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

F.A.Q

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

### 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

### 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.

### 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.

### 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:

# Algorithm Summary

Related Materials:

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

Code Along for Python Pandas (Goolge Colab link)

`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

Code Along for Python Seaborn (Goolge Colab link)

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

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

Dataset:

Exercise 01

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: