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

## Boolean mask arrays
The boolean mask is very useful when it comes to counting, modifying, extracting, or manipulating values in an array based on certain conditions or criteria. For example, the boolean mask can come in handy when we want to count all the values greater than a certain value. We can set a threshold and want to get rid of outliers in our data.

In NumPy, boolean masking is often the most efficient way to accomplish these types of tasks. For example, boolean masking allows us to do the following:

* We can apply conditions such as `>`, `<`, `==`, and so on.
* We can create a mask to filter out the even numbers in a NumPy array.

Let’s look at a very simple example:

# Boolean mask arrays
The boolean mask is very useful when it comes to counting, modifying, extracting, or manipulating values in an array based on certain conditions or criteria. For example, the boolean mask can come in handy when we want to count all the values greater than a certain value. We can set a threshold and want to get rid of outliers in our data.

In NumPy, boolean masking is often the most efficient way to accomplish these types of tasks. For example, boolean masking allows us to do the following:

* We can apply conditions such as `>`, `<`, `==`, and so on.
* We can create a mask to filter out the even numbers in a NumPy array.

Let’s look at a very simple example:

Let’s learn about boolean mask arrays and masking operations in Python.

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

Boolean Masking

Boolean mask arrays