Python for Data Analysis & Visualization 2022

Python for Data Analysis & Visualization 2022

Python for Data Analysis & Visualization 2022

Master the main data analysis and visualization libraries in Python: Numpy, Pandas, Matplotlib, Seaborn, Plotly + more

Language: english

Note: 4.6/5 (121 notes) 17,345 students

Instructor(s): Malvik Vaghadia

Last update: 2022-06-01

What you’ll learn

  • Python, we will be using Python3 in this course
  • Data Analysis Libraries in Python such as NumPy and Pandas
  • Data Visualization Libraries in Python such as Matplotlib and Seaborn
  • How to analyse data
  • Data Visualization
  • Jupyter Notebooks IDE / Anaconda Distribution

 

Requirements

  • No prior knowledge required

 

Description

Learn one of the most in demand programming languages in the world and master the most important libraries when it comes to analysing and visualizing data.

This course can be split into 3 key areas:

  • The first area of the course focuses on core Python3 and teaches you the essentials you need to be able to master the libraries taught in this course

  • The second area focuses on analysing and manipulating data. You will learn how to master both NumPy and Pandas

  • For the final part of the course you learn how to display our data in the form of interesting charts using Matplotlib,  Seaborn and Plotly Express

You will be using Jupyter Notebooks as part of the Anaconda Distribution. Jupyter is the most popular Python IDE available.

The course is packed with lectures, code-along videos, coding exercises and quizzes.

On top of that there are numerous dedicated challenge sections that utilize interesting datasets to enable you to make the most out of these external libraries.

There should be more than enough to keep you engaged and learning! As an added bonus you will also have lifetime access to all the lectures as well as lots of downloadable course resources consisting of detailed Notebooks.

The aim of this course is to make you proficient at using Python and the data analysis and visualization libraries.

This course is suitable for students of all levels and it doesn’t matter what operating system you use.

Curriculum summary:

  • Set Up & Installation

  • Core Python

    • Python Objects, Variables and Data Types

    • Control Flow and Loops

    • Functions

  • External Libraries

  • Data Analysis Libraries

    • NumPy

    • Pandas

    • Connecting to different Data Sources

  • Visualization Libraries

    • Matplotlib

    • Seaborn

    • Plotly Express

  • 4 dedicated Challenge Sections!!!!

 

Who this course is for

  • Python developers curious about the data analysis libraries
  • Python developers curious about the data visualization libraries
  • Anyone interested in learning Python
  • Data Analysts
  • Anyone working with data

 

Course content

  • Course Welcome & Set Up
    • Course Overview
    • Udemy 101
    • Python Overview
    • Anaconda Distribution Installation
    • Jupyter Notebook 101
    • Jupyter Notebook – Adding Comments in Cells
    • Course Resources – Important!
  • Objects, Variables and Data Types
    • Objects and Variables Overview
    • Numbers
    • Integer Variables
    • Coding Exercise Solution
    • Float Variables
    • Coding Exercise Solution
    • Strings
    • Print Formatting with Strings
    • Coding Exercise Solution
    • String Operations
    • String Indexing and Slicing Quiz
    • String Methods and Properties
    • String Methods
    • Coding Exercise Solution
    • String Concatenation and Formatting
    • Lists
    • Lists
    • Coding Exercise Solution
    • Lists
    • Coding Exercise Solution
    • Dictionaries
    • Dictionaries
    • Coding Exercise Solution
    • Tuples and Sets
    • Tuples and Sets
    • Coding Exercise Solution
    • Booleans
    • Key Words in Python
    • Data Types
  • Control Flow and Loops
    • Python Operators
    • Control Flow
    • Control Flow
    • Coding Exercise Solution
    • For Loops
    • For Loops (continued)
    • For Loops
    • Coding Exercise Solution
    • For Loops
    • Coding Exercise Solution
    • While Loops
    • Break, Continue and Pass Statements
    • List Comprehension
    • List Comprehension
    • Coding Exercise Solution
    • IN and NOT IN
  • Functions
    • Built-In Functions
    • Built-In Functions
    • Coding Exercise Solution
    • User Defined Functions
    • User Defined Functions – Examples
    • User Defined Functions
    • Coding Exercise Solution
    • User Defined Functions
    • Coding Exercise Solution
    • Arguments and Keyword Arguments
    • Map and Filter
    • Lambda Functions
    • Lambda Functions
    • Coding Exercise Solution
    • Errors and Exception Handling
  • Challenge Section – Core Python
    • Challenge Questions Overview
    • Solutions Walkthrough
    • Corection: Solutions
  • Modules, Packages and Libraries
    • Built-In Modules
    • External Libraries
  • NumPy
    • NumPy Overview
    • Array Slicing and Indexing
    • Array Manipulation Functions
    • Additional Array Creation Functions
    • Array Arithmetic and Mathematical Functions
    • IO Functions in NumPy
  • Challenge Section – NumPy
    • Challenge Questions
    • Challenge Solutions
  • Pandas
    • Pandas Overview
    • Introduction to Series
    • Introduction to DataFrames
    • Selecting Data 1
    • Selecting Data 2
    • Data Manipulation 1
    • Data Manipulation 2
    • Data Aggregation and Grouping
    • Data Cleansing
    • Combining DataFrames
    • Windowing Operations
  • Challenge Section – Pandas
    • Challenge Questions – TfL Dataset
    • Solutions Walkthrough
    • Challenge Questions – Employees Dataset
    • Solutions Walkthrough
  • Data Sources
    • Excel and CSV
    • HTML
    • Databases
    • Pandas Input and Output Methods
  • Matplotlib
    • Matplotlib Overview
    • Choosing the Right Chart Type
    • Creating a Plot Area 1
    • Creating a Plot Area 2
    • Bar Plots
    • Line Plots
    • FIFA 21 Player Dataset
    • Scatter Plots
    • Histograms
    • Box Plots and Violin Plots
    • Style and Presentation
    • Additional Resources and Cheat Sheets
  • Challenge Section – Matplotlib
    • Challenge Questions Overview
    • Solutions Walkthrough
  • Seaborn
    • Seaborn Overview
    • Categorical Plots
    • Relational Plots
    • Distribution Plots
    • Regression Plots
    • Matrix Plots
    • Multi Plot Grids
    • Style and Presentation
  • Challenge Section – Seaborn
    • Challenge Questions Overview
    • Solutions Walkthrough
  • Plotly Express
    • Plotly Express Overview
    • Interactive Charts in Plotly Express
    • 3D Charts
    • BONUS: Further Learning
    • BONUS: Further Learning Resources

 

Python for Data Analysis & Visualization 2022Python for Data Analysis & Visualization 2022

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