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Design Thinking and Predictive Analytics for Data Products
  • Level Professional
  • Duration 8 hours
  • Course by University of California San Diego
  • Offered by

About

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.

Modules

Course Information

    • 3 Readings
    • 1 Discussion
Show info about module content

1 Discussions

  • What do you hope to get out of taking this course?

3 Readings

  • Syllabus
  • Course Materials
  • Set Up Your System

Recap: Mathematical Notation

    • 1 Readings
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1 Readings

  • Recap: Mathematical Notation

Supervised Learning

    • 2 Videos
    • 1 Assignment
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1 Assignment

  • Review: Supervised Learning

2 Videos

  • Introduction to Supervised Learning
  • Supervised Learning: Regression

Regression

    • 3 Videos
    • 1 Assignment
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1 Assignment

  • Review: Regression

3 Videos

  • Regression in Python
  • Time-Series Regression
  • Autoregression

Week 1 Assessment

    • 1 Discussion
    • 1 Assignment
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1 Assignment

  • Supervised Learning & Regression

1 Discussions

  • What are some applicable uses of regression in the world today?

Features

    • 4 Videos
    • 1 Readings
    • 2 Assignment
Show info about module content

2 Assignment

  • Review: Getting Features
  • Review: Working with Features

4 Videos

  • Features from Categorical Data
  • Features from Temporal Data
  • Feature Transformations
  • Missing Values

1 Readings

  • Supplementary Notebook for Features

Week 2 Assessment

    • 1 Assignment
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1 Assignment

  • Features

Classification

    • 4 Videos
    • 2 Assignment
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2 Assignment

  • Review: Classification and K-Nearest Neighbors
  • Review: Logistic Regression and Support Vector Machines

4 Videos

  • Supervised Learning: Classification
  • Classification: Nearest Neighbors
  • Classification: Logistic Regression
  • Introduction to Support Vector Machines

Week 3 Assessment

    • 1 Discussion
    • 1 Assignment
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1 Assignment

  • Classification

1 Discussions

  • What are some applicable uses of classification in the world today?

More on Classification

    • 2 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • Review: Classification and Training

2 Videos

  • Classification in Python
  • Introduction to Training and Testing

Gradient Descent

    • 3 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • Review: Gradient Descent

3 Videos

  • Gradient Descent in Python
  • Gradient Descent in TensorFlow
  • Livecoding: Tensorflow

Week 4 Assessment

    • 1 Assignment
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1 Assignment

  • More on Classification

Project 2: Making Predictions From Data

    • 2 Readings
    • 1 Discussion
    • 1 PeerReview
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1 Peer Review

  • Project Submission

1 Discussions

  • What is something you learned from doing this final project?

2 Readings

  • Project Description
  • Where to Find Datasets

Auto Summary

Enhance your data science skills with "Design Thinking and Predictive Analytics for Data Products." This professional-level course, led by Coursera, dives into designing predictive models using Python. Over 480 minutes, you'll explore statistical learning, data manipulation, and hands-on model building. Ideal for individuals aiming to advance in predictive analytics, with subscription options available for both Starter and Professional levels.

Julian McAuley
Instructors

Julian McAuley

Ilkay Altintas
Instructors

Ilkay Altintas