1.Welcome!!

This is BUAN B493-W51 - Business Analytics Using Python

Also known as- Data Science for Business using Python

1.1. Introductions

1.1.1. Student introductions

Love to know your motivation to take this course

1. Name, Major & Year
2. Why did you enroll in the course?
3. Have you used any data analysis program before?
4. What do you hope to acheive at the end of the course?

1.1.2. Instructor- ***Sumit Dhar***

1. 20 years in the Data Science/ Analytics Industry
2. Independent Industry Consultant & Data Scientist
3. Worked for start ups and large corporations in senior Data Science role
4. Worked for- MicroStrategy, Cap Gemini- Sogeti, GE, BarclayCard, 24/7 iLabs
5. Banks: Marketing & Risk Analytics || Online Experiene Analytics || IoT/ Device Analytics
6. Motivation in teaching the course:
   • Connect with local University and students
   • Help develop a in demand skill; None of this was taught to me in school
   • Help develop passion for using data in meaningful ways

1.1.3. Introducing the course

• 100% online class
• This is a first course in Python and Data Science
  • '101' skills that can be developed further
• The course is going to be taught out of ipython notebooks and google colab IDE. No class notes etc. Additional material to be made available as required
• Course materials ca be accessed online and all codes can be executed as shown in the class
• Learn through doing
  • By doing Data Science hands down
  • By attmepting to solve data analysis problems through coding
  • Collaborate and Co-opertive in the class and outside while doing everything to stay safe
  • Research, experiement and trial
• Class Material freely available; Online resources weaved into class notes.
• To get credit from work you need to finish assignments on time; extra work/ outside work doesn't get you any grade
• No group assignment

1.1.4. Course Flow

Course Flow Slides

1.2 The Data Science Process

• Every organization is has their own take on what Data Science process works for them
• CRISP-DM was the original forerunner of current suite of analytical methodologies. You can read more here
• We will discuss the Team Team Data Science Process proposed by MicroSoft

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1.3 Making sense of the Landscape

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More details in this article

1.2.1 Analysis vs Analytics

• Analysis refers to the process of segmenting your problem into easily digestible chunks that you can study individually and examine how they relate to each other.

• Analytics, on the other hand, is the application of logical and computational reasoning to the component parts obtained in an analysis. And in doing that one is looking for patterns and often exploring what she could do with them in the future.

1.4 Data Science Tech Stack

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1.5 Python for Data Science

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1.5.1. History of Python programming Language

• Python is a widely used general-purpose, high-level programming language
• Designed by Guido van Rossum in 1991 and developed by Python Software Foundation
• Started as "interesting" Christmas project, but now finds application in diverse areas. You saw the demand earlier

“Python is now also the language of amateurs, and I mean that in the best possible way.” —Guido van Rossum

Interesting interview...

1.5.2. High Level feature of Python programming language

On Quora brief history of evolution of Python as a scientific language of choice

• Python is an interpreted, dynamically-typed language with a precise and efficient syntax.

• Python has a good REPL and new modules can be explored from the REPL with dir() and docstrings. That's one reason to prefer Python over C, C++, or Java.

• The Python community invested in the mid-1990s in Numeric, an "extension to Python to support numeric analysis as naturally as Matlab does"

• Numeric later evolved into NumPy
• Several years later, the plotting functionality from Matlab was ported to Python with matplotlib.
  • Libraries for scientific computing were built around NumPy and matplotlib and bundled into the SciPy package

1.6. Using Google Colab

• Out o the box Python Notebook environment
• All work and Files saved in google drive!!
• Here is a brief overview- How to use Google Colab

1.6.1. Using Google Colab

All programming and analysis assignment are to be submitted as google colab file.

When you submit the assignments please make sure the following:

1. All code you have written execute correctly
2. The grading is 0/FULL Marks.
3. Most assignments are sequential so without completing step 1 you won't be effectiely able to proceed to next steps.
4. You provide Sumit comment privileges such that I can leave comment and execute the notebook
5. Provide the file link in Canvas, so that I can
6. Submission needs to be from your @loyno.edu account

Naming convetion of submission:

week<number>_<partnumber>_<student name>_<student number>.ipynb

number: week number that the assignment was assigned
partnumber: Usually 1, but depends on the final assignment
student name: Name or alias as you may hve in LORA
student number: Your Loyola Student number [not any parts of SS#]

1.7. FREE Resources

1.7.1. Books & Such

• You don't need these to pass the course.
• Sharing these Python and Data Science Resources such that you can increase your knowledge beyond the course content

1. The Hitchhiker’s Guide to Python!
2. Automate the Boring Stuff with Python
3. Python for Everybody
4. Python Data Science Handbook

1.7.2. Websites

1. Google's Python Class
2. Kaggle Tutorial
3. python dot org
4. Python Community
5. stackify
6. stackoverflow
7. full stack python

APPENDIX- A

The course is to be taught using Google Colab. You don't need a development environment on your personal computer.

However, anyone who wants a environment on their personal computers may refer to the following links. The first option is closer to the development environment to be used in the class.

Installing Python IDE on your desktops:

• Anaconda
• PyCharm