Design Thinking and Predictive Analytics for Data Products
- 4.5
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Course Summary
This course focuses on design thinking and predictive analytics for creating data products. You'll learn how to identify and solve problems using data, and how to create data products that provide actionable insights.Key Learning Points
- Learn to apply design thinking principles to data product development
- Understand how to use predictive analytics to create data products
- Explore best practices for creating data products that provide actionable insights
Related Topics for further study
Learning Outcomes
- Apply design thinking principles to data product development
- Create data products that provide actionable insights
- Use predictive analytics to enhance data product development
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of statistics and programming
- Familiarity with data analysis tools such as Python or R
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
- Video lectures
Similar Courses
- Data Science Methodology
- Data Visualization and Communication with Tableau
Related Education Paths
Notable People in This Field
- Cathy O'Neil
- Nate Silver
Related Books
Description
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.
Outline
- 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
- 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
Summary of User Reviews
Learn design thinking and predictive analytics for building data products. This course has received positive reviews from students who found it helpful in building data-driven products.Pros from User Reviews
- The course content is well-structured and easy to follow.
- The instructors are knowledgeable and engaging.
- The course provides hands-on experience with real-world data sets.
- The course provides valuable insights into the use of predictive analytics for building data products.
Cons from User Reviews
- Some students found the course to be too basic and lacking in depth.
- The course can be challenging for students without a strong background in statistics or data analysis.
- The course requires a significant time commitment to complete successfully.
Julian McAuley
4.5 Raiting
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