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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


## Task 1: Creating a bar plot
Recreate the bar plots below for the max and min stock prices that took the biggest hit during the recession. Use the same dataset and codes from the previous practice exercise.

Hint: Use `subplots()` and pass `ncols = 2`.


# Task 1: Creating a bar plot
Recreate the bar plots below for the max and min stock prices that took the biggest hit during the recession. Use the same dataset and codes from the previous practice exercise.

Hint: Use `subplots()` and pass `ncols = 2`.

Let’s look at the solutions to the above exercise.

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

Solution: Plotting the Data

Task 1: Creating a bar plot