Data Science with Jupyter 2-in-1

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 ChLanguage: English | Duration: 4h 43m | Size: 2.1 GB

Get the most out of Jupyter to perform various data science tasks

What you'll 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

Requirements

Some programming experience with Python or R is required

Description

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 Viia area. His interest in Jupyter started acadally 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.

Who this course is for

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