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
Offered By
Python Data Products for Predictive Analytics SpecializationUniversity of California San DiegoAbout this Course
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Shareable Certificate
Earn a Certificate upon completion
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Start instantly and learn at your own schedule.
Course 2 of 4 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 7 hours to complete
English
Subtitles: English
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 2 of 4 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 7 hours to complete
English
Subtitles: English
Offered by
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.
Syllabus - What you will learn from this course
2 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.
2 hours to complete
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
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.
1 hour to complete
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
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.
1 hour to complete
4 videos (Total 31 min)
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
1 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.
1 hour to complete
5 videos (Total 36 min)
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
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?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
- The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
- The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
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?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
Will I earn university credit for completing the Course?
This Course doesn't carry university credit, but some universities may choose to accept Course Certificates for credit. Check with your institution to learn more. Online Degrees and Mastertrack™ Certificates on Coursera provide the opportunity to earn university credit.
More questions? Visit the Learner Help Center.
