About this Course

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 21 hours to complete

Suggested: 9 hours/week...

English

Subtitles: English

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 21 hours to complete

Suggested: 9 hours/week...

English

Subtitles: English

Syllabus - What you will learn from this course

Week
1
2 hours to complete

Week 1: Supervised Learning & Regression

For this first week, we will go over the syllabus, download all course materials, and get your system up and running for the course. We will also introduce the basics of supervised learning and regression....
5 videos (Total 46 min), 4 readings, 3 quizzes
5 videos
Supervised Learning: Regression9m
Regression in Python10m
Time-Series Regression8m
Autoregression6m
4 readings
Syllabus10m
Course Materials10m
Set Up Your System10m
Recap: Mathematical Notation10m
3 practice exercises
Review: Supervised Learning4m
Review: Regression4m
Supervised Learning & Regression10m
Week
2
1 hour to complete

Week 2: Features

This week, we will learn what features are in a dataset and how we can work with them through cleaning, manipulation, and analysis in Jupyter notebooks....
4 videos (Total 29 min), 3 quizzes
4 videos
Features from Temporal Data8m
Feature Transformations4m
Missing Values7m
3 practice exercises
Review: Getting Features
Review: Working with Features
Features10m
Week
3
1 hour to complete

Week 3: Classification

This week, we will learn about classification and several ways you can implement it, such as K-nearest neighbors, logistic regression, and support vector machines....
4 videos (Total 31 min), 3 quizzes
4 videos
Classification: Nearest Neighbors4m
Classification: Logistic Regression10m
Introduction to Support Vector Machines10m
3 practice exercises
Review: Classification and K-Nearest Neighbors6m
Review: Logistic Regression and Support Vector Machines5m
Classification10m
Week
4
1 hour to complete

Week 4: Gradients

This week, we will learn the importance of properly training and testing a model. We will also implement gradient descent in both Python and TensorFlow....
5 videos (Total 36 min), 3 quizzes
5 videos
Introduction to Training and Testing6m
Gradient Descent in Python8m
Gradient Descent in TensorFlow6m
Livecoding: Tensorflow7m
3 practice exercises
Review: Classification and Training4m
Review: Gradient Descent4m
More on Classification10m

Instructors

Avatar

Julian McAuley

Assistant Professor
Computer Science
Avatar

Ilkay Altintas

Chief Data Science Officer
San Diego Supercomputer Center

About University of California San Diego

UC San Diego is an academic powerhouse and economic engine, recognized as one of the top 10 public universities by U.S. News and World Report. Innovation is central to who we are and what we do. Here, students learn that knowledge isn't just acquired in the classroom—life is their laboratory....

About the Python Data Products for Predictive Analytics Specialization

Python data products are powering the AI revolution. Top companies like Google, Facebook, and Netflix use predictive analytics to improve the products and services we use every day. Take your Python skills to the next level and learn to make accurate predictions with data-driven systems with this four-course Specialization from UC San Diego. This Specialization is for learners who are proficient with the basics of Python. You’ll start by creating your first data strategy. You’ll also develop statistical models, devise data-driven workflows, and learn to make meaningful predictions for a wide-range of business and research purposes. Finally, you’ll use design thinking methodology and data science techniques to extract insights from a wide range of data sources. This is your chance to master one of the technology industry’s most in-demand skills. Python Data Products for Predictive Analytics is taught by Professor Ilkay Altintas, Ph.D. and Julian McAuley. Dr. Alintas is a prominent figure in the data science community and the designer of the highly-popular Big Data Specialization on Coursera. She has helped educate hundreds of thousands of learners on how to unlock value from massive datasets....
Python Data Products for Predictive Analytics

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

More questions? Visit the Learner Help Center.