MSBA 618: Programming for Business Analytics Master Syllabus
Course Instructor:
Office Number:
Office Hours:
Email:
Course Meeting Times:
Required Course Materials:
- Required Textbooks: There will be two free textbooks to reference:
- Automating the Boring Stuff with Python
Author: Al Sweigart - Pandas for Everyone: Python Data Analysis, 2nd Edition Author: Daniel Y. Chen Free on O’Reilly Learning Platform through GMU Library: (1) access e-Books@Mason; (2) click O’Reilly Learning Platform (Safari); (3) login using your Mason credentials; (4) once successful, you can search for “Pandas for Everyone” or directly access the book here
- Automating the Boring Stuff with Python
- Required Software/Hardware:
- As this is a hands-on focused course, you will need a functional laptop with internet connectivity and Anaconda for every class. Free Anaconda distribution for individual use is available here. You need to have Anaconda installed and ready to run on your laptop before the 1st day of the class. General instructions for installing the software will be posted on the course site. In addition, Microsoft Office Suite will be needed.
- In the class, the instructor will demonstrate using Windows version of Anaconda. Please recognize that the software interface may look different on MAC computers/laptops. Due to time and resource constraints, support for the use of MAC devices will be limited. If you use a MAC device, it is your responsibility to make sure you follow up and your assignment submissions are compatible with Windows version of Anaconda.
Course Website:
Course Description
This course introduces students to solving a broad set of data analysis problems using the Python programming language. The course will cover Python programming fundamentals such as variables, object types, loops, conditional statements, and functions. The course will also help students learn to use a series of Python library and packages for business analytics that involve data manipulation, descriptive analysis, and data visualization. Finally, the course will introduce students to basic machine learning techniques such as prediction models.
Learning Objectives
Upon successful completion of this course, students will be able to:
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demonstrate basic Python programming skills.
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use Python to develop descriptive statistics & data visualizations to aid business decision-making.
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use Python to manipulate business data and feature engineer raw data.
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use Python to apply machine learning techniques to solve business problems.
MSBA Learning Goals
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Ethical Decision Making: Understand, apply, and evaluate ethical data practices when collecting, analyzing and sharing data to improve the decision-making process in business.
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Fundamental Analytics Techniques and Skills: Demonstrate an in-depth understanding of the most commonly used analytics techniques.
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Identification and Application of Analytics: Apply analytics to different functional areas of business and assess its impact.
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Collaboration and Communication Skills: Effectively interact with clients, and communicate data-driven insights to them.
Course Deliverables
| Deliverable | Weight |
|---|---|
| Homework (group work, 5*13%) | 65% |
|
Exam (individual work) |
25% |
|
Midterm Exam |
10% |
| Total | 100% |
Deliverable 1: Homework (65%; group work)
There will be five homework assignments testing your programming and data analytics skills using Python. Each homework will be worth 13% of your final course grade, for a total of 65%. These are group efforts, and you will work in a group of 2 to 3 students to complete each homework. You will have opportunity to form your own group of 2-3 students and communicate it to the designated discussion board on the course site by a designated deadline. After the deadline, students not in a group will be randomly assigned to one by the instructor. Towards the end of the class, you will complete an online peer evaluation to evaluate each of your teammates’ contribution to the group-based homework (not per homework, but all homework as a whole). Members who do not make due contributions to the group-based homework will have their individual scores for the homework adjusted down from the group score.
Deliverable 2: Exam (25%; individual work)
There will be an exam requiring you to use the programming and analytics skills you have learned in the class. The exam is like a mini project, you will be given a dataset and a set of prompts, and you will need to use Python to conduct appropriate analyses and complete required tasks. The exam is a take-home exam.
Deliverable 3: Class Engagement (10%; individual work)
All students are expected to engage in the classroom throughout the course. There are three components of class engagement.
(1) Self-Introduction (1%): for effective class engagement and to help you know your classmates (which may help you find your teammates for the group-based homework), you will be asked to write an introductory blog post about yourself on the course site during the beginning week of the class. Detailed instructions will be posted on the course site.
(2) DataCamp Course (4%): to help you reinforce the basic Python programming skills for this course, you will be required to complete a free and interactive online course “Introduction to Python” on DataCamp. DataCamp is a great supplemental resource to reference for interactive learning as well as practicing Python. You will directly complete it on DataCamp (shown as assignments on DataCamp). You need to use your GMU email and your name as stated on your University ID to create an account and join the group for this class on DataCamp. You need to complete Chapter 1 Python Basics by the start of Week 2 (1% of your grade), and complete the entire course including all 4 chapters by the start of Week 5 (3% of your grade), and you are encouraged to complete the course earlier. Note: you should spend time outside of class to learn programming logic if you have limited prior experience with programming. There are plenty of other online resource (e.g., W3Schools, learnpython.org, etc.)
(3) Class Participation (5%): As this is a fast-paced programming focused class, every single class is important, and you are expected to participate in in-class practices and exercises for every class. Each class period is also an opportunity for you to help each other and make contributions by asking relevant questions, making thoughtful comments, and bringing your ideas and thoughts to the classroom. In addition, all students are expected to do the peer evaluation for group-based homework. Not completing this activity by the designated deadline may negatively affect your participation score. In addition, all students should conduct themselves in a professional manner. This includes factors such as coming to class on time every day, making sure not to disrupt the learning environment (e.g., by leaving early, forgetting to vibrate your cell phone, bursting out in class, etc.), being respectful of others in the classroom, and handling all course-related communication in a professional manner. Unprofessional behavior will be viewed negatively and may negatively affect your class participation score.
Grading Policies
Due Date
(1) For each deliverable, if there is a class on the due date, the deliverable is due by the beginning of the class; if there is no class on the due date, the deliverable is due by the end of day on the due date. See the course site for specific deliverables and due dates; (2) NO late submissions for group-based homework are accepted with one exception: each group has 1 and only 1 chance to extend 1 and only 1 homework’s due date to the end of the next day without asking (the submission time of the homework on the course site will be used to automatically determine if a group has used up the chance or not); (3) NO exceptions will be made if you miss the deadline of an individual work-based deliverable unless you have legitimate unusual circumstances (e.g., medical emergency) and the instructor has approved alternative arrangements for you in advance (i.e., you have requested it in advance with sufficient supporting documents, and the instructor has approved your request and provided you with a new due date and time). The decision will be at the sole discretion of the instructor.
Re-grading Request
If you have a question about your grade on any of the deliverables or you believe that you were graded incorrectly, please submit a formal request in the written form describing the situation and the reasons that justify your request for re-grading. You should also submit any supporting evidence that helps justify your regrading request. In this case, the instructor will go through your re-grading request and decide if your request is legitimate or not. The decision regarding a re-grading request will be at the sole discretion of the instructor. If your request is accepted, the instructor will re-grade your deliverable, and the grade may go up or down. This grade will be final. You have one week from the date the grade is returned to submit a written request for re-grading or review. After one week, no changes will be considered.
Course Letter Grade
Grading for the course will be based on total points earned and will be as follows.
| A+ | 98%-100% | B+ | 88%-89.99% | C | 70-79.99% |
| A | 93%-97.99% | B | 83%-87.99% | F | below 70% |
| A- | 90%-92.99% | B- | 80%-82.99% |
Tentative Schedule
Note: This schedule is subject to change; any change will be duly communicated in class and/or on Course Site.
| Week | Topics | Deliverables |
| 1 |
Course Intro; Python Basics |
Self-Introduction |
| 2 |
Python Basics |
Chapter 1 Python Basics of DataCamp Course |
| 3 | Data Analytics: DataFrame, Descriptive Statistics |
HW #1 |
| 4 | Data Analytics: Descriptive Statistics, Data Visualization |
HW #2 |
| 5 | Data Analytics: Data Visualization, Data Manipulation |
HW #3 DataCamp Course (all chapters) |
| 6 | Machine Learning | HW #4 |
| 7 | Machine Learning | HW #5 Peer evaluation |
| 8 |
Machine Learning; take-home exam |
|
*: dates without a class held; deliverables due by the end of day.
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