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

## Operators
**Operators** are used to perform arithmetic and logical operations on data. They use the provided data and give results. Some operators follow the *in-fix* (a **+** b) notation while some follow the *pre-fix* (**-** a) notation.  With **in-fix** notation, the operands are placed on the left and right side of the operator. With **pre-fix** notation, the operator is placed before the operand. We will look at the different kinds of operators in Python in this lesson.


# Operators
**Operators** are used to perform arithmetic and logical operations on data. They use the provided data and give results. Some operators follow the *in-fix* (a **+** b) notation while some follow the *pre-fix* (**-** a) notation.  With **in-fix** notation, the operands are placed on the left and right side of the operator. With **pre-fix** notation, the operator is placed before the operand. We will look at the different kinds of operators in Python in this lesson.


This lesson will focus on different types of operators in Python. 

What is Data Science

Python Basics

Handling Tabular Data in Python

Data Cleaning

Exploratory Data Analysis

Statistical Inference

Predictive Models

Machine Learning

Operators

Operators