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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.

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January 6, 2021

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Certification

Course Description

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.

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Course Curriculum

    • Jupyter and Data Science 00:00:00
    • Jupyter concepts 00:00:00
    • A first look at the Jupyter user interface 00:00:00
    • Working with Analytical Data on Jupyter 00:00:00
    • Data scraping with a Python notebook 00:00:00
    • Using heavy-duty data processing functions in Jupyter 00:00:00
    • Using SciPy in Jupyter 00:00:00
    • Expanding on panda data frames in Jupyter 00:00:00
    • Data Visualization and Prediction 00:00:00
    • Make a prediction using scikit-learn 00:00:00
    • Make a prediction using R 00:00:00
    • Interactive visualization 00:00:00
    • Plotting using Plotly 00:00:00
    • Creating a human density map 00:00:00
    • Draw a histogram of social data 00:00:00
    • Plotting 3D data 00:00:00
    • Data Mining and SQL Queries 00:00:00
    • Special note for Windows installation 00:00:00
    • Using Spark to analyze data 00:00:00
    • Another MapReduce example 00:00:00
    • Using SparkSession and SQL 00:00:00
    • Combining datasets 00:00:00
    • Loading JSON into Spark 00:00:00
    • Using Spark pivot 00:00:00
    • R with Jupyter 00:00:00
    • How to set up R for Jupyter 00:00:00
    • R data analysis of the 2016 US election demographics 00:00:00
    • Analyzing 2016 voter registration and voting 00:00:00
    • Analyzing changes in college admissions 00:00:00
    • Predicting airplane arrival time 00:00:00
    • Data Wrangling 00:00:00
    • Reading a CSV file 00:00:00
    • Reading another CSV file 00:00:00
    • Manipulating data with dplyr 00:00:00
    • Sampling a dataset 00:00:00
    • Tidying up data with tidyr 00:00:00
    • Jupyter Dashboards 00:00:00
    • Visualizing glyph ready data 00:00:00
    • Publishing a notebook 00:00:00
    • Creating a Shiny dashboard 00:00:00
    • Building standalone dashboards 00:00:00
    • Statistical Modeling 00:00:00
    • Converting JSON to CSV 00:00:00
    • Evaluating Yelp reviews 00:00:00
    • Using Python to compare ratings 00:00:00
    • Visualizing average ratings by cuisine 00:00:00
    • Arbitrary search of ratings 00:00:00
    • Determining relationships between number of ratings and ratings 00:00:00
    • Machine Learning Using Jupyter 00:00:00
    • Naive Bayes 00:00:00
    • Nearest neighbor estimator 00:00:00
    • Decision trees 00:00:00
    • Neural networks 00:00:00
    • Random forests 00:00:00
    • Optimizing Jupyter Notebooks 00:00:00
    • Deploying notebooks 00:00:00
    • Optimizing your script 00:00:00
    • Monitoring Jupyter 00:00:00
    • Caching your notebook 00:00:00
    • Securing a notebook 00:00:00
    • Scaling Jupyter Notebooks 00:00:00
    • Sharing Jupyter Notebooks 00:00:00
    • Converting a notebook 00:00:00
    • Versioning a notebook 00:00:00

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