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.
Instructor(s):Self-Study
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.
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Lesson 1
- JupyterLab
- Exercise: Creating a Virtual Environment
- Exercise: Getting Started with JupyterLab
- Jupyter Notebook Modes
- Exercise: More Experimenting with Jupyter Notebooks
- Markdown
- Exercise: Playing with Markdown
- Magic Commands
- Exercise: Playing with Magic Commands
- Getting Help
- NumPy
- 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
- pandas
- Getting Started with pandas
- Introduction to Series
- np.nan
- 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
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