MBA 8045 (Analytics Experience)
Fall 2023 Updated on Sep 9, 2023 Section 4 (CRN: 89433): Thursdays 7:15 pm - 10:00 pm
Email Office Hours Link Saber Soleymani (instructor) ssoleymani@gsu.edu Mondays 1:00 - 2:00 pm or by appointment Webex Meetings Sai Roopesh Mandava (Teaching Assistant) smandava4@student.gsu.edu Thursday 2:00 - 3:00 pm Webex Meetings
Course Description
The Analytics Experience allows students to work on real-world problems and apply analytical techniques to answer diverse questions. The course provides an overview of current challenges and solutions in data analytics. Given a business situation and data, students will learn how to solve a problem and acquire insight from data with techniques they have learned.
Learning Objectives
By the end of the course, students should be able to:
- Explain descriptive, exploratory, predictive, and prescriptive data analysis.
- Explain various data structures and their use cases.
- Explain ETL (Extract, Transform, Load) techniques, challenges, and solutions.
- Explore, visualize, and modify structured datasets.
- Identify the appropriate type of analysis based on the problem.
- Describe the process of building supervised predictive models.
Textbooks
- Paczkowski, Walter R. Business Analytics: Data Science for Business Problems. Springer Nature, 2022.
- Wes McKinney Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter. O’Reilly Media, 2022. Available here
Note: This course does not require the purchase of a textbook.
Weekly Schedule
| Date | Week number | Location | Agenda |
|---|---|---|---|
| 08/24 | Week 1 | In-person | Course Introduction, Data Taxonomy, & Python basics |
| 08/31 | Week 2 | In-person | Big Data Challenges & Descriptive Data Analysis |
| 09/07 | Week 3 | Online | Visualization and Exploratory Data Analysis (EDA) |
| 09/14 | Week 4 | In-person | Predictive Data Analysis (part I) |
| 09/21 | Week 5 | In-person | Predictive Data Analysis (part II) |
| 09/28 | Week 6 | Online | Predictive Data Analysis (part III) |
| 10/5 | Week 7 | Online | Final Project Presentation |
Location:
- In-person: Buckhead Center, Room 548
- Online: click on the Webex menu in iCollege to join the class.
The weekly schedule and deadlines are tenative and subject to change. Please check the updates on iCollege.
Grading Policy
Letter Grade Breakdown
| A+ (97-100+) | B+ (87–89.4) | C+ (77–79.4) | D (59–69.4) |
| A (91–96.9) | B (83–86.9) | C (72–76.9) | F (0–59.9) |
| A- (89.5–90.9) | B- (79.5 –82.9) | C- (69.5 –71.9) |
Points Breakdown
| Points | Assignment | Due Date |
|---|---|---|
| 15 | Reading Reflection 1 | Due [08/30] |
| 20 | Reading Reflection 2 | Due [09/20] |
| 5 | Project Proposal | Due [09/18] |
| 15 | Quiz | From [09/28] to [10/4] |
| 10 | Project Presentation | On [10/5] |
| 25 | Project Document | Due [10/6] |
| 10 | Participation & Discussions | - |
| 10 | Bonus Assignment | Due [10/9] |
Course Policies
Attendance
Students are expected to attend all in-person and online classes to gain the full benefit of the course. However, missing one class without explanation is accepted for this course. If you are unable to attend a class, you need to study the materials of the class you missed and engage more in discussions in future classes. If you miss more than one class, you should explain the reason for your absence in the form at https://forms.gle/QrWT62pXTiFqqt8A8
Make-up Quizzes
The lowest quiz grade will be dropped. If you miss a quiz, that will be considered as your lowest grade. In case of an emergency, you should explain the reason in the form at https://forms.gle/QrWT62pXTiFqqt8A8 by selecting the “missing a class” option.
Late/Missed Work
Assignments that are turned in late are subject to a late penalty. 20% deduction will be applied to the submission which is submitted late for up to 48 hours after the deadline. After 48 hours, you will automatically get Zero. If you have a valid reason for missing a deadline, you should explain the reason in the form at https://forms.gle/QrWT62pXTiFqqt8A8
Email Policy
As instructors can see more information about students and their enrolled sections, it is preferable to use iCollege’s messaging system rather than emails. For questions regarding grades, please email the TA and CC the instructor.
Collaboration Policy
All work is expected to be your own for each exam, quiz, homework, and project. No collaboration is allowed unless otherwise stated in the instructions. If you use a paragraph or code from the internet or generative AI tools, you should cite that resource.
Academic Honesty
We must abide by GSU’s Academic Honesty Policy.
Laptop & Technology Statement
You will need a computer to do the assignments. We will be using e-devices in different capacities in the class. Please make sure to silence your e-devices during the meetings.
Online Class Etiquette
Designate Your Workspace
- Treat each online session as if you were present in class.
- Mute your microphone when it is not in use.
- Identifying the speaker is sometimes difficult, especially when the screen is shared. Let others know your name whenever you start speaking.
Syllabus Policy
This course syllabus is a general course plan; the instructor’s deviations may be necessary. The syllabus may be updated throughout the course, so you should check iCollege to ensure you are reading the latest version. I will send an announcement to the class if there has been a major change in the syllabus.
Your constructive assessment of this course plays an indispensable role in shaping education at Georgia State. Upon completing the course, please take the time to fill out the online course evaluation.