Final Project
Due: 30th April, XXXXXXXXXX�59pm ET
Up until now, we have given you fairly detailed instructions for how to design data analyses to
answer specific questions about data -- in particular, how to set up a particular analysis and what
steps to take to run it. In this project, you will put that knowledge to use!
Put yourself in the shoes of a data scientist being given a data set and asked to draw conclusions
from it. Your job will be to understand what the data is showing you, design the analyses you need,
justify those choices, draw conclusions from running the analyses, and explain why they do (or do
not) make sense.
We are deliberately not giving you detailed directions on how to solve these problems, but feel free
to come to office hours and recitation hours to
ainstorm.
Objectives
There are two possible paths through this project:
�. You may use data set #1, which captures information about bike usage in New York City. See
elow for the analysis questions we want you to answer.
�. You may use data set #2, which captures information about student behavior and performance
in an online course. See below for the analysis questions we want you to answer.
Partners
On this project you may work with one partner (except for Honors contracting students who must
work individually). Working with a partner is optional, and working with a partner will not impact how
the project is graded. If you want to work with a partner, it is your responsibility to pair up; feel free
to use Piazza's "Search for Teammates" feature (https:
piazza.com/class/kjn0wpn1k2v5ju?cid=5) to
facilitate this.
If you are working with a partner, you must share a repository. This means that one of you will clone
your repository on GitHub classroom, and the other will join this team (rather than cloning a second
epository). When you go through the normal steps for cloning your repository you will have an
opportunity to join an existing team (i.e., your partner has already created a group) or create a new
team (i.e., your partner hasn't set up their repository yet).
If you plan to team up with someone who has already cloned their repository, it is very important
that you follow these instructions to join their team, rather than creating a new repository.
Path 1: Bike traffic
The NYC_Bicycle_Counts_2016_Co
ected.csv gives information on bike traffic across a number of
idges in New York City. In this path, the analysis questions we would like you to answer are as
follows:
https:
github.com/ECEDataScience/ProjectS21
lo
main/README.md#due-30th-april-2021-1159pm-et
https:
github.com/ECEDataScience/ProjectS21
lo
main/README.md#objectives
https:
github.com/ECEDataScience/ProjectS21
lo
main/README.md#partners
https:
github.com/ECEDataScience/ProjectS21
lo
main/README.md#path-1-bike-traffic
https:
piazza.com/class/kjn0wpn1k2v5ju?cid=5
�. You want to install sensors on the
idges to estimate overall traffic across all the
idges. But
you only have enough budget to install sensors on three of the four
idges. Which
idges
should you install the sensors on to get the best prediction of overall traffic?
�. The city administration is cracking down on helmet laws, and wants to deploy police officers on
days with high traffic to hand out citations. Can they use the next day's weather forecast to
predict the number of bicyclists that day?
�. Can you use this data to predict whether it is raining based on the number of bicyclists on the
idges?
Path 2: Student performance related to video-watching behavio
ehavior-performance.txt contains data for an online course on how students watched videos (e.g.,
how much time they spent watching, how often they paused the video, etc.) and how they
performed on in-video quizzes. readme.pdf details the information contained in the data fields.
There might be some extra data fields present than the ones mentioned here. Feel free to
ignore/include them in your analysis. In this path, the analysis questions we would like you to answe
are as follows:
�. How well can the students be naturally grouped or clustered by their video-watching behavio
( fracSpent , fracComp , fracPaused , numPauses , avgPBR , numRWs , and numFFs )? You should
use all students that complete at least five of the videos in your analysis.
�. Can student's video-watching behavior be used to predict a student's performance (i.e.,
average score s across all quizzes)? This type of analysis could ultimately save significant time
y avoiding the need for tests. You should use all students that complete at least half of the
quizzes in your analysis.
�. Taking this a step further, how well can you predict a student's performance on a particular in-
video quiz question (i.e., whether they will be co
ect or inco
ect) based on their video-
watching behaviors while watching the co
esponding video? You should use all student-video
pairs in your analysis.
What to turn in
You must turn in two sets of files, by pushing them to your team's Github repository:
eport.pdf : A project report, which should consist of:
A section with the names of the team members (maximum of two), your Purdue
username(s), and the path (1 or 2) you have taken.
A section describing the dataset you are working with.
A section describing the analyses you chose to use for each analysis question (with a
paragraph or two justifying why you chose that analysis and what you expect the analysis
to tell you).
A section (or more) describing the results of each analysis, and what your answers to the
questions are based on your results. Visual aids are helpful here, if necessary to back up
your conclusions. Note that it is OK if you do not get "positive" answers from your analysis,
ut you must explain why that might be.
All Python .py code files you wrote to complete the analysis steps.
https:
github.com/ECEDataScience/ProjectS21
lo
main/README.md#path-2-student-performance-related-to-video-watching-behavio
https:
github.com/ECEDataScience/ProjectS21
lo
main/README.md#what-to-turn-in
Note: You may encounter instances in the data where you would need to make a decision on
including extra information, changing non number values in files to appropriate numbers. How you
choose to handle this is up to you. You may visit office hours to seek guidance.
userID VidID fracSpent fracComp fracPlayed fracPaused numPauses avgPBR stdPBR numRWs numFFs s
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