This course is part of the Python Data Products for Predictive Analytics Specialization

Julian McAuley +1 more instructor

Offered By

Python Data Products for Predictive Analytics SpecializationUniversity of California San Diego

About this Course

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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.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 2 of 4 in the

Python Data Products for Predictive Analytics Specialization

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 8 hours to complete

English

Subtitles: English

Instructors

Julian McAuley

Assistant Professor

Computer Science

20,486 Learners

4 Courses

Ilkay Altintas

Chief Data Science Officer

San Diego Supercomputer Center

384,008 Learners

12 Courses

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 2 of 4 in the

Python Data Products for Predictive Analytics Specialization

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 8 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

Week

Week 1

3 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.

3 hours to complete

5 videos (Total 46 min), 4 readings, 3 quizzes

5 videos

Introduction to Supervised Learning10m

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 Learning30m

Review: Regression30m

Supervised Learning & Regression10m

Week

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.

1 hour to complete

4 videos (Total 29 min), 1 reading, 3 quizzes

4 videos

Features from Categorical Data9m

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

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.

1 hour to complete

4 videos (Total 31 min)

4 videos

Supervised Learning: Classification5m

Classification: Nearest Neighbors4m

Classification: Logistic Regression10m

Introduction to Support Vector Machines10m

3 practice exercises

Review: Classification and K-Nearest Neighbors30m

Review: Logistic Regression and Support Vector Machines5m

Classification10m

Week

Week 4

2 hours 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.

2 hours to complete

5 videos (Total 36 min)

5 videos

Classification in Python7m

Introduction to Training and Testing6m

Gradient Descent in Python8m

Gradient Descent in TensorFlow6m

Livecoding: Tensorflow7m

3 practice exercises

Review: Classification and Training30m

Review: Gradient Descent30m

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.

Python Data Products for Predictive Analytics

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.
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.

More questions? Visit the Learner Help Center.

Design Thinking and Predictive Analytics for Data Products