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


## Task 1: Flux of calls per day
We now have the column 'date' in our data. The emergency department needs to know the flux of calls per day for each reason mentioned in the dataset so that they can take the necessary steps to improve the service.

For this task, find the variation in the  number of calls per day for three reasons in the dataset: traffic, fire and EMS.

>**Hint:** You need to group the data for each reason on the date column and call the `count()`.


# Task 1: Flux of calls per day
We now have the column 'date' in our data. The emergency department needs to know the flux of calls per day for each reason mentioned in the dataset so that they can take the necessary steps to improve the service.

For this task, find the variation in the  number of calls per day for three reasons in the dataset: traffic, fire and EMS.

>**Hint:** You need to group the data for each reason on the date column and call the `count()`.

Let’s look at the solution to the above tasks.

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 Grouped Data

Task 1: Flux of calls per day