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

# Concept
A **dictionary** A dictionary is an object that stores a collection of data. Each element in a dictionary has two parts: a key and a value. We use **keys** to locate specific **values**.

For many of us, the word “dictionary” elicits an image of a large book—such as the Merriam-Webster dictionary—that contains words and their definitions. If we want to know the meaning of a particular word, we locate it in the dictionary to find its definition.

In Python, a dictionary is an object that stores data collection in the form of key-value pairs. When we want to retrieve a specific value from a dictionary, we use the key associated with that value. This is similar to the process of looking up a word in the Merriam-Webster dictionary, where, by analogy, the words are keys and the definitions are values.

For example, let’s suppose there’s a company in which each employee has an ID number. We want to write a program that lets us look up any employee’s name by entering their ID number. To accomplish this, we can create a dictionary in which each element contains an employee’s ID number as the key and the employee’s name as the value. Thus, using this program, if we know an employee’s ID number, then we can easily retrieve their name.

Another example of a program that uses a dictionary would be one that lets us enter a person’s name to get that person’s phone number. The program could use a dictionary in which each element contains a person’s name as the key and that person’s phone number as the value. If we know a person’s name, then we can retrieve that person’s phone number.


> Key-value pairs are often referred to as mappings because each key is mapped
to a value.


* Dictionaries are key-value pairs in curly brackets `{}`.
* Dictionaries store objects by key instead of relative position and don’t maintain any reliable left-to-right order. They map keys to the associated values.
* We can expand and shrink dictionaries like lists.

Let's learn about dictionary objects, nesting dictionaries, and different methods of dictionary objects.

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

Dictionaries

Concept