## Intro to Python

### 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 : Python Review Exercise

Google Colab Notebook (Python Crash Course)

Assessment 01 Zip

Python Assessment 01

Assessment 01 Solution

Assessment 02 Zip

Python Assessment 02

Assessment 02 Solution

Mindmap for Python

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

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:

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/

Open Jupyter Notebook

## Data Analysis & Visualization Using Python

### Module 1: 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 2: Data Visualization/EDA/Data Analysis ( Descriptive Statistics and 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

Code Along for Python Pandas (Goolge Colab link)

```Dataset : Loan_Approval_Data
```

Exercise: 1 for Pandas

• Download .ipynb file from here [SF Salaries Exercise( 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 for Pandas with Seaborn

• Download .ipynb file from here [01-911 Calls Data Capstone Project( It’s a zip file . You need to Unzip and use)
• Dataset you can download from here [911( It’s a zip file . You need to Unzip and use)
• Solution Colab Link is here

## Data Visualization Using Power BI

• ### Delicious Pizza

Sample Dashboard:

Dataset:

Dataset:

Hints:

Transformation:

• Check all data type
• Create Date table
• Create New Measurements
• Total Sales [Sum of Sales]
• Total Margin [Sum of Profit]
• Total COGS [Sum of COGS]
• Sales vs COGS [Total Sales – Total COGS]
• Profit % [Total Margin / Total COGS]
• Average Order [Total Sales / Total Number of Row*(use COUNTROWS Function)]

Modeling :

• Create Relationship between Financial & Date table