Jupyter Notebook Training

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Jupyter Notebook Training

Introduction to Jupyter Notebook

Get an overview of Jupyter Notebook, an open-source web application that allows you to create and share documents containing live code, equations, visualizations, and narrative text.

Getting Started with Jupyter Notebook

Learn how to install and set up Jupyter Notebook on your local machine. Understand the basics of the Jupyter interface and how to create and manage notebooks.

Notebook Interface and Features

Explore the Jupyter Notebook interface. Learn about cells, kernels, and how to use different cell types for code, text, and visualizations.

Working with Code in Jupyter Notebook

Learn how to write and execute code in Jupyter Notebook. Understand how to work with different programming languages and use the notebook to run code interactively.

Data Visualization and Analysis

Discover how to create visualizations and perform data analysis using Jupyter Notebook. Learn about popular libraries such as Matplotlib, Seaborn, and Pandas for visualizing and analyzing data.

Documenting and Sharing Notebooks

Understand how to document your work within Jupyter Notebook. Learn how to add text, equations, and other content to make your notebooks more informative and shareable.

Advanced Notebook Features

Explore advanced features of Jupyter Notebook, including magic commands, interactive widgets, and integration with other tools and services.

Jupyter Notebook Extensions

Learn about various extensions and plugins available for Jupyter Notebook that can enhance functionality and streamline your workflow.

Hands-On Projects and Exercises

Apply your knowledge through hands-on projects and exercises. Work on practical examples to reinforce your understanding of Jupyter Notebook and its capabilities.

Jupyter Notebook syllabus

1. Introduction to Jupyter Notebook

1. Getting Started
  • Introduction to Jupyter Notebook
  • Installation and Setup
    • Installing Anaconda
    • Launching Jupyter Notebook
  • Understanding the Jupyter Interface
    • Menu bar
    • Toolbar
    • Code cells vs. Markdown cells
  • Basic Operations
    • Creating and saving notebooks
    • Running cells
    • Keyboard shortcuts

2. Basic Python Programming

  • Variables and Data Types
  • Basic Operators
  • Conditional Statements
  • Loops (for, while)
  • Functions and Modules

3. Introduction to Pandas

  • Installing Pandas
  • Data Structures: Series and DataFrame
  • Reading and Writing Data
    • CSV
    • Excel
    • SQL
  • Data Frame Operations
    • Indexing and selecting data
    • Filtering
    • Sorting

4. Data Cleaning and Preparation

  • Handling Missing Data
  • Data Transformation
  • String Operations
  • Aggregating and Grouping Data

5. Data Visualization

5.1. Introduction to Matplotlib and Seaborn
  • Installing Matplotlib and Seaborn
  • Basic Plotting with Matplotlib
    • Line Plots
    • Bar Plots
    • Histograms
  • Customizing Plots
    • Titles and Labels
    • Legends
    • Colors and Styles
5.2. Advanced Visualization
  • Plotting with Seaborn
    • Distribution Plots
    • Categorical Plots
    • Matrix Plots
  • Interactive Visualizations with Plotly
    • Installing Plotly
    • Basic Interactive Plots
    • Customizing Interactive Plots

6. Advanced Jupyter Features

6.1. Interactive Widgets
  • Installing ipywidgets
  • Basic Widgets
    • Slider
    • Dropdown
    • Text
  • Linking Widgets and Functions
6.2. Advanced Notebook Features
  • Magic Commands
    • Line and cell magics
    • Timeit
    • Debugging
  • Exporting Notebooks
    • HTML
    • PDF
    • LaTeX
  • Collaboration
    • JupyterHub
    • Nbviewer

7. Machine Learning with Scikit-Learn

  • Introduction to Machine Learning
    • Overview of Machine Learning
    • Installing Scikit-Learn
    • Supervised Learning
      • Regression
      • Classification
    • Unsupervised Learning
      • Clustering
      • Dimensionality Reduction

Advanced Training

1. Advanced Jupyter Notebook Features

  • Advanced Notebook Features
    • Advanced Markdown
      • LaTeX for Mathematical Notation
      • Embedded HTML and CSS
    • Magic Commands
      • Advanced Line and Cell Magics
      • Custom Magic Functions
    • Extensions and Customizations
      • Installing and Managing Jupyter Extensions
      • Using Jupyter Themes
      • Creating Custom Jupyter Widgets

2. JupyterLab

  • Introduction to JupyterLab
  • JupyterLab Interface and Features
    • Workspaces
    • Code Consoles
    • File and Data Management
  • Extensions and Customizations in JupyterLab

3. Advanced Data Manipulation with Pandas

  • Data Manipulation Techniques
    • Advanced Indexing and Selection
    • MultiIndex and Hierarchical Indexing
    • Advanced Grouping Operations
      • GroupBy with Multiple Keys
      • Applying Multiple Functions
    • Reshaping and Pivoting Data
  • Performance Optimization
    • Efficient Data Handling
      • Working with Large Datasets
      • Memory Optimization
    • Vectorization and Performance Tuning
    • Parallel Processing with Dask
      • Introduction to Dask
      • Parallel Processing with Dask and Pandas

4. Advanced Data Visualization

  • Advanced Matplotlib and Seaborn
    • Customizing Complex Plots
      • Subplots and Multiple Axes
      • 3D Plotting with Matplotlib
    • Advanced Seaborn Techniques
      • Complex Statistical Plots
      • Custom Theming and Styling
  • Interactive and Real-time Visualization
    • Advanced Plotly Techniques
      • 3D Scatter and Surface Plots
      • Interactive Dashboards
    • Real-time Data Visualization
      • Using Bokeh
      • Streaming Data with Plotly and Bokeh

5. Interactive Widgets and Applications

  • Building Interactive Applications
    • Advanced ipywidgets
      • Custom Widgets
      • Widget Layouts and Styling
    • Integration with Plotly and Bokeh
    • Deploying Interactive Widgets

6. Jupyter Notebook Integration with Other Tools

  • Integration with Databases
    • Connecting to SQL Databases
    • Using SQL Magic Commands
    • Integration with NoSQL Databases
  • Using Jupyter with Cloud Platforms
    • Google Colab
    • AWS SageMaker
    • Azure Notebooks
  • Version Control with Jupyter Notebooks
    • Using Git with Jupyter Notebooks
    • Notebook Diff Tools

7. Advanced Machine Learning and Data Science

  • Advanced Machine Learning Techniques
    • Model Selection and Evaluation
    • Hyperparameter Tuning with GridSearchCV
    • Ensemble Methods
  • Deep Learning with TensorFlow and Keras
    • Introduction to TensorFlow
    • Building Neural Networks with Keras
    • Model Training and Evaluation
  • Data Science Projects and Best Practices
    • Data Science Project Lifecycle
    • Best Practices in Data Analysis
    • Case Studies and Project Examples

Training

Basic Level Training

Duration : 1 Month

Advanced Level Training

Duration : 1 Month

Project Level Training

Duration : 1 Month

Total Training Period

Duration : 3 Months

Course Mode :

Available Online / Offline

Course Fees :

Please contact the office for details

Placement Benefit Services

Provide 100% job-oriented training
Develop multiple skill sets
Assist in project completion
Build ATS-friendly resumes
Add relevant experience to profiles
Build and enhance online profiles
Supply manpower to consultants
Supply manpower to companies
Prepare candidates for interviews
Add candidates to job groups
Send candidates to interviews
Provide job references
Assign candidates to contract jobs
Select candidates for internal projects

Note

100% Job Assurance Only
Daily online batches for employees
New course batches start every Monday
Download Syllabus