e-Attendance

Please follow the steps to complete your e-attendance

Prerequisite

Primary Requirements

Some programming experience (e.g. C, C++, Java, QBasic (!) etc. )
At least high school level math skills will be required.
Passion to learn

IDE Requirements

Most popular IDE for Data Science is Anaconda. You can download and install from here. Make sure your download Python 3.7 distribution.

F.A.Q

» I don’t have the admin permission to install any software (Don’t worry !)

Google Colab [if you already have Google Account ]

Course Outline

Module 1: Python – A Quick Review

In this module, you will get a quick review on Python Language. We will not going in depth but we will try to discuss some important components of Python Language. Please note, this is not meant to be a comprehensive overview of Python or programming in general

Hands-on : Environment Setup and Jupyter Notebook Intro.

Hands-on : Python Code Along

Hands-on : Python Review Exercise

Module 2: Python for Data Analysis ( Pandas )

Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.

Hands-on : Using Python Pandas Library

Module 3: Data Visualization/EDA/Data Analysis ( Seaborn)

In this part of the course we will discuss methods of descriptive statistics. You will learn what cases and variables are and how you can compute measures of central tendency (mean, median and mode) and dispersion (standard deviation and variance). Next, we discuss how to assess relationships between variables, and we introduce the concepts correlation and regression.

Hands-on : Using Python Seaborn Visualization Library

Module 4: Data Analytics / Machine Learning

In this part of the course we will discuss one of the best known Machine Learning Library Scikit-Learn, a package that provides efficient versions of a large number of common algorithms. Scikit-Learn is characterized by a clean, uniform, and streamlined API, as well as by very useful and complete online documentation. A benefit of this uniformity is that once you understand the basic use and syntax of Scikit-Learn for one type of model, switching to a new model or algorithm is very straightforward.

Hands-on : Using Python scikit-learn Library

Presentation File

Presentation File:

Cheat Sheet

Azure ML Cheat Sheet

Algorithm Summary

Source: http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/

Resources (Shared Links)

Related Materials:

Data Science Mindmap : https://gitmind.com/app/doc/fd05946837
Data Concept
- To know more about Data Concept you can click [this] link.
ML Performance Metrics:
- For Supervised Machine Learning
AzureML End-to-End Lecture Series
- Doing Machine Learning with Azure ML (3-day course)

Regression Performance Matrix

https://towardsdatascience.com/regression-an-explanation-of-regression-metrics-and-what-can-go-wrong-a39a9793d914

Classification Performance Matrix

https://medium.com/@MohammedS/performance-metrics-for-classification-problems-in-machine-learning-part-i-b085d432082b

Open Jupyter Notebook

Please follow this link to know about how to open Jupyter Notebook from your Local Machine (in your specified directory)
For a complete User Manual check out the Bryn Mawr College Computer Science Guide.

Module 1: Python Quick Review

Padlet Link

https://padlet.com/arifmarias/v62w8glqgs7zb6ps

Mindmap for Python

https://gitmind.com/app/doc/e105869745

Google Colab Notebook (Python Intro)

https://colab.research.google.com/drive/1e7IXX8apSlV5eZLEY-vF5HlI7GqIUSIK?usp=sharing

Exercise Zip

Python Crash Course Exercises

Exercise Solution

https://colab.research.google.com/drive/1rNz7vD5JzaoB48rNFa35EzuOdIul6uYi?usp=sharing

Module 2: Python for Data Analysis ( Pandas )

Code Along for Python Pandas (Goolge Colab link)

https://colab.research.google.com/drive/1aSQBkUaTFGIZ56wbXm1LuZYp6fOlUy6E?usp=sharing

Download Sample Excel File: Sample Alarm Data

Exercise: 1 for Pandas

Download .ipynb file from here [SF Salaries Exercise Salaries ] ( It’s a zip file . You need to Unzip and use)
Dataset you can download from here [Salaries] ( It’s a zip file . You need to Unzip and use)
Solution Colab Link is here

Exercise: 2 for Pandas

Download .ipynb file from here [Ecommerce Purchases Exercise ] ( It’s a zip file . You need to Unzip and use)
Dataset you can download from here [Ecommerce Purchases] ( It’s a zip file . You need to Unzip and use)
Solution Colab Link is here

Module 3: Data Visualization/EDA/Data Analysis (Seaborn)

Code Along for Python Seaborn (Goolge Colab link)

https://colab.research.google.com/drive/1fME18x4uKmBIFtLl2ckcAzqqriVz2DCB?usp=sharing

Exercise: 1 for Seaborn

Download .ipynb file from here [Seaborn Exercises ] ( It’s a zip file . You need to Unzip and use)
Solution Colab Link is here

Exercise: 2 (Capstone)

Download .ipynb file from here [911] ( It’s a zip file . You need to Unzip and use)
Solution Colab Link is here

Module 4: Data Analytics / Machine Learning

Code Along for Python Machine Learning – Sklearn (Goolge Colab link)

https://colab.research.google.com/drive/1TdKRmSF826vWWcgI-7WfBMABVHVI69Ue?usp=sharing

Dataset:

USA_Housing

Exercise 01

Python Practice Links

I wanted to point out some helpful links for practice. Don’t worry about being able to do these exercises, I just want you to be aware of the links so you can visit them later.

Basic Practice:

http://codingbat.com/python

More Mathematical (and Harder) Practice:

https://projecteuler.net/archives

List of Practice Problems:

http://www.codeabbey.com/index/task_list

A SubReddit Devoted to Daily Practice Problems:

https://www.reddit.com/r/dailyprogrammer

A very tricky website with very few hints and touch problems (Not for beginners but still interesting)

http://www.pythonchallenge.com/

Resources (Books)

Books