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dasari4kntr
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just sharing some tech learning road maps here…it could help for technology learners… you can update here if you come across any good cheat sheets or any tech roadmaps…    GOOGLE DRIVE

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FudPSjnWcAEfEFD?format=jpg&name=4096x409

 

https://drive.google.com/file/d/1Aqdsi2x_QmMnTnV0jB-g-bvMp-BgCvpw/view

 

𝗛𝗶𝘀𝘁𝗼𝗿𝘆 𝗢𝗳 𝗖# 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 During more than 20 years of development, we saw 11 versions of the C# language. Every version gave us some new and interesting features. Here is a brief overview of those features:

𝗖# 𝟭.𝟬 released with .NET 1.0 and VS2002 (January 2002).

𝗖# 𝟭.𝟮 released with .NET 1.1 and VS2003 (April 2003). The first version is called Dispose on IEnumerators which implemented IDisposable.

𝗖# 𝟮.𝟬 released with .NET 2.0 and VS2005 (November 2005). Major new features: generics, anonymous methods, nullable types, and iterator blocks.

𝗖# 𝟯.𝟬 released with .NET 3.5 and VS2008 (November 2007). Major new features: lambda expressions, extension methods, expression trees, anonymous types, implicit typing (var), and query expressions.

𝗖# 𝟰.𝟬 released with .NET 4 and VS2010 (April 2010). Major new features: late binding (dynamic), delegate and interface generic variance, more COM support, named arguments, tuple data type, and optional parameters.

𝗖# 𝟱.𝟬 released with .NET 4.5 and VS2012 (August 2012). Major features: async programming and caller info attributes.

𝗖# 𝟲.𝟬 released with .NET 4.6 and VS2015 (July 2015). Enabled by Roslyn. Features: initializers for automatically implemented properties, using directives to import static members, exception filters, element initializers, await in a catch, and finally, extension Add methods in collection initializers.

𝗖# 𝟳.𝟬 released with .NET 4.7 and VS2017 (March 2017). Major new features: tuples, ref locals and ref return, pattern matching (including pattern-based switch statements), inline out parameter declarations, local functions, binary literals, digit separators, and arbitrary async returns.

𝗖# 𝟳.𝟭 released with VS2017 v15.3 (August 2017). New features: async main, tuple member name inference, default expression, and pattern matching with generics.

𝗖# 𝟳.𝟮 released with VS2017 v15.5 (November 2017). New features: private protected access modifier, Span<T>, aka interior pointer, aka stackonly struct, and everything else.

𝗖# 𝟳.𝟯 released with VS2017 v15.7 (May 2018). New features: enum, delegate, and unmanaged generic type constraints. ref reassignment.

𝗖# 𝟴.𝟬 released with .NET Core 3.0 and VS2019 v16.3 (September 2019). Major new features: nullable reference-types, asynchronous streams, indices and ranges, readonly members, using declarations, default interface methods, static local functions, and enhancement of interpolated verbatim strings.

𝗖# 𝟵.𝟬 released with .NET 5.0 and VS2019 v16.8 (November 2020). Major new features: init-only properties, records, with-expressions, data classes, positional records, top-level programs, and improved pattern matching.

𝗖# 𝟭𝟬.𝟬 released with .NET 6.0 (November 2021). Major new features: record structs, struct parameterless constructors, interpolated string handlers, global using directives, file-scoped namespace declarations, extended property patterns, const interpolated strings, and more.

𝗖# 𝟭𝟭.𝟬 released with .NET 7.0 (November 2022). Major new features: file-scoped types, generic math support, auto-default structs, pattern match Span<char> on a constant string, extended nameof scope, UTF-8 string literals, required members, ref fields, and scoped ref, raw string literals, and more. What features do you use the most?

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https://microsoft.github.io/Data-Science-For-Beginners/#/

 

Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev)

Data Science For Beginners: Roadmap - Sketchnote by @nitya
Lesson Number Topic Lesson Grouping Learning Objectives Linked Lesson Author
01 Defining Data Science Introduction Learn the basic concepts behind data science and how it’s related to artificial intelligence, machine learning, and big data. lesson video Dmitry
02 Data Science Ethics Introduction Data Ethics Concepts, Challenges & Frameworks. lesson Nitya
03 Defining Data Introduction How data is classified and its common sources. lesson Jasmine
04 Introduction to Statistics & Probability Introduction The mathematical techniques of probability and statistics to understand data. lesson video Dmitry
05 Working with Relational Data Working With Data Introduction to relational data and the basics of exploring and analyzing relational data with the Structured Query Language, also known as SQL (pronounced “see-quell”). lesson Christopher
06 Working with NoSQL Data Working With Data Introduction to non-relational data, its various types and the basics of exploring and analyzing document databases. lesson Jasmine
07 Working with Python Working With Data Basics of using Python for data exploration with libraries such as Pandas. Foundational understanding of Python programming is recommended. lesson video Dmitry
08 Data Preparation Working With Data Topics on data techniques for cleaning and transforming the data to handle challenges of missing, inaccurate, or incomplete data. lesson Jasmine
09 Visualizing Quantities Data Visualization Learn how to use Matplotlib to visualize bird data 🦆 lesson Jen
10 Visualizing Distributions of Data Data Visualization Visualizing observations and trends within an interval. lesson Jen
11 Visualizing Proportions Data Visualization Visualizing discrete and grouped percentages. lesson Jen
12 Visualizing Relationships Data Visualization Visualizing connections and correlations between sets of data and their variables. lesson Jen
13 Meaningful Visualizations Data Visualization Techniques and guidance for making your visualizations valuable for effective problem solving and insights. lesson Jen
14 Introduction to the Data Science lifecycle Lifecycle Introduction to the data science lifecycle and its first step of acquiring and extracting data. lesson Jasmine
15 Analyzing Lifecycle This phase of the data science lifecycle focuses on techniques to analyze data. lesson Jasmine
16 Communication Lifecycle This phase of the data science lifecycle focuses on presenting the insights from the data in a way that makes it easier for decision makers to understand. lesson Jalen
17 Data Science in the Cloud Cloud Data This series of lessons introduces data science in the cloud and its benefits. lesson Tiffany and Maud
18 Data Science in the Cloud Cloud Data Training models using Low Code tools. lesson Tiffany and Maud
19 Data Science in the Cloud Cloud Data Deploying models with Azure Machine Learning Studio. lesson Tiffany and Maud
20 Data Science in the Wild In the Wild Data science driven projects in the real world. lesson Nitya
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