Data Visualization

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Intermediate

Dive into the basics of Python, explore your workspace, and perform simple math operations. Learn how to write readable code using comments and handle both basic and advanced mathematical expressions while displaying your results.

Learn how to store and manage data in Python by working with numbers, text, and variables. Understand how to assign values, follow naming conventions, and explore different data types. Gain hands-on experience with string operations, indexing, slicing, and memory recall techniques to efficiently manipulate text in Python.

Learn how to use comparison and logical operators, work with conditional expressions, and apply membership and type comparisons. Build a strong foundation in writing programs that respond dynamically to different conditions using if/else and if/elif/else structures.

Expand your understanding of data types by learning how to group multiple attributes within a single variable. Explore lists, tuples, and dictionaries to efficiently store and manage structured data, along with essential methods for manipulation and retrieval.

Master the art of writing loops in Python—essential constructs for iterating over lists, tuples, dictionaries, and other data structures. Learn to use while and for loops effectively, explore the range function, and work with nested loops to handle complex iteration patterns.

Explore essential built-in functions in Python and take the next step by creating your own. Learn how to define functions, use conditional logic, modify existing functions, and work with lambda expressions to write efficient, reusable code.

In this section, we'll explore the fundamentals of Series and DataFrame structures. You'll also learn about the distinctions between these two types of structures.

Data can be sourced in various formats, such as CSV, JSON, SQL, HTML, and more. With Pandas, you're not limited to a single format — you can work with data across a multitude of file types. In this chapter, we'll specifically focus on the CSV and TXT formats.

Here, you'll learn how to process raw data by removing extraneous information and managing null values in a dataset.

Explore the structure of an HTML file, learn how to load it efficiently, and gain practical skills in working with the data it contains.

Explore the basics of Beautiful Soup to extract data from HTML documents. Learn how to navigate the structure of HTML, access specific elements, and work with their child elements to build a strong foundation in web scraping.

Master working with element attributes and content using Beautiful Soup. Gain the skills to extract specific information, search elements by attribute values, and use advanced techniques to efficiently parse and analyze HTML data.

In this section, we will be introduced to the very basics of matplotlib - creating empty Figure and Axes, how to build a line plot, display multiple lines on one plot, add a legend, customize line type, and so on...

In this section, we will learn about other chart types, such as bar plots, histograms, box plots, how to set necessary parameters and customize them.

In this section, we will learn the scatter plots - one of the easiest ways to discover the relationships in your data.

In this section, we will learn about the seaborn library and build our first plot! We will also learn about the friendship between the pandas and the Seaborn.

We continue learning plots with Seaborn.\n\nYou will learn some more new plots and even build 3-variable plot!

Plots seem so boring without customization...\nIn this section, we will learn how to make our plots perfectly styled!!