Statistics and Graphical Models in Data Science

Statistics:

For any aspiring data scientist, I would highly recommend learning statistics with a heavy focus on coding up examples, preferably in Python or R.
Mostly favorite series is the Statistical Learning series. The SL Series is a  great primer on statistical modeling / machine learning with applications in R.(Reference by qoura.com)

·        Crucial component:
Statistics is a crucial component of data science. At Twitch, The Professional data science team brings together three things: the first is statistics, second is  programming, and  third last is product knowledge. And we would never hire someone who wasn’t strong in stats. You can be a great programmer, but if you don’t know what Byes Rule is, then we have an engineering department I can point you to.”
The origin in statistics is mostly undeniable.

·        Programming:
Python: Python is a mostly used high-level programming language used for general-purpose programming the it is created by Guido van Rossum and the first version is  released in 1991. Python has a design philosophy which mostly emphasizes on code readability (notably using whitespace indentation to delimit code blocks rather than curly braces or keywords), and also a syntax which allows programmers to express concepts in very few lines of code than possible in languages such as C sharp , C++ or Java (script language). The language provides constructs intended to enable writing clear programs on both a small and large scale.

R-Learning: R is also open programming language and amazing software environment for statistical computing and graphics. R is supported by the R Foundation for Statistical Computing. The R language is mostly used between statisticians and data miners for developing statistical software and data analysis. In open Polls, surveys of data miners, and studies of scholarly literature databases show that R's popularity has increased substantially in recent years than others languages such as Python.


Graphical models and Network:

Graphical ways and instruments  can be a powerful way to understand complex and difficult  information and facilitate statistical computations and shows better and fair results. They are an important concept which play a role in helping us uncover patterns, function and our behavior inherent in networks of information, be it gene regulatory networks or  mostly social networks. Data scientists must learn methods for analyzing such networks, which begins with learning how to represent their system as a graph and includes analysis such as centrality measures, influence maximization, and using interference to gain insight on different graphical models. This helps them to find the local interactions that are amazing points  of large-scale network effects – the kind that businesses care about.
The case studies included in this course span all of these areas such as implementing different types of regression to visualize the gender wage gap and playing with deep neural networks to understand how they make decisions. Such case studies can be invaluable in helping data scientists understand how they can put what they learn to use in their own organizations.

A recent Gartner survey found only 41% of IT professionals thought their organizations were ready for the demands of digital business over the next two years, meaning 59% admitted they were not prepared. Don’t let yours be one of them: get your staff some effective training in the data science disciplines that the big data era requires.(full references by: Wikipedia) 

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