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Python Data Analysis with JupyterLab

1,170 USD
Classroom Training, Online Training
Open Source

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  • Date: 13-May-2024 to 14-May-2024
    Time: 9AM - 5PM US Eastern
    Location: Virtual
    Language: English
    Delivered by: LearnQuest
    Price: 1,170 USD
  • Date: 8-Jul-2024 to 9-Jul-2024
    Time: 9AM - 5PM US Eastern
    Location: Virtual
    Language: English
    Delivered by: LearnQuest
    Price: 1,170 USD
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Course Description


This course will start by teaching students how to use Jupyter Notebook, a great tool for writing, testing, and sharing quick Python programs. Even if they do not end up using Jupyter Notebook as your main Python IDE, students will appreciate having it as a tool in your Python toolkit.



Students will learn NumPy, which makes working with arrays and matrices (in place of lists and lists of lists) much more efficient, and pandas, which makes manipulating, munging, slicing, and grouping data much easier. Students will also learn some simple data visualization techniques with matplotlib.


Anyone using or planning to use Python for data science or data analytics, then this is the right Python course.


    Have had a good amount of Python training and/or experience.


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
2. 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
3. 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 Plot
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Self-Paced Training Info

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  • Same in-demand topics as instructor-led public and private classes.
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