MIS 433: Programming for Analytics Master Syllabus
Course Instructor:
Office Number:
Office Hours:
Email:
Course Meeting Times:
Course Materials
Textbooks - There will be two textbooks to reference free of charge:
- Title: Automating the Boring Stuff with Python
Author: Al Sweigart
Link
- Title: Pandas for Everyone
Author: Daniel Y. Chen
Link: Click on O’Reilly for Higher Education then enter your George Mason email address
Technology - Laptop with internet connectivity, webcam, and microphone. Required software includes:
- Anaconda Individual Edition (free 64-Bit version)
- Respondus Lockdown Browser
- Standard MS Office Suite
Course Description
This course introduces students to solving a broad set of data analysis problems using the Python programming language. The course will cover programming fundamentals including variables, object types, loops, conditional statements, and functions. Next, a series of Python library packages are presented for business analytics which involve data loading, data structures, data manipulation and exploratory data analysis. The last portion of the course introduces geospatial analysis and machine learning techniques which cover prediction models and sentiment analysis.
Related IS Job Functions
The skills and competencies developed in this course may be typically used by data analysts, data scientists, and the like.
Course Learning Goals
Core Competencies and Learning Objectives:
Upon the successful completion of this course, students will be able to:
- Utilize Python for analysis on both quantitative and qualitative data.
- Use Python in conjunction with several analytical library packages.
- Manipulate data and feature engineer raw data.
- Develop complex data visualizations.
- Apply a set of machine learning techniques such as training and testing models.
Course Format
This is an in-person course consisting of lectures, classwork/hands-on practice sessions, homework, quizzes/pop quizzes, and exams. Students are to have their laptop ready at the start of class. There will be modules containing assignments with their corresponding deadlines released weekly throughout the semester. Be prepared for class by reviewing the reading material outlined in the weekly module. Reflect on the programming logic, take notes, and practice coding regularly.
| Category | Percentage Weights |
|---|---|
| Classwork / Participation | 10% |
| DataCamp work | 10% |
| Quizzes | 15% |
| Midterm Exam | 25% |
| Final Exam | 25% |
| Project | 15% |
| Final Grade Scale (%) | |
|---|---|
| A | > 93 |
| A- | 90 to < 92 |
| B+ | 87 to < 89 |
| B | 83 to < 86 |
| B- | 80 to < 82 |
| C+ | 77 to < 79 |
| C | 73 to < 76 |
| C- | 70 to < 72 |
| D | 60 to < 69 |
| F | < 60 points |
Classwork/Participation
There will be hands-on work to complete during class time that may require a Canvas submission. Students must have their laptops ready with the required coding software at the start of every class. Be prepared for topic discussions as well as to answer questions, even if you believe that you do not know the answer.
Students are expected to keep an accessible copy of all their completed work throughout the semester. Late work may be accepted with a 30% grade penalty at the instructor’s discretion. This penalty also applies to students who are not present in class if a Canvas submission for past work is requested.
A considerable amount of time should be spent out of class to aid your learning of the programming logic. This is done by very frequently interacting with small pieces of Python code on a regular basis. In addition to the resources utilized in this course, reviewing material from other sources such as YouTube or LinkedIn Learning is encouraged.
DataCamp
Each student must register for a free educational access to DataCamp.com using the custom link provided in the course’s Canvas page (Module 1). You will receive an automated email message from DataCamp letting you know each time a coding assignment is available for completion. This information will also be announced on Canvas. DataCamp is a great supplemental resource to reference for interactive learning as well as practicing Python.
Within Canvas, go to Module 1 and locate the DataCamp registration link for this course. Then use your @gmu.edu email when creating an account (do not use @masonlive.gmu.edu). Be sure to enter your first name and last name as stated on your University ID. There will be a 30% late penalty for DataCamp assignments passed the deadline.
There will be approximately five quizzes to assess your knowledge in programming and analytics. Three quizzes are listed on the course schedule below while two quizzes will not be announced (i.e., pop quizzes). Come to class prepared as quiz questions come from topics covered from previous class sessions and/or modules. The quiz format is typically multiple-choice via Canvas with Respondus Lockdown Browser. Late penalty if accepted by instructor: 30%. A late penalty may also apply if the student is late to class.
Project
A term project will be assigned towards the end of the semester which will cover most of the general topics outlined in the course schedule below. There will be one week of class time that will be allocated for you to work on your program, troubleshoot errors, and ask questions. A considerable amount of time outside of class will be needed to complete this assignment. More details will be provided later in class and/or on Canvas. Late project penalty if accepted: 30%.
Exams
There will be a total of two exams. The midterm exam will consist of topics covered from Week 1 to Week 5, and the final exam will be comprehensive/cumulative. The format will be open answer and multiple-choice questions. A portion of the exam will require that you use Respondus Lockdown Browser. Late exam submissions will not be accepted.
MIS (Management Information Systems) Learning Goals
- Apply knowledge of information technology, operations, and business functions to assess, design and improve business processes.
- Effectively manage projects, including information technology projects.
- Understand the overall systems development life cycle and be able to recommend IT solutions.
BS Business Learning Goals
- The social, global, ethical, and legal contexts of business and will be able to reflect on the role of the individual in business.
- The ability to apply knowledge of professional skills necessary for success in business including effective business writing.
- Technical and analytic skills appropriate for success in business.
- The ability to apply knowledge of core business disciplines including accounting, finance, information systems, management, marketing, and operations management.
- How research in the business disciplines contributes to knowledge and how such research is conducted.
Course Schedule (Tentative)
| Week | Module | |
|---|---|---|
| 1 | 1 | Course intro; software setup; IDE interaction; Hello World; DataCamp |
| 2 | 2 | Python basics: Variables; Types; Operations; Built-in Functions & Methods |
| 3 | 3 | Python basics: Loop; Control statements |
| 4 | 4 | Python basics: User-defined Functions; Built-in Functions; Methods |
| 5 | 5 | Python data analysis: Series, DataFrames, Descriptive Analytics || Quiz |
| 6 | Midterm Exam | |
| 7 | 6 | Python data analysis: Data Visualization; Descriptive Analytics |
| 8 | Spring Recess (no class) | |
| 9 | 6 | Python data analysis: Data Visualization; Descriptive Analytics |
| 10 | 7 | Python data analysis: Data Manipulation || Quiz |
| 11 | 8 | Python data analysis: Machine Learning – Supervised Modeling; Regression |
| 12 | 8 | Python data analysis: Machine Learning – Supervised Modeling; Regression & Classification |
| 13 | 9 | Python data analysis: Machine Learning - Text analytics; Sentiment Analysis |
| 14 | 10 | Python data analysis: Geospatial Analysis |
| 15 | Project || Quiz | |
| 16 | Final exam |
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