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 are outliers?
**Outliers** are observations that are significantly distant from other observations. These do not follow the general trend of the data. Outliers can indicate variation or error in the data. Outliers in a single variable/column are called **univariate** while outliers in multiple variables/columns are called **multivariate**. 

# What are outliers?
**Outliers** are observations that are significantly distant from other observations. These do not follow the general trend of the data. Outliers can indicate variation or error in the data. Outliers in a single variable/column are called **univariate** while outliers in multiple variables/columns are called **multivariate**. 

This lesson will focus on the different types of outliers and why they happen.

What is Data Science

Python Basics

Handling Tabular Data in Python

Data Cleaning

Exploratory Data Analysis

Statistical Inference

Predictive Models

Machine Learning

Outliers

What are outliers?