The Case for C# Over Other Stacks When Developing Data Science Projects

If you're a data scientist, there's a good chance that Python or R are already your go-to languages. But what if you want an alternative? You might be thinking about C#. Is it right for your needs? That depends on a few things: the libraries available in C#, the size of your organization, and the nature of your project. In this post, we'll explore these considerations so that you can make an informed decision about whether or not to start learning C# as an alternative language for data science tasks.

C# Origins

C# is a general-purpose programming language that can be used to build a variety of applications and frameworks. It was built by Microsoft, which also owns its development environment (Visual Studio). The .NET Framework is an open-source library for C# that provides support for other programming languages as well, including:

  • Visual Basic

  • F#

  • Python

  • R

The Versatility of C#

C# has a lot of libraries. The .NET Framework, the .NET Core and Mono all contain thousands of reusable code snippets for common programming tasks. Some are included with the installation, and others can be downloaded from the internet.

For example, if you want to perform some data analysis using Microsoft Excel 2007 or later versions, then you can use the [Microsoft Office Excel add-in] library in C# to do so. This library provides an easy way to interact with the spreadsheet on your computer while still keeping everything inside Visual Studio Code (or whatever IDE you prefer).

The same applies for machine learning: there are many different libraries that allow us to create models based on neural networks or other deep learning models in C# without having to learn another language like Python or R first!

LINQ The Killer Function within C#

LINQ is a query language for .NET that can be used to change the way you work with data in your programs. It includes four main features:

  • Query expressions, which are written as expressions and make it easy to write queries in C# and Visual Basic.

  • Language extensions, which extend the set of built-in types in both languages.

  • Enumerable objects, which represent collections of elements that can return results from queries in order to provide support for filtering, ordering, grouping and other operations on them.

  • Generic methods and types (like those defined by IEnumerable), which allow you to create strongly typed methods without needing to specify their signature at compile time

The flexibility of LINQ within the .NET framework is unmatched when compared to other features in competing tech stacks. Using LINQ paired with Microsoft's Task Parallel Library (TPL) which provides a simplified use of parallelizing code and distributing work across multiple processors is once again unmatched on other platforms. Imagine the need to train or analyze large datasets and being limited to utilizing a single thread or having to read such large datasets from disk compared to leveraging LINQ's query capabilities on in-memory data structures and scaling that over multiple CPUs and all without significant overhead.

Cross-Platform Support

C# has been gaining in popularity over the years. It's now used by millions of developers around the world, and you can use it to create desktop applications, web applications, mobile applications and console apps. C# can also be used in combination with other frameworks like ASP.NET Core on Linux or MacOS — giving you more options when developing software for your organization.

The Oracle/Microsoft Collaboration

How is it that C# has so much momentum? The answer is simple: The Oracle/Microsoft collaboration.

For those who don't know, Oracle and Microsoft have been working together for years to ensure that SQL Server and Oracle are compatible with each other. They've also been working together on cloud computing, data science, and artificial intelligence. These two companies are now two of the biggest names in technology today—and they're both using C# as their primary programming language.

Contrarian Thoughts on R and Python

There are plenty of reasons to use R and Python.

R is a programming language and software environment for statistical computing and graphics. It was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, in 1995. The R language is widely used among statisticians and data miners for developing statistical software and data analysis.

Python, on the other hand, is a general-purpose programming language that allows users to build complex applications quickly without having to worry about memory management or other low-level details. Python has built-in data structures like lists, dictionaries (also called hash tables) etc., powerful standard libraries with hundreds of modules for web scraping, networking etc., simple syntax that makes it easy to learn yet very readable code which makes it great for beginners who want an easy entry point into coding

If you're looking for an alternative to Python or R, consider checking out C#. C# is a great option for data science because it has a lot of libraries and tools that allow you to do more complex operations. Additionally, it's easy to use machine learning tools in C# like TensorFlow, Keras, and Microsoft Cognitive Toolkit (CNTK).

CNTK has been used by Facebook in production since 2012 and is considered one of the best open-source deep learning frameworks available today! In addition, .NET Core 3 comes with new high performance numeric types which can be used for numeric intensive workloads like image processing. These numeric types are also portable across multiple operating systems including Windows 10, Linux and Mac OSX so they can be used with any device running these operating systems without having any issues related with compatibility issues between different platforms which often happen when writing code using other programming languages such as Python or R where most applications are written using just one language because they don't support all platforms out there which makes them unusable if someone wants their software written using another language but doesn't have access over those platforms due lack of support from those companies who created these languages (Google/Facebook etc.).

Betting Big on C#: Less Complexity, More Velocity

We’ve made a case for C# as a viable alternative to Python and R. It has a lot of libraries, it’s easy to use with .NET Core, and it has great support from Oracle. While Python and R are great programming languages, one must ask and remain cognizant of introducing the additional complexities and technical debt of needing multiple skillsets to manage a given enterprise's development environment. It could be said that identifying and going "all-in" on a technical stack that plays well with others and specifically its sibling products provides the utmost capability in delivering solutions at scale and velocity while limiting technical debt.

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