Python for Data Analysis & Visualization

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

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

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!