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Python is one of the most popular programming languages for data science and analytics. It’s used across a wide range of industries. It’s easy to learn, highly flexible, and its various libraries can expand functionality to natively perform statistical functions and plotting.

This course is a comprehensive introduction to statistical analysis using Python. You’ll start with a step-by-step guide to the fundamentals of programming in Python. You’ll learn to apply these functions to numerical data. You’ll first look at strings, lists, dictionaries, loops, functions, and data maps. After mastering these, you’ll take a deep dive through various Python libraries, including pandas, NumPy, Matplotlib, Seaborn, and Plotly. You’ll wrap up with guided projects to clean, analyze, and visualize unique datasets using these libraries.

By the end of this course, you will be proficient in data science, including data management, analysis, and visualization.

Introduction to Data Science with Python

We can perform arithmetic operations with NumPy arrays. Arithmetic operations in the NumPy array work the same way as they do with numbers.

* Element-wise addition of two arrays is done using `+`.
* Element-wise subtraction of two arrays is done using `-`.
* Element-wise multiplication of two arrays is done using `*`.
* Element-wise division of two arrays is done using `/`.
* The power of all elements in an array is obtained using `**`.

> If we attempt to divide zero by zero (`0/0`), we get `NaN`.


Let’s learn with examples:

Let’s learn about the use of various arithmetic operations in NumPy arrays.

Introduction

Python Essentials

NumPy: Data Analysis

Pandas: Data Analysis

Matplotlib: Data Visualization

Seaborn: Statistical Data Visualization

Pandas: Data Visualization

Plotly: Interactive Data Visualization

Project 1: Oil vs Banks During Recession

Project 2: Emergency Calls Dataset

In Closing

Arithmetic Operations