have gotten a taste of what it takes to be a data professional. Now you get a
chance to put it all together! Your Final Project is to choose a subject area (domain)
that you are interested in and conduct your own data project in that area!
project is largely focused on the process, and less on the findings. I will be
looking for your adherence to the process of conducting a data analytic
project, as well as your reflections on what you learned from going through the
a domain area of research
FORMULATE a problem statement & hypothesis. describe the problem
in detail you wish to explore.
the question(s) according to your domain
a. Understand A Business
b. Understand A Stakeholders
data for your project
the Data: Information about the dataset itself, e.g., the attributes and
attribute types, the number of instances, your target variable.
the data, this includes cleaning and preparing the data for analytic purposes
the data, looking for patterns and insights (EDA & Analytics)
● A project report document (APA
formatted) between 5-8 pages in length, not including title page, content page,
or images/graphics/reference. The report should have the below sections:
○ Introduction: this is where you provide a brief description of your
personal motivation for the project and the framing question. Tell the reader
why they care about the results you are about to present and why is the
question you will be answering is important. A description of your dataset
including what type of data it contains, how many attributes, how many
instances. Any additional challenges such as messy or missing values.
○ Data Analysis: this is where you describe your data (summary statistics,
EDA), explain the methods you used to analyze the data. Discuss how the method
works, why it was well suited for your data, and how you applied it.
○ Results: this is where you describe and explain your findings. Why
do you think you found the results you did and what do you think they mean?
○ Conclusion: this should provide a concise answer to the analytical
question posed in the introduction along with a brief description of why the
analysis answered the way it did, which should be consistent with your results
section. Additionally, you may wish to posit questions raised by your analysis
for future analysis.
○ Reflection: Conduct a self-reflection for each of the phases in The
Process Section above to uncover key learnings that you can apply towards
future projects or which you can share with your colleagues. Document at least
4 key learnings from your reflection. Describe observations, challenges, lucky
breaks, emotions that you experienced, etc. you may have experienced during a
Do not just provide diagrams and statistics, each table
& figure included must have a caption (e.g., Figure number and textual
description) that is referenced from the text (e.g., “Figure 2 shows a
frequency diagram for ...”).
● You should also provide your source
code of a well-documented and formatted Jupyter Notebook and dataset files.
This assignment is worth 100 points, which is 10% of your
final grade. Your assignment will be evaluated based on a successful
compilation and adherence to the project requirements. Grading criteria:
● 50 pts for project report
● 50 pts for Python implementation
your project to blackboard by the due date, no late submissions will be
accepted. You should submit a well-documented Jupyter Notebook and dataset
files. Submit both .ipynb and ..docx file, name your files First_Lastname_FinalProject.xxx.