ISYE 6501 Intro to Analytics Modeling: Achieving Success with a Comprehensive Review and Tips
A Comprehensive Review and Tips for Success in Georgia Tech’s OMSCS Course: ISYE 6501 Intro to Analytics Modeling
Background
In order to give a more accurate and informative review of the course, it’s important to provide some context about my background. Omscentral is a great resource for student reviews, but it doesn’t always include information about the reviewers’ backgrounds and experiences. Since Master’s students come from a variety of backgrounds, this can affect their perspectives and evaluations of the course. That’s why I want to share my own background and skillset, which includes over four years of software engineering experience, proficiency in Python and Jupyter Notebook, and extensive coding experience.
Course Structure
Homework:
- 15 week course materials to cover
- Every week Homework and 3 Peer Reviews are due
- Homework 1 to 11: Coding projects with R and Python + Report
- Homework 11–14: Case studies
Exams:
- Midterm 1: Covers content from Week 1 to 7
- Midterm 2: Covers content from Week 8 to 11
- Final Exam: Covers content from Week 1 to 15
Course Project:
- A case study project worth 8% of your grade
You are allowed 1 cheat-sheet (front and back) each for Midterm 1 and 2 and 2 cheat-sheets for Final Exam. All exams utilize multiple-choice questions (MCQ) format.
Course Review
Dr. Sokol has instantly became one of my favorite professors in the program due to his lecture delivery style. Unlike in some courses where people laugh at the professors for their cringe-y jokes, you get to laugh with him. The lectures are truly of high quality.
The course materials are broken logically down into modules and spread across multiple weeks. Although most of the content seem easily digestible and retainable, do not let that fool you. Exams test your analytical ability with a lot of in-depth understanding of the material not covered in the lectures itself. So, expect a lot of breadth in lectures with little depth. It is a good course for someone completely new to the Analytics world (hence, Intro to Analytics Modeling), but does not do justice to those that come with knowledge in this field.
If you have little programming experience, you are going to struggle a lot. Since the first few assignments throw you in the deep end of R language. But worry not, if you attend the Office Hours, you will learn to swim through the assignments since they walk you through most of the assignments in code.
The exams are going to be extremely challenging for those who does not have a good grasp in English since the exams’ wordings can be tricky/poorly worded at times. Also note, for both the Midterms, knowledge from homework is not referenced. So, if you think you will ace your Midterms because you have 100s in your homework, you are up for a very bitter surprise.
All the homework, including final course project, were Peer Graded. Instead of taking a mean of 3 Reviewer’s grades, they take a median, which seemed fair.
One of my biggest pet-peeves is using Piazza for this course. A lot of the other OMSCS/OMSA courses have migrated to Ed but this course still uses Piazza. That being said, Piazza and Slack can be some of your greatest sources of information and clarification for projects. The TA(s) are incredibly helpful and prompt! That helps a lot in getting timely help for assignments.
Review Breakdown:
- Difficulty: 3/5
I believe although the course itself was not hard, the exams were difficult. Specially because a lot of contents covered in lectures were in surface level where as in the exams, they were in-depth.
- Workload: 3/5
I took this course because I needed an easier course for this semester. Although the assignments are not hard (speaking as a programmer), it is definitely time-consuming. That being said, I enjoyed the assignments the most. I got to learn so much. Prior knowledge of Python really helped in picking up R quickly.
- Rating: 4/5
I personally feel like I have learned a lot. Specially, since I had to also force myself to learn outside of my class and from peers. It was a great introductory course and I am glad I took it. But it was not an easy-A.
My Tips
- If you had to choose between projects and studying for exam, ALWAYS choose studying for exams for this course since 75% of the entire course grade depends upon it.
- Try to learn R ahead of this course and get familiar with R Markdown. 90% of the programming assignments (except 1 or 2) were in R.
- Do not procrastinate in this course. You will fall behind hard and fast.
- Do not overthink your answer in exams and overcomplicate it. If you use the KISS methodology in your thought process, you will do fairly well in the exams.
- Exams have a lot of gotcha questions so, always re-read the questions.
- Do not fret too much about the homework assignments. This course does a good job of holding office-hours for students on Mondays and Thursdays. I have consistently attended the Mondays office hours since they give a lot of the skeleton code for the assignment.
- Do not miss out on peer reviews since if you do, you get an instant 0 in your homework even if you turned it in on-time.
- Learn R Markdown and use it in your assignments from the get-go. It truly saves time from needing to import the figures and formatting it into a Word Document. I wasted few of my earlier weeks doing that and I deeply regret it.
- Pace yourself. It is definitely not a hard course but it is a very busy course.
- Peer reviews give an amazing opportunity to learn from peers and get a peek inside their analytical thought-process. Take advantage of that and truly read your peers’ reports every week. That being said, grade graciously.
Here’s the course information in the official website: https://omscs.gatech.edu/isye-6501-intro-analytics-modeling
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Other related blogs:
- Helpful homework resources: Helpful Resources for ISYE 6501: Intro to Analytics Modeling — Georgia Tech’s OMSCS
- Helpful exam strategy to secure an A in the course: Exam Strategy for ISYE 6501: Intro to Analytics Modeling — Georgia Tech’s OMSCS
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