• Compilers,  Data Science

    What is a Compiler?

    Compiler is a program installed on the computer which translates the high-level language which is in the form of human understandable form into a machine level language that is understood by the computer. The machine understandable language is usually in the form of binary representation. This is done in order to create an executable program.

    Types of compilers:

    • Native code compiler– it converts the code from one language to another when the program has to run on the same kind of platform. This executable code that is generated by the compiler can only be run by that platform.
    • One pass compiler– it is a kind of compiler that creates the executable file in exactly one pass.
    • Threaded code compiler– replaces a string by a binary code.
    • Cross compiler– it is the inverse of native code compiler. It converts the source code into the kind of executable code which can run on different kinds of platforms.
    • Source to source compiler– it takes input in a high level language and then converts that code’s output into another high level language only.
    • Source compiler– is the type of compiler that converts a code in a high level language into a code in assembly language only.

    There are many kinds of compilers out of which the above mentioned are the most important one that you should know.

    Different steps the compiler has to perform are- pre-processor, lexical analysis, generation of parse tree, annotation of the parsed tree (semantic analysis), intermediate representation, code optimization and target code generation. These steps can be categorized as front end, back end and middle end respectively, where intermediate representation falls into the category of middle end.

    Compilers come under the category of a language processor. Interpreters are also a kind of language processors that directly run a program without converting the code to an executable one. Common examples where compilers are used are- C/C++, Java, etc. Common examples where interpreters are used are- Python.

    python compiler

    Various applications of compilers are:

    • FORTRAN
    • COBOL
    • LISP

    Working of a compiler

    Initially, one instruction from the source code is taken and is converted into tokens. These tokens are fed into the lexical analyzer which in turn converts these tokens into a parse tree. This parse tree is then annotated by checking the type of each token in the instruction. For example, in case there is an int data type, it is converted into float data type. Then out of the annotated parse tree, an intermediate code is got and that is in turn converted into an intermediate code. This code will not be optimal i.e, we would want a code that is in its simplest form. So this code is fed to the code optimizer which will make the code much simpler. The final target code is then got by converting the optimized code into a machine level instruction.

    Compilers are very important when you code in languages like c and c++, Java etc. There have been adult apps and meet n fuck sites that have gone down do to issues in this area. Without compilers, this code will never run. Compilers convert the source files into an executable code with extension .exe.

  • Data Science

    Programming Languages Best Suited For Data Science

    The insights extracted from Big Data, by Data Science, are to a large extent dependant on the Programming Languages used. Some of the best programming languages to learn for data science are now reviewed to guide our readers.

    Some of the best programming languages to learn for data science are as follows:

    • R: Along with Python, this is the major language for Data Science now. Scheme or S was created by John Chambers in 1976, at Bell Labs. R is an open source implementation of S, by Language Designers Robert Gentleman and Ross Ihaka. S has been combined with lexical scoping semantics to create R. It is an interpreted language, for procedural programming. It provides software for statistical computing and graphics, supported by the R Foundation for Statistical Computing. Business support is provided by Microsoft, RStudio and so on.
    • Python: This language has been dubbed the easiest language to learn and read. It can interface with high-performance algorithms written in Fortran or C, so it is the leading programming language for open Data Science. It was created by Dutchman Programmer Guido van Rossum in the ‘80’s at CWI (Centrum Wiskunde & Informatica) as a successor to his own team’s ABC language (which followed SETL). It was conceived in order to interface with the Amoeba Operating System. It was released in December 1989. The software includes CPython, Psyco, Nuitka, SageMath, Ubuntu, Gentoo Linux, Sugar and XO.
    • SQL: Originally known as SEQUEL, and now standing for “Structured Query Language”, this language was developed during the ‘70’s by Researchers Donald Chamberlain and Richard Boyce of IBM. It was spurred by the published paper by Edgar Frank Todd in 1970, entitled “A Relational Model of Data For large Shared Data Banks”. It is popularly used for querying and editing the information stored in a relational database. Particularly large Databases can be managed by its fast processing time. This is an essential Programming Language skill demanded by employers from candidates.
    • Java: This is the most popularly used programming language for Android Smartphone applications. It is also the favorite language for the development of IoT (Internet of Things) and Edge devices. The Java compiler is written in C and C++ to create a “simple to use” language, and since it is English-based, numeric code knowledge is not required. Java is therefore a High Level language. It is an exceptional computing system supported by Oracle that creates portability between different platforms. It runs on the JVM (Java Virtual Machine). MNC companies use it frequently, to take advantage of its portability between platforms. This is another essential skill for software architects and engineers.
    • Scala: The Scalable Language was designed by Martin Odersky as a generic purpose, high level, and multi-paradigm programming language in order to provide support for the functional programming approach. Scala Programs can do the JVM (Java Virtual Machine) run, and can convert to ‘bytecodes’. Scala is inter-operable with Java, and is therefore a superb general purpose language, as well as being perfect for Data Sciences. As an example, the Cluster Computing Framework, ‘Apache Spark’, is written in Scala.
    • Julia: Julia has similar Syntax as Python, and is also a dynamic, high-level High-performance programming language for Technical Computing, Distributed Parallel Execution, Numerical Accuracy and Extensive Mathematical Function Library. Its performance is as good as ‘C’, which is a statically compiled language. It has been designed for Numerical Computing, but can also be used as a general purpose language. Julia is fast developing as a viable alternative for Python, and is rapidly acquiring a following of top class developers. Experts feel Julia could soon overtake ‘C++’. Julia was designed and developed by Jeff Bezanson, Allen Edelman, Stefan Karpinski, Viral B. Shah and others. Julia is much more adapted for Data Science than the presently popular Python. It is specifically aimed at Data Mining, Distributed and Parallel Computing, Large Scale Linear Algebra, and Machine Learning. All of these make it far better than Python for use in Data Science.
    • TensorFlow: TensorFlow Software was built by Google with an underlying C++ Programming Language. This is an AI Engine, and Coders can use C++ or Python. It uses Data Flow graphs to build models. Large scale neural networks with many layers can be built with TensorFlow, and can be used for Perception, Understanding, Discovering, Prediction and Creation. TensorFlow was Open-sourced by Google in late 2015, and is now the most popular ‘Deep Learning’ framework.
    • MATLAB: This is a multi-pardigm Numerical Computing environment, and Proprietary Programming Language. It is a perfectly suited language for mathematicians and scientists dealing with complex mathematical requirements, such as, Matrix Algebra, Fourier Transforms, and Image and Signal Processing. Latest release was in September 11th 2019. It was designed by Cleve Moler and developed by MathWorks.
  • what is data science
    Data Science,  Databases

    Field of Data Science Explained

    Data science is nothing but a comprehensive sector that involves several methods based on science, procedures, calculations and systems to get information from the data which could be either well structured or quite unstructured in nature. Data science is simply the utilization of the hardware, programming system and highly complex algorithms to find solutions to a lot of problems. It runs on the basic concept of bringing together various fields like stats, analysis of data, learning of machine and methods related to them. It inculcates tricks and theory which are taken from a lot of fields involving the concept of maths, stats, science of computer, and science of information. Data science is nowadays becoming a quite famous term in the communities of executives of business. In spite of this fact, a lot of critics of academic field and various journalists saying that they cannot find any difference between stats and data science, while some other people take it as a pretty popular set of words for “mining of data”. A journalist has come up and argued that the word data science is a kind of buzzword that is, it does not have an explanatory definition and hence, he has very replaced it with the word “analytics of business”. This term has been chosen with reference to programs like degree graduates. 

    Steps that help an individual to Be a data scientist

    There are majorly three steps that help an individual to become a data scientist. They are as follows:

    • The person has to achieve a degree of bachelor in the sector or branch of information and technology, maths, computer science, physics, or any other field related to computers or data science.
    • Then the person has to gain a master’s degree in the same fields or any other computer related field.
    • The person then is required to gain a lot of experience in the field of data science.

    Requirements related to the education of a data scientist

    There are a number of pathways that lead and land a person in the desired career in the field of data science, but in spite of having the zest and plan with complete intent and focus, we all know that it is not entirely possible to start and have a career in this field of data science without having a college degree and further education. A person will as the last requirement require degree of bachelor of duration of four years. Although the person has to keep the fact in his or her mind that about seventy three percent of the skilled persons working in the data science industry, posses a degree of graduation and some thirty eight percent of people even posses a PhD. If the people have aimed at a goal that is a highly advanced position of leadership, then they will surely have to gain either a degree of masters or a doctorate.

    There are some schools that offer degrees in data science. This degree will surely provide the person the skills that are necessary to process and perform the analysis of a very typical collection of data, and it will inculcate a lot of information that is technical and is in relation to computers, statistics and techniques for analysis, and many more. The majority of programs of data science will also possess an innovative and creative element of analysis, it will allow the person to make decisions for judgements that have findings as their basis.

    Career path of a data scientist

    Although people possess the skills required to become a successful data scientist directly coming from college, it usually happens with people to require some of the skills while the training on the job before they are up to the main work and run their careers in fully efficient ways. This training given at jobs before the start of the main work generally has its core around the topic of specific programs of the company and its profits along with the internal system, but there are chances that it might involve techniques of the advanced analysis system that are usually not being taught in their colleges. At the end, we can say that the data science world is an ever evolving area, hence the people working in this sector are necessarily required to constantly make their efficiency and skills up to date. They are continuously being trained to be at the position of the leading person and also at the progressing state of technology and information.

    Data scientists generally work in a lot of creative and efficiency enhancing settings, but the majority of these people are working in settings that are like a usual office which allows the employees to work in teams for projects and to have a good communication.

  • Data Science,  Data Science Education

    Best Online Data Science Schools

    Online data science provides the students with a flexible and affordable path towards a very lucrative data science job. According to the bureau of Labor Statistics the projected employment growth for database administrators is 11% with the current average salary for database administrators standing at $87,020. The increasing popularity of data analytics and data base administrators adds to the ever increasing in employment for data scientists at cloud computing firms.

    Big data is not limited to cloud computing firms. Startups and established businesses are leveraging the power of data science to improve their operations and increase their profits. Competition to acquire talented data scientists is fierce and across a diverse amount of industries and spaces. Dating and hookup apps turn towards data and analytics to help their members find a fuck buddy. Huge adult sites like PornHub utilize data to monetize content. Sports teams use data to make in game decisions. Really the leveraging of data and therefore the need for data scientists continues to grow exponentially.

    Below is a list of top online data school science programs which have been selected based on the quality of the program, type of course provided, school awards, faculty, rankings and reputation.

    University of Southern California (USC)

    USC offers more than just 100-degree options which include master of science in computing science that totals up to 32 units. The USC programs tends to train students in the fundamentals of computer science thus enabling them to retrieve, store, analyze and visualize data. The graduates of online data science are equipped with data science jobs in diverse industries like healthcare, transportation, and energy. The advantage with the program is that it makes it possible for distance students to view the same lecturers as on campus students. Those who get to join live classes are able to ask the professor questions in real time.

    Students who study full time get to earn their data science program in less than 18 months with part time students getting to earn their degree in 2-3 years. The core topics of data science include database systems, algorithm analysis and artificial intelligence.

    University of North Carolina at Chapell Hill

    UNC offers online business administration course with a specialization in data analytics and decision-making concentration. The program offers research-based seminars where online data students create, implement and communicate data driven data-driven business strategies. In order to earn their data science degree, the students have to complete 66 credits.

    Students have to take one core class in analytical tools with the remainder in of the online data science master classes consisting of elective options such as digital marketing, information modelling and management.

    University of California – Berkeley

    Drawing from a multidisciplinary pedagogy the UC Berkeley’s. They train students to design innovative services, applications as well as business solutions. The students tend to use data analytics tools so as to work with complex data and solve-real world problems. The students are also able to pursue their full-time degree or through part-time enrollment.

    The 27-unit curriculum consists of mainly online colleges course in statistics, machine learning, data analytics, data engineering, research design and application. Students who do not have adequate object-oriented programming experience tend to complete a python of data science class.

    University of Illinois at Urbana- Champaign

    data science education

    The university of Illinois offers academic programs and enrolls more than 72,000 students across the globe. The catalogue includes mainly 32-credit program that is centered on cloud computing, data mining, machine learning, data analytics and data visualization. Students who are earning their online data masters program develop skills in statistics and information science as this enables them to apply meaningful information from vast and unstructured data.

    Georgia Tech

    Georgia Tech offers 13 graduate programs including data analytics where learners get to take full online college courses through blackboard learn. Learners at data analytics take full online college programs through developing their mathematical and analytical skills to extract relevant information from data streams. The program provides full admission for graduate students and offers one of the lowest online tuition rates.

    They total up to 30 credits which provide online master degree plan that consist of the required classes in specialization, choosing from analytical tools, business management and big data. Companies like Apple, Bumble, Uber and other tech giants often recruit from this program.

    Southern Methodist University

    The innovative Southern Methodist University uses a skilled based curriculum that combines self-paced online college courses in addition to live weekly classes and collaborative projects. The online masters project centers in data science degree on statistics and visualization, training students to undermine, analyze and apply data so as to make strategic business decisions.

    Rochester Institute of Technology

    It was founded in 1829 and the Rochester Institute of Technology enrolls over 3,200 graduates in accessible academic programs. The 30-credit data science degree program emphasizes experimental learning and career development. The data science programs cultivate theoretical knowledge and practical skills through problem solved assignments.