{"id":22,"date":"2018-01-17T14:36:08","date_gmt":"2018-01-17T19:36:08","guid":{"rendered":"http:\/\/community.mis.temple.edu\/mis3580sec001s2018\/?page_id=22"},"modified":"2025-04-25T10:51:19","modified_gmt":"2025-04-25T14:51:19","slug":"schedule","status":"publish","type":"page","link":"https:\/\/community.mis.temple.edu\/mis2502sec701spring2025\/","title":{"rendered":"Schedule"},"content":{"rendered":"

 <\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
WEEK<\/th>\nDATES<\/th>\nTOPIC<\/th>\nTASKS DUE<\/th>\nMATERIALS<\/th>\n<\/tr>\n<\/thead>\n
1<\/td>\n1\/16<\/td>\n– Course Introduction and Syllabus
\n– The Things You Can Do with Data
\n– The Information Architecture of an Organization<\/td>\n		\u00a0<\/td>\n\n
\u00a0<\/td>\n\u00a0<\/td>\nUnderstanding Database Schemas: Normalization, primary\/foreign keys, joins<\/td>\n\u00a0<\/td>\n\n
2<\/td>\n1\/23<\/td>\nIn-class exercise #01: Creating database schema<\/strong><\/td>\n\n\n
\u00a0<\/td>\n\u00a0<\/td>\nGetting data out of RDMS: SQL SELECT, DISTINCT MIN, MAX, COUNT, and WHERE<\/td>\n\u00a0<\/td>\n\n
3<\/td>\n1\/30<\/td>\n\n

In-class exercise #02: Pen and Paper exercise<\/strong><\/p>\n<\/td>\n

\n

Assignment 1: Database schema<\/p>\n

\n
\u00a0<\/td>\n\u00a0<\/td>\nGetting data out of RDMS: Joining tables<\/td>\n\u00a0<\/td>\n\n
4<\/td>\n2\/6<\/td>\nIn-class exercise #03: Working with SQL, part 1<\/strong><\/td>\n\n

Assignment 2: SQL #1<\/p>\n

\n
\u00a0<\/td>\n\u00a0<\/td>\nIn-class exercise #04: Working with SQL, part 2<\/strong><\/td>\n\u00a0<\/td>\n\n
5<\/td>\n2\/13<\/td>\nReview for Exam 1<\/td>\n\n

Assignment 3: SQL #2<\/p>\n

\n
\u00a0<\/td>\n\u00a0<\/td>\n\n

Introduction to Python<\/p>\n

In-class exercise #05: Getting familiar with Jupyter, Python Basic, Data types<\/strong><\/p>\n<\/td>\n

\n\n
6<\/td>\n2\/20<\/td>\n\n

Exam 1<\/p>\n<\/td>\n

\n
    Exam during first half of class<\/ul>\n<\/td>\n
\n

 <\/p>\n<\/td>\n<\/tr>\n

\u00a0<\/td>\n\u00a0<\/td>\n\n

Python Data Structures<\/p>\n

In-class exercise #06: Python Lists and Dictionaries<\/strong><\/p>\n<\/td>\n

\u00a0<\/td>\n\n
7<\/td>\n2\/27<\/td>\n\n

Python Data Structures<\/p>\n

In-class exercise #06: Python Lists and Dictionaries<\/strong><\/p>\n

 <\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n
9<\/td>\n3\/13<\/td>\n\n

Semi-structured Data Lecture<\/a><\/p>\n

In-class exercise #07: Working with semi-structured data<\/strong><\/p>\n

Python and JSON<\/p>\n

In-class exercise #08: Working with JSON in Python<\/strong><\/p>\n<\/td>\n

\n

Assignment 4: \u00a0Python Basics<\/p>\n

\n
\u00a0<\/td>\n\u00a0<\/td>\n\n

 <\/p>\n<\/td>\n

\u00a0<\/td>\n\u00a0<\/td>\n<\/tr>\n
\u00a0<\/td>\n\u00a0<\/td>\n\n

 <\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n<\/tr>\n

10<\/td>\n3\/20<\/td>\n\n

Python Pandas<\/p>\n

In-class exercise #09: Working with Python Pandas<\/strong><\/p>\n

In-class exercise #10: More practice with Pandas<\/strong><\/p>\n

Reconciling Data: The extract, transform, load process (ETL)<\/p>\n

ETL Lecture Recording now posted on Canvas via Zoom>Cloud Recordings<\/p>\n<\/td>\n

\n

Assignment 5: Python and JSON<\/p>\n

\n
\u00a0<\/td>\n\u00a0<\/td>\n\n

Principles of Data Visualization<\/p>\n

Lecture Recording<\/p>\n

 <\/p>\n<\/td>\n

\n

View recording before next class on 3\/27<\/p>\n<\/td>\n

\n
11<\/td>\n3\/27<\/td>\n\n

Principles of Data Visualization<\/p>\n

In-class exercise #11: Data Visualization<\/strong><\/p>\n

Hypothesis Testing<\/p>\n

In-class exercise #12: Hypothesis Testing and Visualization in Python<\/strong><\/p>\n<\/td>\n

\n

Assignment 6: Pandas<\/p>\n

\n
\u00a0<\/td>\n\u00a0<\/td>\nReview for Exam 2<\/td>\n\u00a0<\/td>\n\n
12<\/td>\n4\/3<\/td>\nExam 2<\/strong><\/td>\n\u00a0<\/td>\n\n

 <\/p>\n<\/td>\n<\/tr>\n

\u00a0<\/td>\n\u00a0<\/td>\n\n

 <\/p>\n<\/td>\n

\u00a0<\/td>\n\n

 <\/p>\n<\/td>\n<\/tr>\n

13<\/td>\n4\/10<\/td>\n\n

Introduction to Advanced Analytics and Python<\/p>\n

Classification using Decision Trees<\/p>\n

In-class exercise #13: Decision Trees in Python<\/strong><\/p>\n<\/td>\n

\u00a0<\/td>\n\n
\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\n

Assignment 7: Decision Trees<\/p>\n

\n

 <\/p>\n<\/td>\n<\/tr>\n

14<\/td>\n4\/17<\/td>\n\n

Analysis Scenario: Identifying similar customers (clustering and segmentation)<\/strong><\/p>\n

In-class exercise #14: Clustering and Segmentation in <\/strong>Python<\/strong><\/p>\n<\/td>\n

\u00a0<\/td>\n\n
\u00a0<\/td>\n\u00a0<\/td>\n\n

 <\/p>\n<\/td>\n

\n

Assignment 8: Clustering<\/p>\n

\n
15<\/td>\n4\/24<\/td>\n\n

Analysis Scenario: What products are purchased together? (Association Rules)<\/p>\n

In-class exercise #15: Computing Confidence, Support, and Lift<\/strong><\/p>\n

In-class exercise #16: Computing Confidence, Support, and Lift<\/strong><\/p>\n<\/td>\n

\u00a0<\/td>\n\n
\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\n

Assignment 9: Association Rules<\/p>\n

\n
16<\/td>\n\u00a0<\/td>\n\n

Exam 3 Review<\/strong><\/p>\n

 <\/p>\n

Final Exam 5\/1 5:45 – 7:45<\/strong><\/p>\n<\/td>\n

\n

PRO points project\u00a0<\/p>\n

\n