Design Thinking and Predictive Analytics for Data Products
- Offered byCoursera
Design Thinking and Predictive Analytics for Data Products at Coursera Overview
Duration | 8 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Design Thinking and Predictive Analytics for Data Products at Coursera Highlights
- 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: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Design Thinking and Predictive Analytics for Data Products at Coursera Course details
- 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.
Design Thinking and Predictive Analytics for Data Products at Coursera Curriculum
Week 1: Supervised Learning & Regression
Introduction to Supervised Learning
Supervised Learning: Regression
Regression in Python
Time-Series Regression
Autoregression
Syllabus
Course Materials
Set Up Your System
Recap: Mathematical Notation
Review: Supervised Learning
Review: Regression
Supervised Learning & Regression
Week 2: Features
Features from Categorical Data
Features from Temporal Data
Feature Transformations
Missing Values
Supplementary Notebook for Features
Review: Getting Features
Review: Working with Features
Features
Week 3: Classification
Supervised Learning: Classification
Classification: Nearest Neighbors
Classification: Logistic Regression
Introduction to Support Vector Machines
Review: Classification and K-Nearest Neighbors
Review: Logistic Regression and Support Vector Machines
Classification
Week 4: Gradient Descent
Classification in Python
Introduction to Training and Testing
Gradient Descent in Python
Gradient Descent in TensorFlow
Livecoding: Tensorflow
Review: Classification and Training
Review: Gradient Descent
More on Classification
Final Project
Project Description
Where to Find Datasets
Design Thinking and Predictive Analytics for Data Products at Coursera Admission Process
Important Dates
Other courses offered by Coursera
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