Data Science with Jupyter: 2-in-1

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Packt Publishing

4:43:18

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1. Jupyter for Data Science

1.1 Data Science with Jupyter.zip

1. The Course Overview.mp4

04:34

2. Jupyter User Interface.mp4

05:04

3. Jupyters Menu Choice.mp4

08:28

4. Real Life Examples Finance and Gambling.mp4

03:48

5. Real Life Examples Insurance and Consumer Products.mp4

04:53

6. Installing JupyterHub.mp4

05:03

7. Optimizing Python Script.mp4

02:54

8. Optimizing R Scripts.mp4

05:08

9. Securing a Notebook.mp4

05:19

10. Heavy-Duty Data Processing Functions in Jupyter.mp4

03:02

11. Using Pandas in Jupyter.mp4

05:19

12. Using SciPy in Jupyter.mp4

05:32

13. Expanding on Panda DataFrames.mp4

02:24

14. Sorting and Filtering DataFrames.mp4

02:49

15. Making a Prediction Using scikit-learn.mp4

04:29

16. Making a Prediction Using R.mp4

03:00

17. Interactive Visualization and Plotting.mp4

06:03

18. Drawing a Histogram of Social Data.mp4

04:55

19. Using Spark to Analyze Data.mp4

04:07

20. Using SparkSession and SQL.mp4

02:58

21. Combining Datasets.mp4

02:47

22. Loading JSON into Spark.mp4

03:36

23. Analyzing 2016 US Election Demographics.mp4

04:14

24. Analyzing 2016 Voter Registration and Voting.mp4

06:01

25. Analyzing Changes in College Admissions.mp4

06:15

26. Predicting Airplane Arrival Time.mp4

03:58

27. Reading a CSV File.mp4

05:50

28. Manipulating Data with dplyr.mp4

07:19

29. Tidying Up Data with tidyr.mp4

03:31

30. Visualizing Glyph Ready Data.mp4

04:57

31. Publishing a Notebook.mp4

04:09

32. Creating a Shiny Dashboard.mp4

03:58

33. Building Standalone Dashboards.mp4

02:58

34. Converting JSON to CSV.mp4

01:44

35. Evaluating Yelp Reviews.mp4

05:53

36. Naive Bayes.mp4

04:56

37. Nearest Neighbor Estimator.mp4

06:45

38. Decision Trees.mp4

05:35

39. Neural Networks and Random Forests.mp4

05:43

40. Test your knowledge.html

2. Jupyter In Depth

1. The Course Overview.mp4

01:38

2. Setting Up.mp4

05:26

3. Jupyter CLI Introduction.mp4

04:38

4. The Jupyter Core Module.mp4

03:37

5. The Jupyter Client.mp4

05:43

6. The Jupyter Console.mp4

04:13

7. Generating Configurations from the CLI.mp4

05:24

8. Storing Configurations.mp4

05:44

9. Configuration Extras.mp4

03:56

10. Ipyleaflet.mp4

07:51

11. More Fun with Ipywidgets.mp4

06:44

12. Using the GitHub API.mp4

07:11

13. Utilizing Twitter.mp4

06:04

14. The Notebook Package.mp4

03:58

15. Gdrive Custom Content Managers.mp4

04:42

16. Customer Bundler Extensions.mp4

02:57

17. Custom File Save Hook.mp4

04:56

18. Custom Request Handlers.mp4

05:23

19. Crafting a Dashboard.mp4

03:49

20. The Dashboard Server.mp4

03:16

21. Bokeh Dashboards.mp4

06:10

22. Test your knowledge.html

Description

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

Who is this for?

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

More details

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. Itâ€™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, youâ€™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:

Â â—Â Â 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.

â—Â Â 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.
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.

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Packt Publishing

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Packt are an established, trusted, and innovative global technical learning publisher, founded in Birmingham, UK with over eighteen years experience delivering rich premium content from ground-breaking authors and lecturers on a wide range of emerging and established technologies for professional development.Packt’s purpose is to help technology professionals advance their knowledge and support the growth of new technologies by publishing vital user focused knowledge-based content faster than any other tech publisher, with a growing library of over 9,000 titles, in book, e-book, audio and video learning formats, our multimedia content is valued as a vital learning tool and offers exceptional support for the development of technology knowledge.We publish on topics that are at the very cutting edge of technology, helping IT professionals learn about the newest tools and frameworks in a way that suits them.

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