| Eren Bilen | |
|---|---|
| bilene@dickinson.edu | |
| Office | Rector North 1309 |
| Office Hours | calendly M 9:00-10:30am, Th 10:20-11:50am |
| GitHub | ernbilen |
- Meeting day/time: T-Th 9:00-10:15am, Tome 232
- Office hours also available by appointment.
This capstone course aims to provide students a strong foundation for the must-have skills needed towards becoming a successful and ethical data analyst. The course must be completed successfully as the final core requirement for degree in data analytics. During the course, we will revisit topics that you have learned in your prior core data analytics courses. By the end of the course, you will produce a clear output: a data science project showcasing your data analysis skills. You should consider this course and the project as an opportunity to practice your oral presentation, writing, and time management skills. Additionally, we will discuss strategies that will help you navigate the job market for data analytics.
- You will learn how to implement a data science project under a feasible timeline. In the process, you will learn skills such as:
- Develop research questions systematically that are feasible to implement in a reasonable timeline
- Evaluate what makes a research question good or bad
- Distinguish correlations from causality, outline methodology and concisely explain results, and characterize the limitations and uncertainty of statistical inference and machine learning algorithms
- Develop the ability to write about and express an opinion on an ethical issue in data analytics for a broad general and/or technical audience
- You will learn how to use software to increase your research productivity and learn coding and collaboration techniques such as:
- Best practices for Python coding (PEP 8)
- Writing modular code with functions and objects
- Creating clear docstrings for functions and variables
- Collaboration tools for writing code using Git and GitHub.com.
- You will get prepared for the job market in data analytics:
- You will have a well prepared application package prepared including a clean resume, data/projects repo showcasing your skills
- Revisit technical concepts you have learned in your previous data analytics courses
Grades will be based on the categories listed below with the corresponding weights.
| Assignment | Points | Percent |
|---|---|---|
| Research Questions | 15 | 15.0% |
| Proposal presentation | 15 | 15.0% |
| Progress report | 5 | 5.0% |
| Progress presentation | 10 | 10.0% |
| Data + ReadMe guide | 15 | 15.0% |
| Poster session | 15 | 15.0% |
| Final presentation | 15 | 15.0% |
| Class participation | 10 | 10.0% |
| Total points | 100 | 100.0% |
- Assignments: Your assignments will be submitted through Github. Each of you will maintain an individual Github repo where you upload your reports, presentations, data guide, and code. This will be useful when you apply for data analyst jobs.
- You are encouraged to work in teams of two. However, you must understand and be able to explain all parts of the code you are submitting. I DO want to see each of you learning how to code solutions so that you could do it later on your own.
- Your assignments, both written and code portions, will be turned in via a pull request from your private GitHub.com repository which is a fork of the class master repository on my account. (You will need to set up a GitHub account if you do not already have one.)
- Due dates for each assignment will be announced. Late assignments will not be graded.
- The Effect by Nick Huntington-Klein
- LaTeX Cheat Sheet and an excellent tutorial by Dave Richeson
- QuantEcon
- Notes on Machine Learning & Artificial Intelligence by Chris Albon
If you have any condition, such as a physical or learning disability, which will make it difficult for you to carry out the work as I have outlined it or which will require academic accommodations, please notify me through email with the appropriate documentation during the first two weeks of the course.