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Data Science with .NET and Polyglot Notebooks
Data Science with .NET and Polyglot Notebooks

Data Science with .NET and Polyglot Notebooks: Programmer's guide to data science using ML.NET, OpenAI, and Semantic Kernel

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Profile Icon Matt Eland
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Paperback Aug 2024 404 pages 1st Edition
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Arrow left icon
Profile Icon Matt Eland
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$47.99
Paperback Aug 2024 404 pages 1st Edition
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$26.99 $38.99
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$47.99
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Free Trial
Renews at $19.99p/m
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$26.99 $38.99
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Data Science with .NET and Polyglot Notebooks

Data Science, Notebooks, and Kernels

Data science seems like it is at an all-time high in popularity as advances in computing, storage, and data analysis have made new types of applications not just possible, but accessible to people who traditionally wouldn’t call themselves data scientists.

This book aims to help developers expand their existing knowledge and capabilities into the fields of data science, machine learning, artificial intelligence (AI), and data analysis.

In this opening chapter, we’ll cover these broad topics and explore what the field of data science includes, how the various parts of data science relate to each other, and how data science notebooks enable you to perform new tasks in new ways.

This chapter covers the following topics:

  • Exploring the field of data science
  • Data science notebooks and Project Jupyter
  • Extending notebooks with kernels
  • Polyglot Notebooks and .NET Interactive

Exploring the field of data science

Let’s start by defining what data science is and isn’t.

I define data science as the discipline of preparing and analyzing large amounts of data to extract insights and determine future behavior through machine learning and predictive modeling.

In other words, data science is all about gathering insights from the large amounts of data organizations amass every day. In fact, this is part of why data science has experienced an increase in popularity in the past decade.

Over the past several decades, organizations have moved more of their applications to be hosted on cloud computing providers such as Microsoft Azure, Amazon Web Services (AWS), and Google Cloud. This shift to cloud hosting had several benefits, including the following:

  • Ease in scaling web applications and databases as usage grows
  • Ease in adding new capabilities such as cloud storage and machine learning
  • Automated tools for collecting, storing, and...

Data science notebooks and Project Jupyter

In programming, when you want to illustrate a concept or explore new coding techniques, it’s normal to create a small application such as a console program and write the code you need there. This can be helpful for validating and communicating ideas, making small examples, reproducing errors, or building small demos.

Like programmers, data scientists also sometimes need to perform small experiments. While it’s entirely possible to perform data science experiments by creating a new Python or .NET program, a far more common approach for data scientists and data analysts is to create a notebook.

A notebook is a combination of documentation in the form of Markdown cells mixed together with code cells. This combination of code and documentation allows you to provide rich formatted documentation via Markdown while also providing live executable code in code cells.

For years, when people have talked about notebooks in data...

Extending notebooks with kernels

In our example in the previous section, we saw how code cells could be executed to produce a result.

This work is carried out by a kernel designed for the programming language you’re working with. In the earlier example, we saw a code cell written in Python. Since this is Python code, it uses the Python kernel.

In this way, the Jupyter Notebooks environment can support multiple languages, including Julia, Python, and R, as shown in Figure 1.8:

Figure 1.8 – Different kernels available to the notebook

Figure 1.8 – Different kernels available to the notebook

When you execute a Python cell, the code is sent to the Python kernel, which interprets it, executes it, and produces a result as illustrated in Figure 1.9:

Figure 1.9 – Jupyter Notebooks executing the Python kernel

Figure 1.9 – Jupyter Notebooks executing the Python kernel

This extensible design allows Jupyter Notebooks to support additional languages by adding additional kernels for those languages.

This is exactly what Polyglot...

Polyglot Notebooks and .NET Interactive

Polyglot Notebooks is a VS Code extension that integrates into Jupyter Notebooks to provide additional capabilities and language support through the kernel mechanism.

The goal of Polyglot Notebooks is to enrich the notebook experience by moving the supported languages beyond just the original languages. Polyglot Notebooks accomplishes this by adding its own kernel, called .NET Interactive, which supports the following languages:

  • C#
  • F#
  • JavaScript
  • Python
  • R
  • PowerShell
  • HTML
  • HTTP Requests
  • Mermaid
  • SQL
  • KQL

The philosophy of Polyglot Notebooks is that you should be able to choose the languages and tools you want to use for the task you’re trying to accomplish.

In other words, Polyglot Notebooks wants to allow you to pull data into your notebook via SQL, KQL, or a REST request, and then use C#, F#, or JavaScript to manipulate it before allowing you to visualize it using the capabilities...

Summary

In this chapter, we discussed the high-level fields of data science, machine learning, and AI.

While we covered these earlier in more depth, here is a consolidated set of definitions:

  • Artificial intelligence is the broadest field and revolves around emulating aspects of behaviors found in humans and animals.
  • Data science is the discipline of preparing and analyzing large amounts of data to extract insights and determine future behavior through machine learning and predictive modeling.
  • Machine learning is a broad field involving applying mathematics and statistics to solve data problems. Machine learning includes supervised learning, unsupervised learning, and semi-supervised learning including reinforcement learning.
  • Supervised learning involves applying statistical and mathematical techniques to model trends and relationships found in datasets.

In this chapter, we also discussed the role of notebooks in data science for conducting iterative experiments...

Further reading

See the following resources for more information on the topics covered in this chapter:

Left arrow icon Right arrow icon
Download code icon Download Code

Key benefits

  • Conduct a full range of data science experiments with clear explanations from start to finish
  • Learn key concepts in data analytics, machine learning, and AI and apply them to solve real-world problems
  • Access all of the code online as a notebook and interactive GitHub Codespace
  • Purchase of the print or Kindle book includes a free PDF eBook

Description

As the fields of data science, machine learning, and artificial intelligence rapidly evolve, .NET developers are eager to leverage their expertise to dive into these exciting domains but are often unsure of how to do so. Data Science in .NET with Polyglot Notebooks is the practical guide you need to seamlessly bring your .NET skills into the world of analytics and AI. With Microsoft’s .NET platform now robustly supporting machine learning and AI tasks, the introduction of tools such as .NET Interactive kernels and Polyglot Notebooks has opened up a world of possibilities for .NET developers. This book empowers you to harness the full potential of these cutting-edge technologies, guiding you through hands-on experiments that illustrate key concepts and principles. Through a series of interactive notebooks, you’ll not only master technical processes but also discover how to integrate these new skills into your current role or pivot to exciting opportunities in the data science field. By the end of the book, you’ll have acquired the necessary knowledge and confidence to apply cutting-edge data science techniques and deliver impactful solutions within the .NET ecosystem.

Who is this book for?

This book is for experienced C# or F# developers who want to transition into data science and machine learning while leveraging their .NET expertise. It’s ideal for those looking to learn ML.NET and Semantic kernel and extend their .NET skills to data science, machine learning, and Generative AI Workflows.

What you will learn

  • Load, analyze, and transform data using DataFrames, data visualization, and descriptive statistics
  • Train machine learning models with ML.NET for classification and regression tasks
  • Customize ML.NET model training pipelines with AutoML, transforms, and model trainers
  • Apply best practices for deploying models and monitoring their performance
  • Connect to generative AI models using Polyglot Notebooks
  • Chain together complex AI tasks with AI orchestration, RAG, and Semantic Kernel
  • Create interactive online documentation with Mermaid charts and GitHub Codespaces
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Table of Contents

21 Chapters
Part 1: Data Analysis in Polyglot Notebooks Chevron down icon Chevron up icon
Chapter 1: Data Science, Notebooks, and Kernels Chevron down icon Chevron up icon
Chapter 2: Exploring Polyglot Notebooks Chevron down icon Chevron up icon
Chapter 3: Getting Data and Code into Your Notebooks Chevron down icon Chevron up icon
Chapter 4: Working with Tabular Data and DataFrames Chevron down icon Chevron up icon
Chapter 5: Visualizing Data Chevron down icon Chevron up icon
Chapter 6: Variable Correlations Chevron down icon Chevron up icon
Part 2: Machine Learning with Polyglot Notebooks and ML.NET Chevron down icon Chevron up icon
Chapter 7: Classification Experiments with ML.NET AutoML Chevron down icon Chevron up icon
Chapter 8: Regression Experiments with ML.NET AutoML Chevron down icon Chevron up icon
Chapter 9: Beyond AutoML: Pipelines, Trainers, and Transforms Chevron down icon Chevron up icon
Chapter 10: Deploying Machine Learning Models Chevron down icon Chevron up icon
Part 3: Exploring Generative AI with Polyglot Notebooks Chevron down icon Chevron up icon
Chapter 11: Generative AI in Polyglot Notebooks Chevron down icon Chevron up icon
Chapter 12: AI Orchestration with Semantic Kernel Chevron down icon Chevron up icon
Part 4: Polyglot Notebooks in the Enterprise Chevron down icon Chevron up icon
Chapter 13: Enriching Documentation with Mermaid Diagrams Chevron down icon Chevron up icon
Chapter 14: Extending Polyglot Notebooks Chevron down icon Chevron up icon
Chapter 15: Adopting and Deploying Polyglot Notebooks Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
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