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Design Thinking and Predictive Analytics for Data Products

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Design Thinking and Predictive Analytics for Data Products

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Gain insight into a topic and learn the fundamentals.
4.5

116 reviews

Intermediate level
Some related experience required
8 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
4.5

116 reviews

Intermediate level
Some related experience required
8 hours to complete
Flexible schedule
Learn at your own pace

Build your subject-matter expertise

This course is part of the Python Data Products for Predictive Analytics Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 5 modules in this course

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.

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.

What's included

5 videos4 readings3 assignments2 discussion prompts

5 videos•Total 46 minutes
  • Introduction to Supervised Learning•11 minutes
  • Supervised Learning: Regression•10 minutes
  • Regression in Python•10 minutes
  • Time-Series Regression•8 minutes
  • Autoregression•6 minutes
4 readings•Total 40 minutes
  • Syllabus•10 minutes
  • Course Materials•10 minutes
  • Set Up Your System•10 minutes
  • Recap: Mathematical Notation•10 minutes
3 assignments•Total 70 minutes
  • Supervised Learning & Regression•10 minutes
  • Review: Supervised Learning•30 minutes
  • Review: Regression•30 minutes
2 discussion prompts•Total 20 minutes
  • What do you hope to get out of taking this course?•10 minutes
  • What are some applicable uses of regression in the world today?•10 minutes

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.

What's included

4 videos1 reading3 assignments

4 videos•Total 29 minutes
  • Features from Categorical Data•9 minutes
  • Features from Temporal Data•8 minutes
  • Feature Transformations•4 minutes
  • Missing Values•8 minutes
1 reading•Total 3 minutes
  • Supplementary Notebook for Features•3 minutes
3 assignments•Total 10 minutes
  • Review: Getting Features•0 minutes
  • Review: Working with Features•0 minutes
  • Features•10 minutes

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.

What's included

4 videos3 assignments1 discussion prompt

4 videos•Total 31 minutes
  • Supervised Learning: Classification•5 minutes
  • Classification: Nearest Neighbors•4 minutes
  • Classification: Logistic Regression•10 minutes
  • Introduction to Support Vector Machines•11 minutes
3 assignments•Total 45 minutes
  • Classification•10 minutes
  • Review: Classification and K-Nearest Neighbors•30 minutes
  • Review: Logistic Regression and Support Vector Machines•5 minutes
1 discussion prompt•Total 10 minutes
  • What are some applicable uses of classification in the world today?•10 minutes

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.

What's included

5 videos3 assignments

5 videos•Total 36 minutes
  • Classification in Python•7 minutes
  • Introduction to Training and Testing•6 minutes
  • Gradient Descent in Python•9 minutes
  • Gradient Descent in TensorFlow•7 minutes
  • Livecoding: Tensorflow•7 minutes
3 assignments•Total 75 minutes
  • More on Classification•15 minutes
  • Review: Classification and Training•30 minutes
  • Review: Gradient Descent•30 minutes

In the final week of this course, you will continue building on the project from the first course of Python Data Products for Predictive Analytics with simple predictive machine learning algorithms. Find a dataset, clean it, and perform basic analyses on the data.

What's included

2 readings1 peer review1 discussion prompt

2 readings•Total 20 minutes
  • Project Description•10 minutes
  • Where to Find Datasets•10 minutes
1 peer review•Total 60 minutes
  • Project Submission•60 minutes
1 discussion prompt•Total 10 minutes
  • What is something you learned from doing this final project?•10 minutes

Earn a career certificate

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Instructors

Instructor ratings
4.4 (57 ratings)
University of California San Diego
5 Courses•35,001 learners
University of California San Diego
14 Courses•518,583 learners

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Showing 3 of 116

AM
·

Reviewed on May 7, 2021

It was great course ,helped me in getting better understanding of data and do predictive modeling.

PM
·

Reviewed on Apr 6, 2026

this platform provides an oppertunity to spread my knowledge beyond my careerline

A
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Reviewed on Apr 15, 2026

Design Thinking and Predictive Analytics for Data Products

Frequently asked questions

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. 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.

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.

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