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Data and Analytics

Department of Management Information Systems, Temple University

Data and Analytics

MIS 2502.701 ■ Spring 2025 ■ Jaclyn Hansberry
  • Announcements
  • Schedule
  • About
    • Course Description
    • Course Details
    • Software Installation
    • Evaluation and Grading
    • PRO Points Requirement
    • Getting Help
    • Instructor
    • ITA

Schedule

 

WEEK DATES TOPIC TASKS DUE MATERIALS
1 1/16 – Course Introduction and Syllabus
– The Things You Can Do with Data
– The Information Architecture of an Organization
 
  • Course Introduction
  • Things You Can Do with Data
  • Information Architecture
    Understanding Database Schemas: Normalization, primary/foreign keys, joins  
  • Relational Data Modeling
2 1/23 In-class exercise #01: Creating database schema
  • Install MySQL Workbench
  • If it crashes on a Mac machine, try an earlier version
  • Alternatively, use virtual machine: OWLab
  • ICA#1: Task
  • Submit on Canvas
    Getting data out of RDMS: SQL SELECT, DISTINCT MIN, MAX, COUNT, and WHERE  
  • PowerPoint: SQL 1
3 1/30

In-class exercise #02: Pen and Paper exercise

Assignment 1: Database schema

  • Task
  • Answer Sheet to fill in 
  • Submit on Canvas
  • ICA #2: Task
  • ICA #2: Submit on Canvas
    Getting data out of RDMS: Joining tables  
  • PowerPoint: SQL 2
4 2/6 In-class exercise #03: Working with SQL, part 1

Assignment 2: SQL #1

  • Task
  • Moviedb schema
  • Answer Sheet to fill in
  • Submit on Canvas
  • ICA #3: Task
  • ICA#3 moviedb schema 
  • Submit ICA #3 on Canvas
    In-class exercise #04: Working with SQL, part 2  
  • ICA #4: Task
  • ICA #4: Submit on Canvas
5 2/13 Review for Exam 1

Assignment 3: SQL #2

  • Task
  • Submit on Canvas
  • Exam Review Slides
  • Study Guide
  • Practice Quiz for Proctorio Installation 
   

Introduction to Python

In-class exercise #05: Getting familiar with Jupyter, Python Basic, Data types

  • Install Jupyter and Anaconda
  • ICA #5: Notebook
  • ICA #5: Submit on Canvas
6 2/20

Exam 1

    Exam during first half of class

 

   

Python Data Structures

In-class exercise #06: Python Lists and Dictionaries

 
  • ICA #6: Notebook
  • ICA #6: Submit on Canvas
7 2/27

Python Data Structures

In-class exercise #06: Python Lists and Dictionaries

 

 

    • ICA #6: Notebook
    • ICA #6: Submit on Canvas
9 3/13

Semi-structured Data Lecture

In-class exercise #07: Working with semi-structured data

Python and JSON

In-class exercise #08: Working with JSON in Python

Assignment 4:  Python Basics

  • Notebook
  • Submit on Canvas
  • Semi-structured Data Slides
  • ICA #7: Task
  • ICA #7: Submit on Canvas
  • ICA #8: Task
  • Data: airbnb
  • ICA #8: Submit on Canvas
   

 

   
   

 

 

 

10 3/20

Python Pandas

In-class exercise #09: Working with Python Pandas

In-class exercise #10: More practice with Pandas

Reconciling Data: The extract, transform, load process (ETL)

ETL Lecture Recording now posted on Canvas via Zoom>Cloud Recordings

Assignment 5: Python and JSON

  • Notebook
  • Data
  • Submit on Canvas

 

View recording before next class on 3/27

  • ICA #9: Task
  • Data
  • ICA #9: Submit on Canvas

  • PowerPoint: ETL
  • ICA #10: Task
  • Data
  • ICA #10: Submit on Canvas
   

Principles of Data Visualization

Lecture Recording

 

View recording before next class on 3/27

  • PowerPoint: Data Visualization
11 3/27

Principles of Data Visualization

In-class exercise #11: Data Visualization

Hypothesis Testing

In-class exercise #12: Hypothesis Testing and Visualization in Python

Assignment 6: Pandas

  • Notebook
  • Data
  • Submit on Canvas
  • ICA #11: Task
  • Data
  • ICA #11: Submit on Canvas
  • PowerPoint: Hypothesis Testing
  • ICA #12 Notebook, NBA, Sales
  • ICA #12: Submit on Canvas
    Review for Exam 2  
  • Study Guide
12 4/3 Exam 2  

 

   

 

 

 

13 4/10

Introduction to Advanced Analytics and Python

Classification using Decision Trees

In-class exercise #13: Decision Trees in Python

 
  • PowerPoint: Advanced Analytics – Introduction
  • PowerPoint: Classification using Decision Trees
  • ICA#13 Task, Notebook, Data,
    Answer Sheet to fill
  • ICA #13: Submit on Canvas
     

Assignment 7: Decision Trees

  • Task
  • Data
  • Answer Sheet to fill
  • Submit on Canvas

 

14 4/17

Analysis Scenario: Identifying similar customers (clustering and segmentation)

In-class exercise #14: Clustering and Segmentation in Python

 
  • PowerPoint: Clustering and Segmentation
  • ICA #14: Task, Notebook, Data
  • ICA #14: Submit on Canvas
   

 

Assignment 8: Clustering

  • Task
  • Data
  • Answer Sheet to fill
  • Submit on Canvas
  • PowerPoint: Association Rule Mining
  • ICA #15: Task
  • ICA #15: Submit on Canvas
15 4/24

Analysis Scenario: What products are purchased together? (Association Rules)

In-class exercise #15: Computing Confidence, Support, and Lift

In-class exercise #16: Computing Confidence, Support, and Lift

 
  • ICA #16: Task, Notebook, Data
  • Output [in case Python did not work]
  • ICA #16: Submit on Canvas
     

Assignment 9: Association Rules

  • Task
  • RulesFile
  • Submit on Canvas
  • Exam Review Slides
  • Student Feedback Forms
16  

Exam 3 Review

 

Final Exam 5/1 5:45 – 7:45

PRO points project 

  • Task
  • Submit on Canvas
  • Follow the instructions in the Task document to request points
  • Exam 3 Study Guide
  • Exam 3 Review Slides 

 

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Useful Links

  • Canvas Course
  • Discord
  • MasteryGrids
  • Software Installation

Quick Info

  • Time and Locations: 5:30 pm – 8:00 pm, Thursday via Zoom
  • Instructor: Jaclyn M. Hansberry, jaclyn.hansberry@temple.edu
  • Office Hours: Monday 4 pm-5 pm; Zoom
  • ITA: Abby Dorotov tup46046@temple.edu

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