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Course Skill Level:


Course Duration:

3 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:


Who should attend & recommended skills:

Beginners with basic Python skills

Who should attend & recommended skills

  • This course is designed for beginners who want a guide to building an efficient data science pipeline using Jupyter.
  • Skill-level: Foundation-level Jupyter for Data Science skills for Intermediate skilled team members. This is not a basic class.
  • Python: Basic (1-2 years’ experience)

About this course

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.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Jupyter for Data Science expert instructor, students will learn about and explore:
  • Getting the most out of your Jupyter notebook to complete the trickiest of tasks in Data Science
  • Learning all the tasks in the data science pipeline from data acquisition to visualization and implement them using Jupyter
  • Getting ahead of the curve by mastering all the applications of Jupyter for data science with this unique and intuitive guide
  • Understanding why Jupyter notebooks are a perfect fit for your data science tasks
  • Performing scientific computing and data analysis tasks with Jupyter
  • Interpreting and explore different kinds of data visually with charts, histograms, and more
  • Extending SQL’s capabilities with Jupyter notebooks
  • Combining the power of R and Python 3 with Jupyter to create dynamic notebooks
  • Creating interactive dashboards and dynamic presentations
  • Mastering the best coding practices and deploy your Jupyter notebooks efficiently

Course breakdown / modules

  • Jupyter concepts
  • A first look at the Jupyter user interface

  • 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

  • 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

  • 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

  • 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

  • Reading a CSV file
  • Reading another CSV file
  • Manipulating data with dplyr
  • Sampling a dataset
  • Tidying up data with tidyr

  • Visualizing glyph ready data
  • Publishing a notebook
  • Creating a Shiny dashboard
  • Building standalone dashboards