This document provides an overview and introduction to a course on data analysis with Python. It includes introductions from the instructor and teaching assistant. Key topics that will be covered in the course are discussed such as Python programming fundamentals, data manipulation and visualization with Python libraries, and advanced data analysis techniques. Instructions are provided on setting up Python through Anaconda and accessing Jupyter notebooks through Google File Stream. The next class will focus on programming fundamentals like variables and assignments.

1. Data Analysis with Python
ITAO 30210
Class 1
1

2. via Joshua Stevens

3. My Eyes Hurt…
qz.com

4. My Eyes Hurt…
qz.com

5. Introductions
n Name
n Hometown
n ‘Fun’ activity
over break
n Why BAN or
BTEC? What
plans for
future?

6. Introductions
n Application Developer, Web Group
n Concurrent Instructor
q Building Web Applications (MGTI)
q Data Analysis with Python (ITAO)
q Survey of Software Engineering (ITAO)
n At Notre Dame
q 8 years in OIT
q 2 years in Mendoza (Senior Web Developer)
q 5+ years in MarComm

7. Course Overview: Resources
n Al Sweigart (2017) Automate the Boring Stuff with Python (ABSP)
q E-copy is available for free at https://automatetheboringstuff.com
n Jake VanderPlas (2016) Python Data Science Handbook Essential
Tools for Working with Data. (PDSH) (O'Reilly Media)
q Available in bookstore
q Available online shops (and free online version)
n Access to Vocareum, a cloud based class management system
REFERENCE
n William McKinney (2017) Python for Data Analysis, 2nd Edition (PyDA)
(O’Reilly Media)

8. Introduction
n Teaching Assistant: Anisha Jaipuria
n Office Hours: Mondays 11:30 AM (Zoom)

9. Python Installation for practice
https://www.anaconda.com/download/

10. Course Overview: Vocareum for Online-
homework
n In order to enroll in the class on Vocareum you must
click and finish Homework 0 on Sakai.
q Remember there are two parts to submit
n This semester access to Vocareum is provided for
free to Notre Dame Students through a funding from
the Provost Learning Initiative grant.
n Set of assignments are due on a given day
q Do not wait till the last day to finish the set
q Penalty is applied for late submission (see Syllabus for
more details)

11. Course Overview: Objectives
n Analyzing data is all about asking the right questions and then
presenting your results in the best way possible to get your message
across.
q Understand the fundamentals of Python programming language and learn to
code.
q Gain a hands-on experience in data analysis by working with large amount of data
in Python.
q Write programs to collect, process and store data for future analysis.
q Apply data analytics tools to extract relevant information and present results as
business reports.
q Being able to tell a story with the data. This involves short summaries and
visualizations.

12. Course Overview: Semi-Flip Approach
n Specific links and pages from the textbook
are presented at the end of each class.
q You need to practice them before you come to the
class.
q If you are looking at the material for the first
time in the class, it can be very confusing.
n Most of the classes are split between the
lecture slides/notes and in class practice
sessions.

13. Course Overview: Lab Computer Usage
n We will be using the lab computers
q Hands-on experience
q In-class assignments
q Exams
n Using any other application (like email, messaging,
social media, etc.) than stipulated software is strictly
prohibited.
n Any information that is displayed on these screens
during the class time is considered public.
q I can use software to project any desktop that is being
used!!

14. Course Overview: Lab Computer Usage
n Just Joking!
n Highly recommended that you use your own
laptop for both homework as well as in class
learning and exams
n Download Anaconda for Free
n Easy to install on both Windows and Mac
n Contains everything you need to complete
this course

15. Course Overview: Grading
n Online (Vocareum) Homework
q 20%
n Group Project
q 20%
n In-Class Assignments (3)
q 15%
n Midterm Exam
q 20%
n Final Exam
q 25%

16. Course Overview: Topics
Fundamentals
Of Python
Programming
Data Manipulation,
Wrangling, and Visualization
with Python
Advanced
Data Analysis
With Python
• Python basics
• iPython notebook
• Flow Control
• Functions
• Lists and
Dictionaries
• Data Manipulation
• Numpy
• Pandas
• Scipy
• Data Visualization
• Matplotlib
• Web Scrapping
• Introduction to ML
• scikit-learn
• Linear Regression
• Simple classification
• Clustering

17. Provide Feedback – Early and Often
n Constructive and actionable feedback is
always welcome throughout the semester.
n Share your feedback personally (email, Slack
or office hours).
q If you don’t feel comfortable doing this you can
use the anonymous feedback form on Sakai.

18. Data Science is the Future

19. What is Data Science?

20. Who is a Data Analyst?
Data Scientist (n.):
Person who is better at
statistics than any
software engineer and
better at software
engineering than any
statistician.
-- Josh Willis
http://www.datasciencecentral.com

21. Data Science Process

22. Data Science Process
http://www.kdnuggets.com/2017/06/7-steps-mastering-data-preparation-python.html
Cross Industry Standard Process for Data Mining (CRISP-DM)

23. Introduction to Programming

24. What is a program?
n A set of instructions for a computer to
perform a task
Let us change our traditional attitude to the
construction of programs: Instead of imagining
that our main task is to instruct a computer
what to do, let us concentrate rather on
explaining to humans what we want the
computer to do.
Donald E. Knuth, Literate Programming, 1984

25. What is a program?
n A set of instructions for a computer to
perform a task
n Typically a program has
q Input data
q Process the data & instructions
q Produce an output

26. Python Programming

27. Python Programming
n Python is one of the most popular dynamic
languages, along with R, Ruby, Julia, etc.
n It is widely used by data scientists in both
academia and industry.
n Compared to fundamental programming
languages like C/C++: “Python reads like
kindergarten math and is easy on the layman’s
eye. It requires less code to complete basic
tasks, making it an economical language to
learn.”

28. Why Python?

29. Why Python?
n Readable and structured code
q Easy to learn
n It has a vibrant open source community.
q This means it is continuously evolving and getting
better everyday.
n Rich ecosystem of libraries makes it ideal and
essential language to learn in the analytics
domain.
q NumPy, SciPy, Pandas, Matplotlib, NLTK, DJango

30. Setting Expectations for this course
n Students will get familiarized with Python as a language and
learn techniques for data analytics, including:
q Data Collection
q Data Extraction and Manipulation
q Data Analysis methods
n This course does not teach
q Software engineering techniques
q App development
q Object-oriented programming concepts
q Checkout Survey of Software Engineering if you are interested
n I want to make sure you learn enough Python and data analytics
to be able to do more advanced content by yourself.

31. Python 2 vs Python 3
n The python community is undergoing a
transition from Python 2 series to Python 3
series
n Most of the code is backward compatible (!)
n We’ll learn Python 3
q Anytime you install or create code, remember to
always use Python 3

32. Programming Resources
n Python Tutor
q http://pythontutor.com/
q This is the best way to visualize your code and
see how the code gets executed step by step.
q Strongly recommended for the first time learners.

33. Additional Online Resources
n A Byte of Python
q https://python.swaroopch.com/
n LinkedIn Learning (Lynda)
q Playlist of videos are available on Sakai
n DataCamp
q Intro to Python for Data Science (link available on Sakai)
q Intermediate Python for Data Science (link available on Sakai)
n This is a fast paced course. Strongly recommend these
additional resources to complement in-class lectures if you
have never programmed before.

34. Best way to learn Python
n Practice, then practice and then practice more!
n Google and Stack Overflow are your best
friends!
n Programming is a creative activity
n You have to learn to break down a problem into
smaller tasks

35. Google File Stream
n Login to Google File Stream
q Start -> File Stream

36. Google File Stream
n If successful you
should see My Drive
as a directory in your
Google File Stream

37. Python environments
n Python interpreter

38. Python environments
n iPython interactive shell

39. Python environments
n iPython Notebook (Jupyter)

40. Python through Anaconda
n Start -> anaconda
prompt

41. Anaconda Prompt

42. n Make sure you have logged in Google File
Stream
n Type G: and press enter
q Remember the :, not just G
Anaconda Prompt – Google File Stream

43. Jupyter Notebook
n Once you are in G: drive, type jupyter notebook
q G:> jupyter notebook

44. Jupyter Notebook
n If successful, you should be able to see this
in a browser (Chrome)

45. Jupyter Notebooks on Your Laptop
n Download Anaconda for Mac or PC
q Single click install
n Launch Anaconda Navigator

46. Anaconda Navigator

47. Next class
n Fundamentals of Programming
n Variables and Assignments
n Reading: ABSP Ch.1
n Hands-on: Attempt Homework 0 on Sakai

49. https://www.youtube.com/watch?v=zrzMhU_4m-g