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

# Grouping items together
Until now we have learned that a variable can only hold a single item. But there are many ways to hold multiple values and refer to all of them by a single variable in Python, and the most popular is a *list*. A **list** can hold multiple values at the same time. These values could belong to any data type. 

Lists are mostly used to store similar kinds of information. For example, if we want to store the marks of 50 students of a class, we would not want to make 50 separate variables. Instead, we hold them in a list. 

To make a list we need to enclose all items separated by commas in `[]` and assign them to a variable. 

This lesson will introduce lists in Python and focus on how to use them.

What is Data Science

Python Basics

Handling Tabular Data in Python

Data Cleaning

Exploratory Data Analysis

Statistical Inference

Predictive Models

Machine Learning

Lists

Grouping items together