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Python for Data Analysis & Visualization

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Python for Data Analysis & Visualization, available at $89.99, has an average rating of 4.54, with 111 lectures, 18 quizzes, based on 1610 reviews, and has 34500 subscribers.

You will learn about 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 This course is ideal for individuals who are Python developers curious about the data analysis libraries or Python developers curious about the data visualization libraries or Anyone interested in learning Python or Data Analysts or Anyone working with data It is particularly useful for Python developers curious about the data analysis libraries or Python developers curious about the data visualization libraries or Anyone interested in learning Python or Data Analysts or Anyone working with data.

Enroll now: Python for Data Analysis & Visualization

Summary

Title: Python for Data Analysis & Visualization

Price: $89.99

Average Rating: 4.54

Number of Lectures: 111

Number of Quizzes: 18

Number of Published Lectures: 111

Number of Published Quizzes: 18

Number of Curriculum Items: 129

Number of Published Curriculum Objects: 129

Number of Practice Tests: 1

Number of Published Practice Tests: 1

Original Price: £99.99

Quality Status: approved

Status: Live

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

Who Should Attend

  • 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

Target Audiences

  • 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

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 Python3and 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,  Seabornand 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!

Course Curriculum

Chapter 1: Course Welcome & Set Up

Lecture 1: Course Overview

Lecture 2: Udemy 101

Lecture 3: Python Overview

Lecture 4: Anaconda Distribution Installation

Lecture 5: Jupyter Notebook 101

Lecture 6: Jupyter Notebook – Adding Comments in Cells

Lecture 7: Course Resources – Important!

Chapter 2: Objects, Variables and Data Types

Lecture 1: Objects and Variables Overview

Lecture 2: Numbers

Lecture 3: Coding Exercise Solution

Lecture 4: Coding Exercise Solution

Lecture 5: Strings

Lecture 6: Coding Exercise Solution

Lecture 7: String Operations

Lecture 8: String Methods and Properties

Lecture 9: Coding Exercise Solution

Lecture 10: String Concatenation and Formatting

Lecture 11: Lists

Lecture 12: Coding Exercise Solution

Lecture 13: Coding Exercise Solution

Lecture 14: Dictionaries

Lecture 15: Coding Exercise Solution

Lecture 16: Tuples and Sets

Lecture 17: Coding Exercise Solution

Lecture 18: Booleans

Lecture 19: Key Words in Python

Chapter 3: Control Flow and Loops

Lecture 1: Python Operators

Lecture 2: Control Flow

Lecture 3: Coding Exercise Solution

Lecture 4: For Loops

Lecture 5: For Loops (continued)

Lecture 6: Coding Exercise Solution

Lecture 7: Coding Exercise Solution

Lecture 8: While Loops

Lecture 9: Break, Continue and Pass Statements

Lecture 10: List Comprehension

Lecture 11: Coding Exercise Solution

Lecture 12: IN and NOT IN

Chapter 4: Functions

Lecture 1: Built-In Functions

Lecture 2: Coding Exercise Solution

Lecture 3: User Defined Functions

Lecture 4: User Defined Functions – Examples

Lecture 5: Coding Exercise Solution

Lecture 6: Coding Exercise Solution

Lecture 7: Arguments and Keyword Arguments

Lecture 8: Map and Filter

Lecture 9: Lambda Functions

Lecture 10: Coding Exercise Solution

Lecture 11: Errors and Exception Handling

Chapter 5: Challenge Section – Core Python

Lecture 1: Challenge Questions Overview

Lecture 2: Solutions Walkthrough

Lecture 3: Corection: Solutions

Chapter 6: Modules, Packages and Libraries

Lecture 1: Built-In Modules

Lecture 2: External Libraries

Chapter 7: NumPy

Lecture 1: NumPy Overview

Lecture 2: Array Slicing and Indexing

Lecture 3: Array Manipulation Functions

Lecture 4: Additional Array Creation Functions

Lecture 5: Array Arithmetic and Mathematical Functions

Lecture 6: IO Functions in NumPy

Chapter 8: Challenge Section – NumPy

Lecture 1: Challenge Questions

Lecture 2: Challenge Solutions

Chapter 9: Pandas

Lecture 1: Pandas Overview

Lecture 2: Introduction to Series

Lecture 3: Introduction to DataFrames

Lecture 4: Selecting Data 1

Lecture 5: Selecting Data 2

Lecture 6: Data Manipulation 1

Lecture 7: Data Manipulation 2

Lecture 8: Data Aggregation and Grouping

Lecture 9: Data Cleansing

Lecture 10: Combining DataFrames

Lecture 11: Windowing Operations

Instructors

Rating Distribution

  • 1 stars: 11 votes
  • 2 stars: 16 votes
  • 3 stars: 107 votes
  • 4 stars: 577 votes
  • 5 stars: 899 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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