Course

Python Data Products for Predictive Analytics

University of California San Diego

Python Data Products for Predictive Analytics empowers learners to take their Python skills to the next level by creating accurate predictions and deploying machine learning models. This four-course specialization from UC San Diego is designed for those proficient with Python basics.

Throughout the specialization, learners will develop statistical models, devise data-driven workflows, and gain insights from various data sources using design thinking methodology and data science techniques. The course is taught by prominent figures in the data science community, Professor Ilkay Altintas, Ph.D., and Julian McAuley.

  • Develop data strategy and process for generating, collecting, and consuming data
  • Understand the fundamental concepts of statistical learning and predictive modeling
  • Evaluate performance of regressors/classifiers and deploy machine learning models
  • Learn to create interactive data visualizations and compute basic statistics

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Python Data Products for Predictive Analytics
Course Modules

This four-course specialization, Python Data Products for Predictive Analytics, covers topics such as basic data processing and visualization, design thinking and predictive analytics for data products, meaningful predictive modeling, and deploying machine learning models.

Basic Data Processing and Visualization

Develop data strategy and process for how data will be generated, collected, and consumed.

  • Load and process formatted datasets such as CSV and JSON
  • Clean datasets by filtering and removing outliers
  • Gain basic experience with data processing libraries such as numpy and data ingestion with urllib, requests

Design Thinking and Predictive Analytics for Data Products

This course builds on the data processing covered in the first module and introduces the basics of designing predictive models in Python. Learners will understand statistical learning concepts and various methods of building predictive models, culminating in a capstone project.

Meaningful Predictive Modeling

Learners will understand error measures, evaluate performance of regressors/classifiers, and learn techniques to avoid overfitting and achieve good generalization performance.

Deploying Machine Learning Models

This module covers project structure of interactive Python data applications, Python web server frameworks, best practices for deploying ML models, and deployment scripts and serialization of models.

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