Lesson 1: Addressing Business Issues with Data ScienceTopic A: Initiate a Data Science ProjectTopic B: Formulate a Data Science Problem
Topic A: Initiate a Data Science Project
Topic B: Formulate a Data Science Problem
Lesson 2: Extracting, Transforming, and Loading DataTopic A: Extract DataTopic B: Transform Data Topic C: Load Data
Topic A: Extract Data
Topic B: Transform Data Topic C: Load Data
Lesson 3: Analyzing DataTopic A: Examine DataTopic B: Explore the Underlying Distribution of DataTopic C: Use Visualizations to Analyze DataTopic D: Preprocess Data
Topic A: Examine Data
Topic B: Explore the Underlying Distribution of Data
Topic C: Use Visualizations to Analyze Data
Topic D: Preprocess Data
Lesson 4: Designing a Machine Learning ApproachTopic A: Identify Machine Learning ConceptsTopic B: Test a Hypothesis
Topic A: Identify Machine Learning Concepts
Topic B: Test a Hypothesis
Lesson 5: Developing Classification ModelsTopic A: Train and Tune Classification ModelsTopic B: Evaluate Classification Models
Topic A: Train and Tune Classification Models
Topic B: Evaluate Classification Models
Lesson 6: Developing Regression ModelsTopic A: Train and Tune Regression ModelsTopic B: Evaluate Regression Models
Topic A: Train and Tune Regression Models
Topic B: Evaluate Regression Models
Lesson 7: Developing Clustering ModelsTopic A: Train and Tune Clustering ModelsTopic B: Evaluate Clustering Models
Topic A: Train and Tune Clustering Models
Topic B: Evaluate Clustering Models
Lesson 8: Finalizing a Data Science ProjectTopic A: Communicate Results to StakeholdersTopic B: Demonstrate Models in a Web AppTopic C: Implement and Test Production Pipelines
Topic A: Communicate Results to Stakeholders
Topic B: Demonstrate Models in a Web App
Topic C: Implement and Test Production Pipelines

Learner Outcomes
In this course, you will implement data science techniques in order to achieve organizational goals. You will:
Use data science principles to address business issues
Apply the extract, transform, and load (ETL) process to prepare datasets
Use multiple techniques to analyze data and extract valuable insights
Design a machine learning approach to address business issues
Train, tune, and evaluate classification models
Train, tune, and evaluate regression and forecasting models
Train, tune, and evaluate clustering models
Finalize a data science project by presenting models to an audience, putting models into production, and monitoring model performance

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