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Data Science for Managers

  • Welcome to Data Science for Managers!

R Bootcamp

  • R Basics
    • R as a Calculator
    • Assignment
    • Data Types
    • Quiz #1
    • Atomic Vectors
    • Functions
    • Quiz #2
  • Data Frames
    • R Packages
    • Reading in Data
    • Data Frame Basics
    • Exercise: Data Frame Basics
    • Fixing Variable Types
    • Sorting Data
    • Filtering Rows
    • Selecting Columns
    • Exercise: Data Frame Manipulation
    • Quiz
  • Welcome to the Course!

Exploratory Data Analysis

  • 1. Exploring Data
    • 1.1. Defining Data
    • 1.2. Summary Statistics
    • 1.3. Visualization
    • 1.4. Outliers
  • 2. Wrangling & Visualization with the tidyverse
    • 2.1. The Pipe Operator
    • 2.2. Summarising Data
    • 2.3. (§) Visualization with ggplot2
    • 2.4. Tutorial: Summary Statistics & Visualization

Inference

  • 3. Statistical Inference
    • 3.1. Samples & Populations
    • 3.2. (§) Confidence Intervals
    • 3.3. Hypothesis Testing
    • 3.4. Tutorial: Hypothesis Testing
  • 4. Causal Inference
    • 4.1. Observational Studies
    • 4.2. Randomized Experiments
    • 4.3. Tutorial: Causal Inference
    • 4.4. Power
    • 4.5. Tutorial: Power
  • 5. Linear Regression
    • 5.1. Correlation
    • 5.2. Simple Linear Regression
    • 5.3. Understanding Our Regression Model
    • 5.4. Multiple Linear Regression
    • 5.5. Dummy Variables
    • 5.6. Tutorial: Linear Regression
    • 5.7. (§) Transformations
    • 5.8. (§) Interactions

Prediction & Machine Learning

  • 6. Introduction to Machine Learning
  • 7. Logistic Regression
    • 7.1. Why Not Linear Regression?
    • 7.2. Simple Logistic Regression
    • 7.3. Multiple Logistic Regression
    • 7.4. Building Logistic Regression Models
    • 7.5. Tutorial: Logistic Regression
  • 8. (§) k-Nearest Neighbors (kNN)
    • 8.4. The Bias-Variance Trade-off
  • 9. Tree Models
    • 9.1. Decision Trees
    • 9.2. (§) Random Forest
    • 9.3. (§) XGBoost
  • 10. Model Evaluation
    • 10.1. Partitioning Data
    • 10.2. Performance Metrics
    • 10.3. Tutorial: Building & Validating a Decision Tree Model
  • 11. (§) Neural Networks
  • 12. (§) Unsupervised Learning
  • 13. (§) Natural Language Processing

Appendix

  • Essential R Commands
  • Coding Platforms
  • Web Applications
  • Recommended Materials
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Contents
  • Articles
  • Book & Textbooks

Recommended Materials¶

Articles¶

Artificial Intelligence—The Revolution Hasn’t Happened Yet by Michael I. Jordan

Avoid the Pitfalls of A/B Testing by Iavor Bojinov, Guillaume Saint-Jacques, and Martin Tingley

Democratizing Transformation by Marco Iansiti & Satya Nadella

Book & Textbooks¶

Agrawal, Ajay, Joshua Gans, and Avi Goldfarb. Prediction Machines: The Simple Economics of Artificial Intelligence., 2018. Print.

Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. An Introduction to Statistical Learning : with Applications in R. New York :Springer, 2013.

Iansiti, Marco, and Karim R. Lakhani. Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World. Boston: Harvard Business Review Press, 2020.

Provost, Foster and Fawcett, Tom. Data Science for Business. Beijing: O’Reilly, 2013.

Taddy, Matt. Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions. McGraw-Hill Education, 2019.

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Web Applications

By Professor Iavor Bojinov, Senior Lecturer Michael Parzen, and Research Associate Paul J. Hamilton
© Copyright 2021.