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

## Pandas

**Pandas** is a very popular Python library that is used for data analysis, which is the second step of the Data Science lifecycle. It offers functionalities to handle and manipulate data very efficiently. It has been widely adopted by people who are not computer scientists or programmers as it makes them move beyond Excel for analyzing data. We will be using Pandas in this course for all of our analyses.

# Pandas

**Pandas** is a very popular Python library that is used for data analysis, which is the second step of the Data Science lifecycle. It offers functionalities to handle and manipulate data very efficiently. It has been widely adopted by people who are not computer scientists or programmers as it makes them move beyond Excel for analyzing data. We will be using Pandas in this course for all of our analyses.

This lesson will focus on how to import data from CSV files in Python using Pandas - a library for handling datasets in Python.

What is Data Science

Python Basics

Handling Tabular Data in Python

Data Cleaning

Exploratory Data Analysis

Statistical Inference

Predictive Models

Machine Learning

Importing Data in CSV Files with Pandas

Pandas