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

# Lambda expression
Now that we know the basics of functions, we can move on and learn about the lambda expression. We’ll be using lambda expressions extensively in our pandas library. 
* A lambda expression can be used to create a small anonymous function, that is, a function without any name.
* These throw-away functions are created where we need them.
* Lambda expressions are mainly used in combination with the `filter()` and `map()` built-in functions.

The general syntax of a lambda expression is:
```py
lambda argument_list: expression
```

In the syntax above, `argument_list` consists of a comma-separated list of arguments and `expression` is an arithmetic expression that uses these arguments.

We can assign a lambda expression to a variable to give it a name as well.

Let’s write a function, which returns the square of a number, and then rewrite that function as a lambda expression. This is easier than it sounds!

Let’s learn about the lambda expression and its use with map and filter methods.

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

Plotly: Interactive Data Visualization

Project 1: Oil vs Banks During Recession

Project 2: Emergency Calls Dataset

In Closing

Lambda Expression, Map and Filter

Lambda expression