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

# Welcome to the course

This course covers everything you need to know about advanced business intelligence— from the basics to implementing data preprocess pipelines and presenting insights. In most cases, this is all you need for advanced analytics. The goal of this course is to help you acquire data science skills that will enable you to start a large freelance project or work for an international enterprise.

# Intended audience

This course is designed for programmers who want to learn various methods of visually representing and analyzing data.

# Prerequisites
Although we’ve designed the course so to be accessible to anyone, the following will be helpful:
- Some knowledge of Python programming. 
- Some knowledge of common Python libraries, such as NumPy and pandas.
- Some basic mathematics knowledge. 



Python has quickly become the language of choice for many people when it comes to data analytics, machine learning, and data science. Even though many other languages, such as R and Julia, are also popular, Python clearly takes the lead in popularity for most programmers. This is because of its applicability to many fields, its ease of use, and its amazing ecosystem of libraries that support data analytics and machine learning. We’ll use Python exclusively in this course and discuss many of its great libraries, such as NumPy, pandas, Matplotlib, Seaborn, and Plotly.

By taking this course, you’ll learn to do the following:

- Plot different types of charts, individually and together.

- Set the Tick, Text, Legend, and Annotate elements of a plot.

- Customize the Grid and Spine displays of a plot.

- Set the colors for different elements of a plot.

- Customize the style and appearance of different plot components.

- Choose different chart types based on data type and requirement.

- Combine and merge datasets.

- Show the distribution of data.

- Show the relationship between different variables.

- Plot data in 2-D and 3-D.


Let's have a look at the course's content and its intended audience.

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

Data Visualization with Seaborn for Walmart Sales Projection

Plotly: Interactive Data Visualization

Project 1: Oil vs Banks During Recession

Project 2: Emergency Calls Dataset

In Closing

About the Course