Design Thinking and Predictive Analytics for Data Products

Design Thinking and Predictive Analytics for Data Products

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

Instructors: Julian McAuley

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5 modules

Gain insight into a topic and learn the fundamentals.

68 reviews

Intermediate level

Some related experience required

8 hours to complete

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

68 reviews

Intermediate level

Some related experience required

8 hours to complete

Flexible schedule

Learn at your own pace

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

12 assignments¹

AI Graded see disclaimer

Taught in English

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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

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

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.