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

In Python, **pandas** is an open-source library providing high-performance, easy-to-use data structures and data analysis tools. Today, pandas is actively supported by a community of like-minded individuals around the world who contribute their valuable time and energy to help make pandas open source.

In this section of the course, we’ll learn to use pandas for data analysis. To compare pandas with a familiar program, we can think of pandas as being an extremely powerful version of Excel with a lot more features. We’ll cover the following key concepts in this lesson:

* Data structures—Series and DataFrame.
* Indexing and selection.
* Hierarchical indexing.
* Data cleaning and preparation.
* Handling missing data.
* Data wrangling—merging/joining, combining/concatenation. 
* Data aggregation and GroupBy.




Let’s learn about pandas, as well as some methods and attributes of Series in pandas.

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

Data Structures in pandas