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

# What is Exploratory Data Analysis?
**Exploratory Data Analysis (EDA)** is the process of analyzing datasets with the aim of understanding them more deeply. As the term "exploratory" suggests, during EDA the focus is to *explore* or *understand* the data better. We try to make sense of the data in the context of our problem. 

During EDA, we can do any kind of analysis that gives us some insight into our data. Some of the common practices in EDA are:

* Looking at the data types of the variables 
* Identifying the most important variables
* Looking at the distributions of the variables
* Summarizing the data  
* Finding biases in the data
* Looking at the different trends in data
* Studying relationships among quantities
* Spotting anomalies in the data
* Visualizing the data


This lesson will introduce exploratory data analysis.

What is Data Science

Python Basics

Handling Tabular Data in Python

Data Cleaning

Exploratory Data Analysis

Statistical Inference

Predictive Models

Machine Learning

Introduction

What is Exploratory Data Analysis?