Delve into foundational Python programming, explore libraries like pandas and Matplotlib, and gain insights into data management, analysis, and visualization with practical projects.

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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

Perform the following tasks:

1. Generate a vector array of 25 numbers using `arange()`.
2. Write a code to convert the vector array into a 2-D matrix using reshape.
3. Add a scalar value $100$ to all the values in the array.
4. Return the array.
# Input
N/A
# Output
The method should return a matrix of numbers containing values from 100 to 124.
# Sample output
`[[100 101 102 103 104]
 [105 106 107 108 109]
 [110 111 112 113 114]
 [115 116 117 118 119]
 [120 121 122 123 124]]`


Complete this challenge to practice what you’ve learned about NumPy essentials so far.

Introduction

Python Essentials

NumPy: Data Analysis

Pandas: Data Analysis

Matplotlib: Data Visualization

Seaborn: Statistical Data Visualization

Data Visualization with Seaborn for Walmart Sales Projection

Pandas: Data Visualization

Plotly: Interactive Data Visualization

Project 1: Oil vs Banks During Recession

Project 2: Emergency Calls Dataset

In Closing

Challenge: NumPy Essentials