Python Data Analysis with JupyterLab

If you are using or plan to use Python for data science or data analytics, then this is the right Python course for you. This course is in-depth and assumes that you already possess a strong understanding of Python from previous training or experience.

You will learn how to use Jupyter Notebook, an essential tool for writing, testing, and sharing quick Python programs. As the course progresses, you will also learn about Python libraries such as NumPy, which makes working with arrays and matrices more efficient, and pandas, a key tool for manipulating, munging, slicing, and grouping data. The course will conclude with an overview of simple data visualization techniques with matplotlib.

What you will learn

• JupyterLab & Jupyter notebooks
• Markdown
• The purpose of NumPy
• One-dimensional & Two-dimensional NumPy arrays
• Using boolean arrays to create new arrays
• The purpose of pandas
• Series objects and one-dimensional data
• DataFrame objects to two-dimensional data
• Creating plots with matplotlib

How you will benefit

• Obtain valuable Python data analysis skills
• Learn to work with Jupyter Notebook
• Gain best practices for using matplotlib
• Discover how to use NumPy to work with arrays and matrices of numbers
• Develop experience utilizing pandas to analyze data

Requirements:

Hardware Requirements:

• This course can be taken on either a PC or Mac.

Software Requirements:

• PC: Windows 10 or later.
• Mac: macOS 11.0 or later.
• Browser: The latest version of Google Chrome or Mozilla Firefox are preferred. Microsoft Edge and Safari are also compatible.
• Adobe Acrobat Reader.
• Software must be installed and fully operational before the course begins.

Other:

• Email capabilities and access to a personal email account.

Instructional Material Requirements:

The instructional materials required for this course are included in enrollment and will be available online.

Prerequisites:

Prior to enrolling in this course, you must have previous Python programming experience. You should be comfortable writing your own functions and working with strings, lists, tuples, dictionaries, loops, and conditionals.

Instructor: Self-Study
Course Hours: 28.00