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

What you will 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

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 and Seaborn

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.


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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
  • 4 dedicated Challenge Sections!!!!
English
language

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
Float Variables
Strings
Print Formatting with Strings
String Operations
String Indexing and Slicing Quiz
String Methods and Properties
String Methods
String Concatenation and Formatting
Lists
Lists
Lists
Dictionaries
Dictionaries
Tuples and Sets
Tuples and Sets
Booleans
Key Words in Python
Data Types

Control Flow and Loops

Python Operators
Control Flow
Control Flow
For Loops
For Loops (continued)
For Loops
For Loops
While Loops
Break, Continue and Pass Statements
List Comprehension
List Comprehension
IN and NOT IN

Functions

Built-In Functions
Built-In Functions
User Defined Functions
User Defined Functions – Examples
User Defined Functions
User Defined Functions
Arguments and Keyword Arguments
Map and Filter
Lambda Functions
Lambda Functions
Errors and Exception Handling

Challenge Section – Core Python

Challenge Questions Overview
Solutions Walkthrough

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
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