"There are five managerial roles; they are to set objectives, organize, motivate and communicate, measure, and develop people."
-Peter Drucker (1909-2005)

Course Outline/Calendar

The purpose of this web site is to provide information and materials for students enrolled in Wayne Smith's Business 312 course, Class # 20656, for the Spring, 2024 semester. This course meets Mon/Wed 9:30am - 10:45am (Mon 1/22 - Wed 5/15, 16 weeks) in BB2206 [ Campus Map ]. This course is taught in the in-person format. This course is offered by the College of Business and Economics at California State University, Northridge (CSUN). CSU Northridge is one of 23 campuses in the California State University system.

The following summary identifies the key terms used in the course outline.

Note: Materials listed as (CSUN only) are restricted to CSUN students, faculty, and staff only. To access these restricted materials, CSUN stakeholders will need to enter their full CSUN email address and their CSUN password. There is a small chance this type of authentication may not work for some smartphones or tablets; in that case, simply use a laptop or desktop computer.

Question:

A general organizing question to place this particular material in context

Topic(s):

General topic(s) to be discussed

Richardson/Watson:

Readings from the textbook

Library:

Readings from the library materials (or occasionally, a direct, online resource)

Supplemental:

Miscellaneous Readings

Due:

Assignment or report due at the immediate beginning of class

Review:

Usually a student-led review of an assignment or report

Exercise:

done individually or in teams (announced and collected in-class)

Activity:

done individually or in teams (announced and done either in-class or as homework)

Lecture:

a lecture or presentation done by the instructor

Video Clip(s):

Video Clip(s) shown in class, typically from a movie or TV show

Quiz:

a relatively short test

Exam:

a relatively long test

(contingency)

Slack time embedded during the course to cover unforeseen events

The following is the detail of the course in chronological order. Students must complete the readings and assignments before the due date.

• Topic(s): Enrollment Management, Class Materials, Class Introduction
• Assumptions I'll Make About You:
  • Supplemental: Review of First Principles (Fundamentals): Lower-Division Core [ .pptx ]
  • Teams and Teamwork [ .pdf ] (Sat. Aug 5, 2023)
  • Sample Quiz - Spreadsheets [ .pdf ]
  • Examples of Strong Writing: Sentences [ .pptx ], Paragraphs [ .pptx ], Arguments [ .pptx ], and Style [ .pptx ]
  • Examples of Areas for Improvement: Language Use and Grammar [ .pptx ]
    • Detailed Writing Errors [ .xlsx ]
  • Lecture: Professional Influence (Elements of an Argument) [ .pptx ]
  • Lecture: Public Speaking Tips (pp. 12-5 to 12-10) [ .pdf ]
• Lecture: Introductory Background Lecture [ .pptx ] (Sun. Jan 28, 2024)
• Library: Sandberg (Tardiness Contagion)

• Lecture: Guest, Craig Oka/Danielle Rodriguez/Karla Ayala (Director/Coordinator(s), Nazarian College Career Education and Professional Development (CEPD) Office)
  • office: BB2234, phone: 818-677-4697
  • Activity: Nazarian CEPD 'Handshake' Registration (answer the questionnaire to set up a profile)
    • Login [ .html ]
    • Assignment Details [ .pdf ]
    • Due (Fri. Feb. 9, in Handshake): Update your Handshake profile
  • Activity: Nazarian CEPD 'VMock' Registration (create a login with your CSUN email address and upload your current resume twice)
    • Login [ .html ]
    • Assignment Details [ .pdf ]
    • Due (Fri. Feb. 9, in VMOCK): Upload an initial Résumé and an updated Résumé
  • Supplemental: Important Career Dates For Spring, 2024
    • Industry Night [ .html ] (All Nazarian students)
    • Meet the Firms - Day/Time [ .html ], Registration - [ .html ] (Accountancy students)

• (please place non-needed items in the back shelves or "cubbyholes")
• Activity: Discuss Assignment: Data Opportunity/Challenge Essay
• Richardson/Watson: Ch. 1, Using Business Analytics to Address Business Questions (Textbook Powerpoint slides) [ .pptx ]
• Lecture: Introduction to Data and Analytics [ .pptx ]
• Library: Moss (Zara/Dresses/Data/Profits)
  • Question: Which elements of Zara's strategy are related to which theories from your prior courses?
  • Question: Which elements of Zara's strategy employ which elements of Analytics?

• Richardson/Watson: Ch. 2, An Introduction to Business Data Sources (Textbook Powerpoint slides) [ .pptx ]
• Richardson/Watson: Appendix E, Installing Excel's Analysis Toolpak Add-in
• Richardson/Watson: Appendix F, Installing Excel's Solver Add-in
• Lecture: Locating and Using Quality Data Sources
• Library: Lin (Farmers' Data Surplus)
• Library: Pohle (AirBnB Reviews)
• Lecture: Introductory Excel
  • ACME Sales [ .xlsx, .csv ]
  • Menus, Ribbon, Files (.csv/.xlsx/.pdf), Formatting (numbers, lines, other)
  • Inserting/Deleting Rows, Quick Navigation (keyboard), Basic Functions, Getting Help
  • Relative/Absolute References, InterWorksheet References, Copy/Paste Special
  • Installing Analysis Toolpak, Installing Solver Addin
• Lecture: Introductory Excel (try it on your own)
  • ACME Performance Evaluation [ .xlsx, .csv ]

• (Due to inclement weather class today is cancelled)
• Richardson/Watson: Ch. 6, Using Data Visualization
• Lecture: Visualization in Excel and PivotTables/Charts in Excel
  • ACME Sales [ .xlsx, .csv ]
  • Freeze Panes, Data Sort, Data Filter
  • Data Dictionary
  • Exploratory Graphing, Results Graphing
  • Pre-Charting/Wrangling, Types of Charts (Continuous data/Discrete data), Formatting, Documentation
  • Pivot Tables
  • Pivot Charts
• Lecture: Visualization in Excel and PivotTables/Charts in Excel (try it on your own)
  • ACME Performance Evaluation [ .xlsx, .csv ]

• Due: Assignment: Data Opportunity/Challenge Essay
• Richardson/Watson: Ch. 3, Statistics and Tools Required in Business Analytics (Sections 3.1 - 3.5)
• Lecture: Using the Excel Data Analysis Tools (Menu | Data | Data Analysis)
  • ACME Sales [ .xlsx, .csv ]
  • Descriptive Analytics - Rank and Percentile, Descriptive Statistics
  • Diagnostic Analytics - Histogram, z-Test, t-Test, F-test, Analysis of Variance (ANOVA)
  • Predictive Analytics - Correlation, Regression
  • Other (FIN) - Covariance
  • Other (MKT) - Sampling
  • Other (SOM 306) - Experiential Smoothing, Moving Average
  • Other (misc.) - Random Number Generation
  • Other (Engineering) - Fourier Analysis
• Lecture: Using the Excel Data Analysis Tools (Menu | Data | Data Analysis) (try it on your own)
  • ACME Performance Evaluation [ .xlsx, .csv ]

• Lecture: Team Assignment
• Lecture: Data Analytics Case choices [ .pdf ] (Fri. Feb 16, 2024)
• Richardson/Watson: Ch. 3, Statistics and Tools Required in Business Analytics (Sections 3.6 - 3.7)
• Lecture: Exploratory Data Analysis (Descriptive Analytics)
  • ACME Sales [ .xlsx, .csv ]
  • Measures of Central Tendency - Mean, Median, Mode
  • Measures of Dispersion - Range, Variance, Standard Deviation, Skewness, Kurtosis
  • ACME Sales Profit/Loss Histogram [ .xlsx ]
  • Ratios - Coefficent of Variation
• Lecture: Exploratory Data Analysis (Descriptive Analytics) (try it on your own)
  • ACME Performance Evaluation [ .xlsx, .csv ]

• Richardson/Watson: Ch. 3, Statistics and Tools Required in Business Analytics (Section 3.8, part 1 of 2)
• Library: Hopkins (Evaluating Drug Efficacy)
• Library: Glickman (Rite Aid's AI/Facial Recognition)
• Lecture: Hypothesis Test ('June at the Multiplex' Case)
  • Supplemental: Case Text - [ .pdf ]
  • Supplemental: Case Questions - [ .pdf ]
  • Supplemental: Case Data File - [ .xlsx ]
  • Supplemental: Case Analysis - [ .pptx ]
  • Supplemental: Case Solution Data File - [ .xlsx ]
• Lecture: Hypothesis Tests in Excel (Diagnostic Statistics)
  • z-Test
  • t-Test
  • Confidence Intervals
• Lecture: Hypothesis Tests in Excel (Diagnostic Statistics) (try it on your own)
  • ACME Performance Evaluation [ .xlsx, .csv ]

• Richardson/Watson: Ch. 3, Statistics and Tools Required in Business Analytics (Section 3.8, part 2 of 2)
• Video Clip(s): Big Bang Theory (clip) Hypothesis Testing - "The Fuzzy Boots Corollary" (Season 1, Disk 1, Episode 3) (YouTube clip) [ .html ]
• Lecture: Hypothesis Test ('June at the Multiplex' Case, continued)
• (contingency)

• Richardson/Watson: Ch. 3, Statistics and Tools Required in Business Analytics (Section 3.8)
• Library: Smith (Predicting Swimsuit Sales)
• Lecture: Linear Regression ('Artisan Emporium' Case)
  • Supplemental: Case Text - [ .pdf ]
  • Supplemental: Case Questions - [ .pdf ]
  • Supplemental: Case Data File - [ .xlsx ]
  • Supplemental: Case Analysis - [ .pptx ]
  • Supplemental: Case Solution Data File - [ .xlsx ]
• Lecture: Linear Regression in Excel (Predictive Statistics)
  • ACME Sales [ .xlsx, .csv ]
  • Scatterplots, Correlation
  • Simple (single independent variable) Linear Regression
• Lecture: Linear Regression in Excel (Predictive Statistics) (try it on your own)
  • ACME Performance Evaluation [ .xlsx, .csv ]

• Due: First, second, and third Data Analytics Case choices
• Lecture: Description of Data Analytics Case deliverables: analysis (written and presentation) [ .html ]
• Richardson/Watson: Ch. 4, Business Analytics (Descriptive and Diagnostic Analytics)
• Ethical Decision-making [ .pptx ] (Sat. Jul 16, 2022)
• Supplmental: Artificial Intelligence: The Global Landscape of Ethical Guidelines (Nature Journal) [ .pptx ]
• Supplmental: A Guide to Ethical Data Science (Royal Statistical Society) [ .pptx ]

• Richardson/Watson: Ch. 5, Confirmatory Business Analytics (Predictive Analytics and Prescriptive Analytics)
• Activity: Canvas Lab Assignments (to be done in class with a student laptop)
• (contingency)

• Richardson/Watson: Ch. 12, Putting It All Together: Three Capstone Projects
• Activity: Review Canvas Lab Assignments (to be done in class with a student laptop)
• Library: Whelan (Disney Pay Dispute)

• Lecture: Analysis of Disney Gender Pay Gap Dispute
• Lecture: Questions, Exploratory Visualizations, Descriptive Analytics, Diagnostic Analytics, and Predictive Analytics
• Disney Salary Data [ .xlsx, .csv ]
• Disney Salary Data Dictionary [ .xlsx, .csv ]
• Lecture: Intermediate Excel (Simple Data Joins and Queries)
  • ACME Sales [ .xlsx, .csv ]
  • Special Tools: =VLOOKUP, =DSUM, =DCOUNT, =DAVERAGE
  • Example: =VLOOKUP [ .xlsx ]
  • Example: =DSUM [ .xlsx ]
• Lecture: Intermediate Excel (Simple Data Joins and Queries) (try it on your own)
  • ACME Performance Evaluation [ .xlsx, .csv ]

• Activity: Personal Reflection #1
• Mid-course Team Evaluation
  • Activity: Informal Team Evaluation [ .pdf ] (Sat. Aug 5, 2023)
  • Activity: Performance Improvement [ .pptx ] (Sun. Apr 14, 2024)
  • Activity: Team meeting with the Instructor (if necessary)
• Supplemental: Data Analytics Case (Common Errors and Omissions) [ .pptx ] (Mon. Mar 4, 2024)
• Activity: Review Disney Data Analytics
• Lecture: Review of Visualizations and Hypothesis Tests
  • ACME Sales [ .xlsx, .csv ]
  • Exploratory Visualizations
  • t-tests
  • Example: [ .xlsx ]
• Lecture: Review of Visualizations and Hypothesis Tests (try it on your own)
  • ACME Performance Evaluation [ .xlsx, .csv ]
• (contingency)

• Exam: Mid-term

• (no class, Spring Recess)

• (no class, Spring Recess)

• Richardson/Watson: Ch. 7, Marketing Analytics
• Library: Flint (NFL Audience Data)
• Lecture: Intermediate Excel (Multiple Linear Regression)
  • ACME Sales [ .xlsx, .csv ]
  • Review of (Simple, single indenpedent variable) Linear Regression
  • Multiple (multiple independent variables) Linear Regression, Diagnostics/Issues
  • Avoiding Multicollinearity
  • Example: [ .xlsx ]
• Lecture: Intermediate Excel (Multiple Linear Regression) (try it on your own)
  • ACME Performance Evaluation [ .xlsx, .csv ]

• Richardson/Watson: Ch. 8, Accounting Analytics
• Library: Maidenberg (OpenTable Data)
• Lecture: Intermediate Excel (Reproducible Spreadsheets)
  • ACME Sales [ .xlsx, .csv ]
  • Using Data Analysis Toolpack
  • When to use built-in functions
  • When to use custom mathematical functions
  • Other aspects of Extraordinarily Well-Documented Spreadsheets
  • Useful functions in Accounting spreadsheets
• Lecture: Intermediate Excel (Reproducible Spreadsheets) (try it on your own)
  • ACME Performance Evaluation [ .xlsx, .csv ]

• (no class, César Chávez Holiday)

• Richardson/Watson: Ch. 9, Financial Analytics
• Library: Cheng (Exponentials)
• Lecture: Intermediate Excel (Financial Functions)
  • ACME Sales [ .xlsx, .csv ]
  • Example: =PV, =NPV [ .xlsx ]
• Lecture: Intermediate Excel (Reproducible Spreadsheets) (try it on your own)
  • ACME Performance Evaluation [ .xlsx, .csv ]
• (contingency)

• Richardson/Watson: Ch. 10, Operations Analytics
• Library: Young (Target Speeds Delivery)
• Lecture: Opimization (Linear Programming)
• Video Clip(s): Big Bang Theory (clip) Optimization - "The Financial Permeability" (Season 2, Disk 3, Episode 14) (YouTube clip) [ .html ]
• Lecture: Excel Solver (Mathematical Optimization/Linear Programming)
  • (SOM 306, Production) Dessert Diet ('Minimization' Problem) Set-up [ .xlsx ]
  • (SOM 306, Production) Dessert Diet ('Minimization' Problem) Solution [ .xlsx ]
  • (MKT 304, Operations) Greek Foods ('Maximization' Problem) Set-up [ .xlsx ]
  • (MKT 304, Operations) Greek Foods ('Maximization' Problem) Solution [ .xlsx ]
  • (FIN 303, Investment Portfolios) (tbd) Set-up
  • (FIN 303, Investment Portfolios) (tbd) Solution
  • (MGT 360, Staffing) Persons-Tasks ('Assignment' Problem) Set-up [ .xlsx ]
  • (MGT 360, Staffing) Persons-Tasks ('Assignment' Problem) Solution [ .xlsx ]
• Library: Levinson (Efficient to a Fault)

• Due: Assignment: Data Analytics Case (Written)
• Richardson/Watson: Ch. 11, Advanced Analytics
• Library: Bousquette (GM Third-Party Apps)
• Lecture: Other Excel Capabilities (Automation/Programming)
  • ACME Sales [ .xlsx, .csv ]
  • Macros (Automatically recorded keystrokes)
  • Visual Basic For Applications (VBA)
  • PowerBI (Excel 'Business Intelligence' addin--slightly better Data Analysis)
• Lecture: Other (Excel) Statistical Methods
  • Analysis of Variance (ANOVA) (hypothesis testing but when there are more than two groups)
  • Time Series/Forecasting (when the x-axis is time)
  • Maps/Geography (set Data Type = 'Geography' for Zip Code or Lat/Lon column)
• Lecture: Other (non-Excel) Statistical Methods
  • Classification analysis (like regression, but the dependent Y variable is a categorial/qualitative variable such as "yes/no")
  • Logistic Regression analysis (like regression, but the relationship between X and Y is a curve not a line)
  • Cluster analysis (segment the rows/observations into distinct categories)
• Lecture: Other (non-Excel) Tools
  • Other GUI Spreadsheets - Google Sheets (part of Google Docs), Apple Numbers (comes with MacOS), LibreOffice (open source, all OSs)
  • Other CSUN Business Analytics GUI Applications - SPSS (students get it free for a year)
  • Other Business Analytics GUI Applications - Tableau Desktop (students get it free for a year)
  • 'Automated' Business Analytics GUI Applications - Alteryx
  • CLI Programming Applications (more academic, more statistical) - R (sample in the web, webr), (download, free download, CRAN)
  • CLI Programming Applications (more business, more general purpose) - Python free download, Anaconda Python
• (contingency)

• Lecture: Presenter Team's Responsibilities
• Supplemental: Requirements for Oral Presentations [ .pdf ]
• Supplemental: Formatting Requirements for Presentation Deliverables [ .pdf ]
• Lecture: Presentation Examples (Powerpoint presentations from my prior BUS 302 classes) [ .pptx ]

• Lecture: Audience Teams' Responsibilities
• Lecture: Discussion ("Challenge") Question Template [ .pdf ]

• (no class - just practice your presentations)
• (contingency)

• Due: Data Analytics Case (Presentation) - Team 1
• Due: Data Analytics Case (Presentation) - Team 2

• Due: Data Analytics Case (Presentation) - Team 3
• Due: Data Analytics Case (Presentation) - Team 4

• Due: Data Analytics Case (Presentation) - Team 5
• Due: Data Analytics Case (Presentation) - Team 6

• Due: Data Analytics Case (Presentation) - Team 7
• Due: Data Analytics Case (Presentation) - Team 8

• Supplemental: Upward Influence [ .pptx ] (Mon. Dec 20, 2021)
• Activity: Formal Team Evaluation
• Activity: Personal Reflection #2
• Learning Excel Through Practice (What Data are You Passionate About?)
  • by Major/Career - Acct (SEC EDGAR, CalcBench), FIN (Google Finance)
  • by Gov't Jurisdiction - LA City, LA County, State of CA, US
  • by Sport - MLS, WNBA, NFL, MLB, et al.
  • in CSUN Library ('Statistical Data') - DataPlanet, US Census, Statista, US Trade Online
  • by Individual Passion - Rescued Dogs, Spayed/Neutured Cats, Entrepreneurship, Latin America, US/Mexico Trade
  • by course next semester - FIN/SOM/MKT, core or elective (get the syllabus/outline/book, try a couple of exercises)
• Upcoming events (What Can You Learn About Data with Others?)
  • 'SQL Saturday' - Sat. June 1, 2024, LMU, free [ .html ]
  • 'Data Con LA' - (date: tbd, usually in August at USC), inexpensive [ .html ]
• (Spring: student evaluations of faculty) (please complete by Fri. May 10 at 11:59pm) [ .html ]
• (contingency)

• Note: Special Time - (8:00am - 9:00am)
• Note: This day and time needs to be verified by each student individually in SOLAR after the beginning of the semester (especially to make sure there are no conflicts).
• Exam: Final Exam