Data Science with Jupyter- 2-in-1

Data Science with Jupyter: 2-in-1, available at $39.99, has an average rating of 3.55, with 60 lectures, 2 quizzes, based on 12 reviews, and has 167 subscribers.

You will learn about 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 Create custom extensions and build data widgets using Jupyter Notebook Perform scientific computing and data analysis tasks with Jupyter Create interactive dashboards and dynamic presentations Master the best coding practices and deploy your Jupyter Notebooks efficiently This course is ideal for individuals who are This Learning Path targets students and professionals keen to master the use of Jupyter to perform a variety of data science tasks. It is particularly useful for This Learning Path targets students and professionals keen to master the use of Jupyter to perform a variety of data science tasks.

Enroll now: Data Science with Jupyter: 2-in-1

Summary

Title: Data Science with Jupyter: 2-in-1

Price: $39.99

Average Rating: 3.55

Number of Lectures: 60

Number of Quizzes: 2

Number of Published Lectures: 60

Number of Published Quizzes: 2

Number of Curriculum Items: 62

Number of Published Curriculum Objects: 62

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

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

Create custom extensions and build data widgets using Jupyter Notebook

Perform scientific computing and data analysis tasks with Jupyter

Create interactive dashboards and dynamic presentations

Master the best coding practices and deploy your Jupyter Notebooks efficiently

Who Should Attend

This Learning Path targets students and professionals keen to master the use of Jupyter to perform a variety of data science tasks.

Target Audiences

This Learning Path targets students and professionals keen to master the use of Jupyter to perform a variety of data science tasks.

Jupyter has emerged as a popular tool for code exposition and the sharing of research artefacts. It is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Some of its uses includes data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and more. To perform a variety of data science tasks with Jupyter, you’ll need some prior programming experience in either Python or R and a basic understanding of Jupyter.

This comprehensive 2-in-1 course teaches you how to perform your day-to-day data science tasks with Jupyter. It’s a perfect blend of concepts and practical examples which makes it easy to understand and implement. It follows a logical flow where you will be able to build on your understanding of the different Jupyter features with every section.

This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Jupyter for Data Science,starts off with an introduction to Jupyter concepts and installation of Jupyter Notebook. You will then learn to perform various data science tasks such as data analysis, data visualization, and data mining with Jupyter. You will also learn how Python 3, R, and Julia can be integrated with Jupyter for various data science tasks. Next, you will perform statistical modelling with Jupyter. You will understand various machine learning concepts and their implementation in Jupyter.

The second course, Jupyter In Depth, will walk you through the core modules and standard capabilities of the console, client, and notebook server. By exploring the Python language, you will be able to get starter projects for configurations management, file system monitoring, and encrypted backup solutions for safeguarding their data. You will learn to build dashboards in a Jupyter notebook to report back information about the project and the status of various Jupyter components.

By the end of this training program, you’ll comfortably leverage the power of Jupyter to perform various data science tasks efficiently.

Meet Your Expert(s):

We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:

?●?? Dan Toomey has been developing applications for over 20 years. He has worked in a variety of industries and companies of all sizes, in roles from sole contributor to VP/CTO level. For the last 10 years or so, he has been contracting companies in the eastern Massachusetts area under Dan Toomey Software Corp. Dan has also written R for Data Science and Learning Jupyter with Packt Publishing.

●??Jesse Bacon is a hobbyist programmer that lives and works in the northern Virginia area. His interest in Jupyter started academically while working through books available from Packt Publishing. Jesse has over 10 years of technical professional services experience and has worked primarily in logging and event management.

Course Curriculum

Chapter 1: Jupyter for Data Science

Lecture 1: The Course Overview

Lecture 2: Jupyter User Interface

Lecture 3: Jupyter’s Menu Choice

Lecture 4: Real Life Examples – Finance and Gambling

Lecture 5: Real Life Examples – Insurance and Consumer Products

Lecture 6: Installing JupyterHub

Lecture 7: Optimizing Python Script

Lecture 8: Optimizing R Scripts

Lecture 9: Securing a Notebook

Lecture 10: Heavy-Duty Data Processing Functions in Jupyter

Lecture 11: Using Pandas in Jupyter

Lecture 12: Using SciPy in Jupyter

Lecture 13: Expanding on Panda DataFrames

Lecture 14: Sorting and Filtering DataFrames

Lecture 15: Making a Prediction Using scikit-learn

Lecture 16: Making a Prediction Using R

Lecture 17: Interactive Visualization and Plotting

Lecture 18: Drawing a Histogram of Social Data

Lecture 19: Using Spark to Analyze Data

Lecture 20: Using SparkSession and SQL

Lecture 21: Combining Datasets

Lecture 22: Loading JSON into Spark

Lecture 23: Analyzing 2016 US Election Demographics

Lecture 24: Analyzing 2016 Voter Registration and Voting

Lecture 25: Analyzing Changes in College Admissions

Lecture 26: Predicting Airplane Arrival Time

Lecture 27: Reading a CSV File

Lecture 28: Manipulating Data with dplyr

Lecture 29: Tidying Up Data with tidyr

Lecture 30: Visualizing Glyph Ready Data

Lecture 31: Publishing a Notebook

Lecture 32: Creating a Shiny Dashboard

Lecture 33: Building Standalone Dashboards

Lecture 34: Converting JSON to CSV

Lecture 35: Evaluating Yelp Reviews

Lecture 36: Naive Bayes

Lecture 37: Nearest Neighbor Estimator

Lecture 38: Decision Trees

Lecture 39: Neural Networks and Random Forests

Chapter 2: Jupyter In Depth

Lecture 1: The Course Overview

Lecture 2: Setting Up

Lecture 3: Jupyter CLI Introduction

Lecture 4: The Jupyter Core Module

Lecture 5: The Jupyter Client

Lecture 6: The Jupyter Console

Lecture 7: Generating Configurations from the CLI

Lecture 8: Storing Configurations

Lecture 9: Configuration Extras

Lecture 10: Ipyleaflet

Lecture 11: More Fun with Ipywidgets

Lecture 12: Using the GitHub API

Lecture 13: Utilizing Twitter

Lecture 14: The Notebook Package

Lecture 15: Gdrive Custom Content Managers

Lecture 16: Customer Bundler Extensions

Lecture 17: Custom File Save Hook

Lecture 18: Custom Request Handlers

Lecture 19: Crafting a Dashboard

Lecture 20: The Dashboard Server

Lecture 21: Bokeh Dashboards

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 2 votes

2 stars: 0 votes

3 stars: 2 votes

4 stars: 4 votes

5 stars: 4 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!