encoding strategies, measure and document data quality, and apply effective approaches to handle missing values. Using Python and pandas, you'll practice assessing cardinality, implementing target encoding, validating completeness with Great Expectations, and building transparent transformation lineage. You’ll also clean messy fields such as ages, salary outliers, and dates to ensure consistent model-ready outputs. Designed for analysts, data engineers, and ML practitioners, this course equips you with the job-ready skills needed to prepare high-quality datasets that support trustworthy insights and predictive modeling.

Syllabus

Transform Data: Cleanse, Encode, Validate: Choose the Right Encoding: From Cardinality to Model Fit

You will analyze categorical features to determine the optimal encoding strategy based on cardinality and model fit considerations.

Transform Data: Cleanse, Encode, Validate: Data Quality Metrics and Lineage Documentation

You will evaluate data quality metrics and document data transformation lineage to ensure transparency and reliability.

Transform Data: Cleanse, Encode, Validate: Handle Missing Data with Confidence: Impute, Flag, and Validate

You will apply techniques to impute, flag, and validate missing or null values to produce consistent, model-ready datasets.

Orchestrate, Analyze, and Evaluate ML Pipelines: Building ETL and ELT Pipelines for Feature Stores

You will apply ETL and ELT pipelines to ingest data from various sources into a feature store using structured transformation workflows.

Orchestrate, Analyze, and Evaluate ML Pipelines: Managing Schema Changes for Pipeline Resilience

You will analyze upstream schema changes and implement safeguards to maintain data pipeline resilience and downstream compatibility.

Orchestrate, Analyze, and Evaluate ML Pipelines: Evaluating Pipeline Health Against SLAs

You will evaluate data freshness, lag, and pipeline success rates against service level agreements to assess operational reliability.

Optimize ML Dev: Version, Reproduce, and Save: Version ML Workflows with Confidence

You will apply version control branching strategies to manage code, experiments, and project artifacts effectively.

Optimize ML Dev: Version, Reproduce, and Save: Build Reproducible ML Environments

You will apply virtual environment tools to configure reproducible project environments with stable dependencies.

Optimize ML Dev: Version, Reproduce, and Save: Optimize Compute Costs in ML Experiments

You will analyze resource utilization across CPU, GPU, and memory usage to optimize compute costs during experimentation.

Project: Build a Production-Ready ML Data Pipeline

In this project, you will design and implement a production-style machine learning data pipeline for a financial services risk modeling scenario. The raw dataset contains missing values, inconsistent categorical entries, potential outliers, and simulated schema drift. Your task is to transform this dataset into a validated, model-ready feature store. You will clean and preprocess structured tabular data, select encoding strategies based on feature cardinality, implement data validation using Great Expectations, detect schema changes between pipeline runs, generate SLA metrics to assess reliability, and save processed features in parquet format. Beyond the core pipeline, you will also apply professional development practices that are standard in production ML teams: setting up a virtual environment for reproducibility, using version control branching strategies to manage your work, and analyzing resource utilization to understand compute costs. Your final deliverable is a modular Python script and a structured written engineering explanation that demonstrates your ability to design reliable, production-aligned ML data infrastructure.