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Jupyter for Data Science

  • Course Code: Data Science - Jupyter for Data Science
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 3 Days Audience: This course is geared for those who wants to Get guide to building an efficient data science pipeline using Jupyter.

Course Snapshot 

  • Duration: 3 days 
  • Skill-level: Foundation-level Jupyter for Data Science skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to Get guide to building an efficient data science pipeline using Jupyter.   
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

Jupyter Notebook is a web-based environment that enables interactive computing in notebook documents. It allows you to create documents that contain live code, equations, and visualizations. This book is a comprehensive guide to getting started with data science using the popular Jupyter notebook. If you are familiar with Jupyter notebook and want to learn how to use its capabilities to perform various data science tasks, this is the book for you! From data exploration to visualization, this book will take you through every step of the way in implementing an effective data science pipeline using Jupyter. You will also see how you can utilize Jupyter’s features to share your documents and codes with your colleagues. The book also explains how Python 3, R, and Julia can be integrated with Jupyter for various data science tasks. By the end of this book, you will comfortably leverage the power of Jupyter to perform various tasks in data science successfully. 

Working in a hands-on learning environment, led by our Jupyter for Data Science expert instructor, students will learn about and explore: 

  • Get the most out of your Jupyter notebook to complete the trickiest of tasks in Data Science 
  • Learn all the tasks in the data science pipeline—from data acquisition to visualization—and implement them using Jupyter 
  • Get ahead of the curve by mastering all the applications of Jupyter for data science with this unique and intuitive guide 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Understand why Jupyter notebooks are a perfect fit for your data science tasks 
  • Perform scientific computing and data analysis tasks with Jupyter 
  • Interpret and explore different kinds of data visually with charts, histograms, and more 
  • Extend SQL’s capabilities with Jupyter notebooks 
  • Combine the power of R and Python 3 with Jupyter to create dynamic notebooks 
  • Create interactive dashboards and dynamic presentations 
  • Master the best coding practices and deploy your Jupyter notebooks efficiently 

Audience & Pre-Requisites 

This course is designed for beginners who wants to guide to building an efficient data science pipeline using Jupyter. 

Pre-Requisites:  Students should have familiar with  

  • Basics of Python  
  • Knowledge of Python is assumed. 

Course Agenda / Topics 

  1. Jupyter and Data Science 
  • Jupyter and Data Science 
  • Jupyter concepts 
  • A first look at the Jupyter user interface 
  1. Working with Analytical Data on Jupyter 
  • Working with Analytical Data on Jupyter 
  • Data scraping with a Python notebook 
  • Using heavy-duty data processing functions in Jupyter 
  • Using SciPy in Jupyter 
  • Expanding on panda data frames in Jupyter 
  1. Data Visualization and Prediction 
  • Data Visualization and Prediction 
  • Make a prediction using scikit-learn 
  • Make a prediction using R 
  • Interactive visualization 
  • Plotting using Plotly 
  • Creating a human density map 
  • Draw a histogram of social data 
  • Plotting 3D data 
  1. Data Mining and SQL Queries 
  • Data Mining and SQL Queries 
  • Special note for Windows installation 
  • Using Spark to analyze data 
  • Another MapReduce example 
  • Using SparkSession and SQL 
  • Combining datasets 
  • Loading JSON into Spark 
  • Using Spark pivot 
  1. R with Jupyter 
  • R with Jupyter 
  • How to set up R for Jupyter 
  • R data analysis of the 2016 US election demographics 
  • Analyzing 2016 voter registration and voting 
  • Analyzing changes in college admissions 
  • Predicting airplane arrival time 
  1. Data Wrangling 
  • Data Wrangling 
  • Reading a CSV file 
  • Reading another CSV file 
  • Manipulating data with dplyr 
  • Sampling a dataset 
  • Tidying up data with tidyr 
  1. Jupyter Dashboards 
  • Jupyter Dashboards 
  • Visualizing glyph ready data 
  • Publishing a notebook 
  • Creating a Shiny dashboard 
  • Building standalone dashboards 
  1. Statistical Modeling 
  • Statistical Modeling 
  • Converting JSON to CSV 
  • Evaluating Yelp reviews 
  • Using Python to compare ratings 
  • Visualizing average ratings by cuisine 
  • Arbitrary search of ratings 
  • Determining relationships between number of ratings and ratings 
  1. Machine Learning Using Jupyter 
  • Machine Learning Using Jupyter 
  • Naive Bayes 
  • Nearest neighbor estimator 
  • Decision trees 
  • Neural networks 
  • Random forests 
  1. Optimizing Jupyter Notebooks 
  • Optimizing Jupyter Notebooks 
  • Deploying notebooks 
  • Optimizing your script 
  • Monitoring Jupyter 
  • Caching your notebook 
  • Securing a notebook 
  • Scaling Jupyter Notebooks 
  • Sharing Jupyter Notebooks 
  • Converting a notebook 
  • Versioning a notebook 

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