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.

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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

Python is known for its brilliant support for third party resources. The language makers can't add every functionality to the language, therefore, the open-source community has created multiple *packages* that can be used when needed. We do not need to write code for everything. People have already done that for us. We only need to use that code in our program. 



# Classes and Modules
A **class** in python is a collection of one or more functions and variables. The functions and variables that are defined in a class are called its **members**. Member functions are formally called `methods`. 

A **module** in python is a collection of one or more classes and functions that we can reuse in our programs.

A collection of different modules, classes, and functions is known as a **package**. The illustration below describes this hierarchy 



This lesson will introduce the concept of packages and modules in python. 

What is Data Science

Python Basics

Handling Tabular Data in Python

Data Cleaning

Exploratory Data Analysis

Statistical Inference

Predictive Models

Machine Learning

Packages and Modules

Classes and Modules