Initial_Project_Directions
Course Project: Your
Only Assignment
Due Last Day of Class
Why a Project
• A project help build a portfolio.
• It forces you to do analysis
(usually) and communicate
your results.
• Far more interesting to grade
than an exam.
“It doesn’t matter how great your analysis
is unless you can explain it to others: you
need to communicate your results.”
R for Data Science
Hadley Wickham & Ga
ett Grolemund
General Project Guidelines
A) Find a Data Source and Analyze it
• You can search Kaggle or other dataset repositories)
• Work*
• Make your own dataset.
• Use an API (like the twitter API) to gather data (Not easy)
• Web Scraping (hard)
• Improve on Previous Analysis Projects
B) Other Python related options (pick any)
• Code a ML Algorithm from scratch
• Improve a Python project from another class (doesn’t have to be analysis
elated)
• Translate your old code to Python
General Project Guidelines
• Choose option A or B.
Grading
• Due to the nature of data analytics being a bit subjective and
people being able to choose between different project options,
anyone who turns in a project will get an A (95%) in this class.
• Students may get up to an A+ (100%) if the project is impressive.
• I will still provide feedback and areas for improvement on your
project.
General Project Advice Option A
• The following couple of slides are just advice.
Option A: Task 1
•Make a problem statement
• After picking your dataset, it is
important to figure out what
problem you are trying to
solve.
Option A: Task 2
• Identify who may use your
esult
• In other words, figure out what
is the potential usefulness of
your analysis
Option A: Task 3
•Make some preliminary goals
for your project (what may
come out of your work)
Option A: Task 4
• Think of some Success Metrics
for your analysis
• For machine learning tasks,
accuracy can help
• Hypothesis Testing
• How do we know it
works/improves
Option A: Task 5
•Mention any uncertainty
isks
that may be a challenge to
complete your project.
• Example: For machine learning
tasks, it helps to have more than
250 rows.
Option B
B) Python related options (pick
any)
• Code a ML Algorithm from
scratch
• Improve a Python project from
another class
• Translate your code to Python
Option B
Since this is incredibly open
ended, you will have to figure
out what you want to show.
Microsoft Word - onlineSyllabus.docx
Data Analytics using Python Syllabus
Course Number: CSE-41204
Instructor Information
Name: Michael Galarnyk
Email: XXXXXXXXXX
LinkedIn: https:
www.linkedin.com/in/michaelgalarnyk/
Communication Policy
You may contact me by email. It usually helps to contact me a couple days before your
one project is due as most students tend to ask around that time.
Course Information
Course Description (Goals and Objectives)
In this course, you will learn the rich set of tools, li
aries, and packages that comprise
the highly popular and practical Python data analysis ecosystem. This course is
primarily taught via screen sharing programming videos. Topics taught range from
asic Python syntax all the way to more advanced topics like supervised and
unsupervised machine learning techniques.
Key Topics
• Installing Python/Jupyte
IPython on Windows and Mac
• Python Basics (variables, strings, simple math, conditional logic, for loops, lists,
tuples, dictionaries, etc)
• Using the Pandas li
ary to manipulate data (filtering and sorting data, combining
files, GroupBy, etc)
• Plotting data in Python using Matplotlib and Seaborn
• Logistic Regression using Scikit-Learn
• Classification and Regression Metrics
• Decision Trees using Scikit-Learn
• Random Forests (Scikit-Learn)
• Clustering Algorithms (K-Means, Hierarchical Clustering)
• Dimensionality Reduction (Principal Component Analysis)
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Course Materials and Textbooks
Suggested Texts: None
Student Learning Outcomes
By the end of this course, students will be able to:
a) Interpret trends in data
) Produce a project that they can use as part of their data analytics/science portfolio.
Course Schedule
While a lot of the students in this class know the basics, reviewing the basics is
important even for experienced python programmers. That is why the first two weeks
are dedicated to the basics and will be continuously reviewed throughout the
emainder of the course during more advanced topics.
Session Topic Assignments
w/due dates
1 Intro + Setup + Basics (strings, lists,
tuples, etc)
2 Tuples, dictionaries, sets, functions
3 Pandas Part 1
4 Pandas Part 2
5 Matplotlib + Logistic Regression
6 Decision Trees
7 Decision Trees + Random Forests
8 Unsupervised Learning (KMeans +
dimensionality reduction)
9 Topics of Interest and How to Learn
Them*
* Lecture about the topics we didn’t
cover in this class and how to learn
them.
Final Project Due
Page 3 of 5
Grading and Assignment Information
Letter grades are based on the UC San Diego Extension Grading Scale. Your final
course grade is based on the percentage of points you have earned.
Passing Grades
A+ 100%
A 90-99%
A XXXXXXXXXX%
B XXXXXXXXXX%
B 83-85%
B XXXXXXXXXX%
C XXXXXXXXXX%
C 71-75%
C XXXXXXXXXX%
Weighted Grading Criteria
UC San Diego Extension does NOT have a requirement about how instructors weight
their grading criteria. I have decided to make nearly 100% of your grade be a project.
Details and rationale for this are explained in assignment section of Blackboard.
Assignments (Class Project) 100%
TOTAL 100%
Grading Policies
This course can be taken as part of the Python Programming certificate. In order for
the class to count towards your certificate it must be taken for a letter grade or as
pass/no pass. Classes that are taken as NFC cannot count towards a certificate. You
can change your grading option any time BEFORE the last day of class through My
Extension.
Late Policy:
Final Project is due on date specified on course schedule. An assignment is
considered late if it is posted or sent after the due date/time.
Late assignments will be accepted at the discretion of the instructor and cannot be
accepted more than 1 week late. A couple hours or a day late is typically okay. I don’t
take off points for late assignments.
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Assignments
Due to the nature of this course (a final project), any type of submission you see fit is
typically acceptable. I normally see some variation of:
a) .ipynb file
) .ipynb file + powerpoint file
c) .ipynb file + report (I highly discourage writing a report as a blog post is better for an
online presence.
d) .ipynb file + blog post
e) .py file
f) .py file + powerpoint file
The reason for allowing different type of projects is that I want this class to be a way for
students to improve themselves as they see fit. Everyone coming into the class has
different goals and I allow for people to show me however they want that they learned
something or improved on previous knowledge in this class.
Discussion Board
Feel free to ask questions on the board or on the unlisted youtube videos for the
course.
Quizzes & Tests
No quizzes or tests.
UC San Diego Extension Policies and Resources
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Please refer to UC San Diego Extension’s website (Student Resources tab) for specific
details about academic policies and procedures: Student Resources.
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extension.ucsd.edu/). Extension students must access the website to find out
the status of the emergency situation. Email and or phone lines may not be accessible.
Page 5 of 5
Information will be updated online as the situation progresses and an ALL CLEAR will
e posted once the situation is resolved.
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At UC San Diego Extension, we strive to make learning experiences as accessible as
possible. If you anticipate or experience physical or academic ba
iers based on
disability, we encourage you to contact the Extension Disability Coordinator to apply for
easonable accommodations. Visit our website: Services for Students with Disabilities.
Please note that it is your responsibility to initiate contact with the Disability
Coordinator.
Phone: XXXXXXXXXXEmail: XXXXXXXXXX