Daniel Caraway

Daniel Caraway

I put the fun in functions.

MSDS SCM 651: Business Analytics

3 minute read

Business Analytics

MSDS (MBA) - Q3: SCM651

Description

This course is intended for the graduate student who is interested in developing a portfolio of skills in business analytics.

Learning Objectives

  1. Data collection: using tools to collect and organize data (e.g., Google Analytics)
  2. Data analysis: identify patterns in the data via visualization, statistical analysis, and data mining
  3. Strategy and decisions: develop alternative strategies based on the data
  4. Implementation: develop a plan of action to implement the business decisions

Deliverables

Class Outline

Assignments and Deliverables

  • Submission: Homework Assignment 1
  • Submission: Homework Assignment 2
  • Submission: Homework Assignment 3
  • Submission: Homework Assignment 4
  • Submission: Team Peer Review
  • Final Exam
  • Grading: Team Peer Review
  • Grading: Class Participation

Unit 1: Business Analytics and Data Visualization

  • 1.1 Weekly Introduction
  • 1.2 Week 1 Overview
  • 1.3 What is Business Analytics? - Case Overview
  • 1.4 What is Driving Analytics?
  • 1.5 What Makes Analytics Difficult?
  • 1.6 Excel Overview
  • 1.7 Excel: Calculation and Formulas
  • 1.8 Excel: Graphing and Visualization
  • 1.9 Excel: Sorting and Filters
  • 1.10 Excel: Pivot Tables and Charts
  • 1.11 Excel: Powerview

Unit 2: Financial Analysis and Statistics

  • 2.1 Weekly Introduction
  • 2.2 Week 2 Overview
  • 2.3 Excel: New Present Value
  • 2.4 Excel: Internal Rate of Return
  • 2.5 Excel: Data Analysis Install
  • 2.6 Excel: Descriptive Statistics
  • 2.7 Excel: Correlations
  • 2.8 Regression Overview
  • 2.9 Excel: Univariate Linear Regression
  • 2.10 Excel: Exponential Regression
  • 2.11 Excel: Power Regression
  • 2.12 Excel: Multivariate Regression
  • 2.13 Excel: Time Series Moving Average Regression

Unit 3: Sensitivity Analysis, Dashboards, and Google Analytics

  • 3.1 Weekly Introduction
  • 3.2 Week 3 Overview
  • 3.3 Excel: One-way Sensitivity Analysis
  • 3.4 Excel: Two-way Sensitivity Analysis
  • 3.5 Excel: Conditional Formatting
  • 3.6 Excel: Dashboards
  • 3.7 Google Analytics Overview
  • 3.8 Google Analytics: Audience
  • 3.9 Google Analytics: Acquisition
  • 3.10 Google Analytics: Behavior

Unit 4: Databases and Queries

  • 4.1 Weekly Introduction
  • 4.2 Week 4 Overview
  • 4.3 Databases
  • 4.4 MS Access: Importing Data
  • 4.5 MS Access: Creating Relationships
  • 4.6 MS Access: Simple Queries
  • 4.7 MS Access: Fixing Dirty Data
  • 4.8 MS Access: Complex Queries

Unit 5: PowerPivot

  • 5.1 Weekly Introduction
  • 5.2 Week 5 Overview
  • 5.3 Excel: PowerPivot Overview
  • 5.4 Excel: PowerPivot Install
  • 5.5 Excel: PowerPivot Importing
  • 5.6 Excel: PowerPivot Relationships
  • 5.7 Excel: PowerPivot Table Properties and Filters
  • 5.8 Excel: Creating Pivot Tables with PowerPivot
  • 5.9 Excel: PowerPivot Slicers
  • 5.10 Excel: PowerPivot Timelines
  • 5.11 Excel: PowerPivot Charts

Unit 6: Optimization Overview

  • 6.1 Weekly Introduction
  • 6.2 Week 6 Overview
  • 6.3 Optimization Overview
  • 6.4 Excel: Goal Seek
  • 6.5 Excel: Solver Install
  • 6.6 Excel: Solver Unconstrained Optimization
  • 6.7 Excel: Useful Functions in Solver
  • 6.8 Excel: Optimal Product Mix Optimization
  • 6.9 Excel: Workforce Scheduling Optimization
  • 6.10 Excel: Transportation & Distribution Optimization
  • 6.11 Excel: Capital Budgeting Optimization

Unit 7: Statistical Analysis with R

  • 7.1 Weekly Introduction
  • 7.2 Week 7 Overview
  • 7.3 Overview of R
  • 7.4 R: Loading and Viewing Data
  • 7.5 R: Histograms, Boxplots, Scatterplots, Mean Plots, XY Plots
  • 7.6 R: 3D Graphs
  • 7.7 R: Statistical Summaries
  • 7.8 R: Correlations
  • 7.9 R: ANOVA
  • 7.10 R: Regression
  • 7.11 R: Regression with Dummy Variables
  • 7.12 R: Regression with Moderating Effects

Unit 8: Regression Diagnostics, Fraud Detection, and Decision Trees

  • 8.1 Weekly Introduction
  • 8.2 Week 8 Overview
  • 8.3 Regression Assumptions and Diagnostics Overview
  • 8.4 R: Regression Linearity Test
  • 8.5 R: Collinearity Test
  • 8.6 R: Heteroscedasticity Test
  • 8.7 R: Serial Correlation Test
  • 8.8 R: Outlier Test
  • 8.9 Data Mining & Installing Rattle
  • 8.10 R: Benford’s Law
  • 8.11 R: Decision Trees

Unit 9: Choice Models and Neural Networks

  • 9.1 Weekly Introduction
  • 9.2 Week 9 Overview
  • 9.3 Choice Models Overview
  • 9.4 R: Logit Analysis
  • 9.5 R: Logit Predictions
  • 9.6 R: Probit Analysis
  • 9.7 R: Probit Predictions
  • 9.8 R: Perceptron & Neural Network Overview

Unit 10: Tableau Dashboards

  • 10.1 Weekly Introduction
  • 10.2 Week 10 Overview
  • 10.3 Overview of DashBoards
  • 10.4 Tableau: Demonstration
  • 10.5 Tableau: Connecting to Databases
  • 10.6 Tableau: Creating Relationships
  • 10.7 Tableau: Building Worksheets
  • 10.8 Tableau: Geolocations
  • 10.9 Tableau: Calculations
  • 10.10 Tableau: Filters
  • 10.11 Tableau: Building Dashboards
  • 10.12 Interview with Jaycob BurnsPage

RESOURCES:

Deep Neural Networks:

DeepMind AlphaGo defeats world champion in game of Go (March 2016; Jan 2017 update): https://www.scientificamerican.com/article/how-the-computer-beat-the-go-master/ http://fortune.com/2017/01/07/google-alphago-ai/

Google Brain’s neural network develop AI encryption (November 2016): https://www.scmagazine.com/google-brains-neural-networks-develops-ai-encryption/article/570049/ http://www.wired.co.uk/article/google-artificial-intelligence-encryption

DeepStack Defeats 10 out of 11 poker champions (March 2017), develops intuition: http://www.cnn.com/2017/03/02/health/artificial-intelligence-poker-intuition-study/index.html https://www.scientificamerican.com/article/time-to-fold-humans-poker-playing-ai-beats-pros-at-texas-hold-rsquo-em/

Facebook’s Artificial Intelligence robots shut down after they start talking to each other in their own language (July 2017) http://www.independent.co.uk/life-style/gadgets-and-tech/news/facebook-artificial-intelligence-ai-chatbot-new-language-research-openai-google-a7869706.html

A bot just defeated one of the world’s best video gamers (neural network beats world champions in multi-player game – August 2017) http://money.cnn.com/2017/08/12/technology/future/elon-musk-ai-dota-2/index.html

Not Neural Networks:

DeepBlue defeats Gary Kasporov in chess (1996): https://en.wikipedia.org/wiki/Deep_Blue_(chess_computer)

DeepThought defeats chess grand master Brent Larsen (1988), but loses to Gary Kasparov in chess (1989): https://en.wikipedia.org/wiki/Deep_Thought_(chess_computer)

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