This is the second course in the four-course specialization Python Data Products for Predictive Analytics, building on the data processing covered in Course 1 and introducing the basics of designing predictive models in Python. In this course, you will understand the fundamental concepts of statistical learning and learn various methods of building predictive models. At each step in the specialization, you will gain hands-on experience in data manipulation and building your skills, eventually culminating in a capstone project encompassing all the concepts taught in the specialization.
This course is part of the Python Data Products for Predictive Analytics Specialization
Design Thinking and Predictive Analytics for Data Products
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About this Course
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Course 2 of 4 in the
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Intermediate Level
Approx. 10 hours to complete
Suggested: 4 hours/week
English
Subtitles: English
Syllabus - What you will learn from this course
Week
12 hours to complete
Week 1: Supervised Learning & Regression
Welcome to the second course in this specialization! This 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
21 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), 1 reading, 3 quizzes
4 videos
Features from Temporal Data8m
Feature Transformations4m
Missing Values7m
1 reading
Supplementary Notebook for Features3m
3 practice exercises
Review: Getting Features
Review: Working with Features
Features10m
Week
31 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
41 hour to complete
Week 4: Gradient Descent
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 Classification15m
Instructors
Julian McAuley
Assistant ProfessorComputer Science
Ilkay Altintas
Chief Data Science OfficerSan 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 and deploy machine learning models 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.
Frequently Asked Questions
When will I have access to the lectures and assignments?
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.
What will I get if I subscribe to this Specialization?
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.
What is the refund policy?
Is financial aid available?
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