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Predictive Modeling and Analytics
  • Level Foundation
  • المدة 11 ساعات hours
  • الطبع بواسطة University of Colorado Boulder
  • Offered by

عن

Welcome to the second course in the Data Analytics for Business specialization! This course will introduce you to some of the most widely used predictive modeling techniques and their core principles. By taking this course, you will form a solid foundation of predictive analytics, which refers to tools and techniques for building statistical or machine learning models to make predictions based on data. You will learn how to carry out exploratory data analysis to gain insights and prepare data for predictive modeling, an essential skill valued in the business. You'll also learn how to summarize and visualize datasets using plots so that you can present your results in a compelling and meaningful way. We will use a practical predictive modeling software, XLMiner, which is a popular Excel plug-in. This course is designed for anyone who is interested in using data to gain insights and make better business decisions. The techniques discussed are applied in all functional areas within business organizations including accounting, finance, human resource management, marketing, operations, and strategic planning. The expected prerequisites for this course include a prior working knowledge of Excel, introductory level algebra, and basic statistics.

الوحدات

Introduction

    • 2 Videos
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2 Videos

  • Introduction to the Course
  • 0. Introduction to the Module. Why Exploratory Data Analysis is Important

Data Preprocessing

    • 4 Videos
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4 Videos

  • 1. Data Cleanup and Transformation
  • 2. Dealing With Missing Values
  • 3. Dealing with Outliers
  • 4. Adding and Removing Variables

Data Visualization

    • 2 Videos
    • 1 Discussion
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1 Discussions

  • Data Exploration

2 Videos

  • 5. Common Graphs
  • 6. What is Good Data Visualization?

Assignments

    • 1 Readings
    • 1 PeerReview
    • 2 Assignment
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2 Assignment

  • Week 1 Quiz
  • Week 1 Application Assignment 1 (optional): Data Cleanup

1 Peer Review

  • Week 1 Application Assignment 2: Data Visualization

1 Readings

  • Register for Analytic Solver Platform for Education (ASPE)

Introduction

    • 1 Videos
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1 Videos

  • 0. Introduction to Predictive Modeling

Regression Techniques

    • 7 Videos
    • 1 Discussion
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1 Discussions

  • Reflection on Statistical Techniques

7 Videos

  • 1. Introduction to Linear Regression
  • 2. Assessing Predictive Accuracy Using Cross-Validation
  • 3. Multiple Regression
  • 4. Improving Model Fit
  • 5. Model Selection
  • 6. Challenges of Predictive Modeling
  • 7. How to Build a Model using XLMiner

Assignments

    • 2 Assignment
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2 Assignment

  • Week 2 Quiz
  • Week 2 Application Assignment

Introduction

    • 1 Videos
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1 Videos

  • 0. Introduction to classification

Logistic Regression

    • 3 Videos
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3 Videos

  • 1. Introduction to Logistic Regression
  • 2. Building Logistic Regression Model
  • 3. Multiple Logistic Regression

Assessing and Improving Classification Models

    • 4 Videos
    • 1 Discussion
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1 Discussions

  • The Best Prediction Method

4 Videos

  • 4. Cross Validation and Confusion Matrix
  • 5. Cost Sensitive Classification
  • 6. Comparing Models Independent of Costs and Cutoffs
  • 7. Building Logistic Regression Models using XLMiner

Assignments

    • 2 Assignment
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2 Assignment

  • Week 3 Quiz
  • Week 3 Application Assignment

Introduction

    • 1 Videos
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1 Videos

  • 0.Introduction to Advanced Predictive Modeling Techniques

Trees

    • 5 Videos
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5 Videos

  • 1. Introduction to Trees
  • 2. Classification Trees
  • 3. Regression Trees
  • 4. Bagging, Boosting, Random Forest
  • 5. Building Trees with XLMiner

Neural Networks

    • 2 Videos
    • 1 Discussion
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1 Discussions

  • Reflection: Trees & Neural Networks

2 Videos

  • 6. Neural Networks
  • 7. Building Neural Networks using XLMiner

Assignments

    • 1 PeerReview
    • 3 Assignment
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3 Assignment

  • Week 4 Quiz
  • Week 4 Application Assignment
  • Final Course Assignment Quiz

1 Peer Review

  • Final Course Assignment Peer Review

Auto Summary

Discover the essentials of Predictive Modeling and Analytics in this foundational Data Science & AI course by Coursera. Led by an expert instructor, this 660-minute course delves into predictive modeling techniques, exploratory data analysis, and data visualization using XLMiner. Ideal for business professionals across various domains, it requires basic Excel, algebra, and statistics knowledge. Available through Starter, Professional, and Paid subscriptions.

Dan Zhang
Instructor

Dan Zhang