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
- 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
How the course is taught
- Self-paced, online course
- 3 Months access
- 28 course hours
- Exercise: Creating a Virtual Environment
- Exercise: Getting Started with JupyterLab
- Jupyter Notebook Modes
- Exercise: More Experimenting with Jupyter Notebooks
- Exercise: Playing with Markdown
- Magic Commands
- Exercise: Playing with Magic Commands
- Getting Help
- Exercise: Demonstrating Efficiency of NumPy
- NumPy Arrays
- Exercise: Multiplying Array Elements
- Multi-dimensional Arrays
- Exercise: Retrieving Data from an Array
- More on Arrays
- Using Boolean Arrays to Get New Arrays
- Random Number Generation
- Exploring NumPy Further
- Getting Started with pandas
- Introduction to Series
- Accessing Elements in a Series
- Exercise: Retrieving Data from a Series
- Series Alignment
- Exercise: Using Boolean Series to Get New Series
- Comparing One Series with Another
- Element-wise Operations and the apply() Method
- Series: A More Practical Example
- Introduction to DataFrames
- Creating a DataFrame using Existing Series as Rows
- Creating a DataFrame using Existing Series as Columns
- Creating a DataFrame from a CSV
- Exploring a DataFrame
- Exercise: Practice Exploring a DataFrame
- Changing Values
- Getting Rows
- Combining Row and Column Selection
- Boolean Selection
- Pivoting DataFrames
- Be careful using properties!
- Exercise: Series and DataFrames
- Plotting with matplotlib
- Exercise: Plotting a DataFrame
- Other Kinds of Plots
- This course can be taken on either a PC or Mac.
- PC: Windows 8 or later.
- Mac: macOS 10.10 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.
- Email capabilities and access to a personal email account.
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.
Instructional Material Requirements:
The instructional materials required for this course are included in enrollment and will be available online.
You can start this course at any time your schedule permits.
You have 3 month access to the course. After enrolling, you can learn and complete the course at your own pace, within the allotted access period.
There is no time limit to complete each lesson, other than completing all lessons within the allotted access period.
Because this course is self-paced, no extensions will be granted after the start of your enrollment.