Gain insights into conducting meaningful data analysis in Python without prior programming experience. Discover Python fundamentals, data preprocessing, EDA, and predictive analytics for solving business problems.

env.tar.gz

Python3

Ready to move past Excel for complex business analysis? Then you’ll find this course very helpful.

This hands-on introductory Data Science course is aimed at professionals and students who don't have any experience with programming. It will help you advance your career by preparing you to conduct meaningful data analysis in Python on any dataset — large or small.

You’ll begin with the fundamentals of Python, with focus on CSV files in Python, covering concepts like data preprocessing and Exploratory Data Analysis (EDA). In the second half, you'll focus on predictive and inferential analysis using statistical and machine learning techniques, and learn how these techniques can help solve business problems.

Data Science for Non-Programmers

## Pivot Tables
**Pivot tables** are a summary of the whole data that give us useful information. These tables reorganize the desired data in a different format. These tables can transform data from columns to rows or rows to columns, or group data by any attribute. Because of the reorganization and transformation of the data, these tables were given the name **pivot**. These tables can include statistics such as sum, mean, maximum, minimum, and many more. 

### Use cases
Pivot Tables are used to: 

* Group data for business problems such as calculating sales by region or products
* Compare different classes of a data field such as comparing data for males and females
* Easily find out unique values in a field such as finding out different types of products for which we have data. 
* Summarize complex tables

# Pivot Tables
**Pivot tables** are a summary of the whole data that give us useful information. These tables reorganize the desired data in a different format. These tables can transform data from columns to rows or rows to columns, or group data by any attribute. Because of the reorganization and transformation of the data, these tables were given the name **pivot**. These tables can include statistics such as sum, mean, maximum, minimum, and many more. 

## Use cases
Pivot Tables are used to: 

* Group data for business problems such as calculating sales by region or products
* Compare different classes of a data field such as comparing data for males and females
* Easily find out unique values in a field such as finding out different types of products for which we have data. 
* Summarize complex tables

This lesson will focus on how to create pivot tables in Python using pandas.

What is Data Science

Python Basics

Handling Tabular Data in Python

Data Cleaning

Exploratory Data Analysis

Statistical Inference

Predictive Models

Machine Learning

Pivot Tables

Pivot Tables

Use cases