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Jupyter for Data Science

Video Description

Your one-stop guide to building an efficient data science pipeline using Jupyter.

About This Video

  • Get the most out of your Jupyter Notebook and 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 Jupyter's data science applications with this unique and intuitive guide

In Detail

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 video course 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 video course is for you! From data exploration to visualization, this course will take you 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 course also explains how Python 3, R, and Julia can be integrated with Jupyter for various data science tasks.

By the end of this course, you will comfortably leverage the power of Jupyter to perform various tasks in data science successfully.

Table of Contents

  1. Chapter 1 : Jupyter Concepts
    1. The Course Overview 00:04:34
    2. Jupyter User Interface 00:05:04
    3. Jupyter’s Menu Choice 00:08:28
    4. Real Life Examples – Finance and Gambling 00:03:48
    5. Real Life Examples – Insurance and Consumer Products 00:04:53
  2. Chapter 2 : Optimizing Jupyter Notebooks
    1. Installing JupyterHub 00:05:04
    2. Optimizing Python Script 00:02:55
    3. Optimizing R Scripts 00:05:09
    4. Securing a Notebook 00:05:20
  3. Chapter 3 : Working with Analytical Data on Jupyter
    1. Heavy-Duty Data Processing Functions in Jupyter 00:03:02
    2. Using Pandas in Jupyter 00:05:20
    3. Using SciPy in Jupyter 00:05:32
    4. Expanding on Panda DataFrames 00:02:25
    5. Sorting and Filtering DataFrames 00:02:49
  4. Chapter 4 : Data Visualization and Prediction
    1. Making a Prediction Using scikit-learn 00:04:29
    2. Making a Prediction Using R 00:03:00
    3. Interactive Visualization and Plotting 00:06:03
    4. Drawing a Histogram of Social Data 00:04:55
  5. Chapter 5 : Data Mining and SQL Queries
    1. Using Spark to Analyze Data 00:04:08
    2. Using SparkSession and SQL 00:02:58
    3. Combining Datasets 00:02:48
    4. Loading JSON into Spark 00:03:37
  6. Chapter 6 : R with Jupyter
    1. Analyzing 2016 US Election Demographics 00:04:14
    2. Analyzing 2016 Voter Registration and Voting 00:06:01
    3. Analyzing Changes in College Admissions 00:06:15
    4. Predicting Airplane Arrival Time 00:03:58
  7. Chapter 7 : Data Wrangling
    1. Reading a CSV File 00:05:50
    2. Manipulating Data with dplyr 00:07:20
    3. Tidying Up Data with tidyr 00:03:32
  8. Chapter 8 : Jupyter Dashboards
    1. Visualizing Glyph Ready Data 00:04:58
    2. Publishing a Notebook 00:04:09
    3. Creating a Shiny Dashboard 00:03:58
    4. Building Standalone Dashboards 00:02:58
  9. Chapter 9 : Statistical Modeling
    1. Converting JSON to CSV 00:01:44
    2. Evaluating Yelp Reviews 00:05:53
  10. Chapter 10 : Machine Learning Using Jupyter
    1. Naive Bayes 00:04:57
    2. Nearest Neighbor Estimator 00:06:45
    3. Decision Trees 00:05:35
    4. Neural Networks and Random Forests 00:05:44