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What Is MATLAB?

MATLAB is a high-performance language for technical computing. It integrates computation, visualization, and programming in an easy-to-use environment where problems and solutions are expressed in familiar mathematical notation. Typical uses include:

• Math and computation
• Algorithm development
• Modeling, simulation, and prototyping
• Data analysis, exploration, and visualization
• Scientific and engineering graphics
• Application development, including Graphical User Interface building

MATLAB is an interactive system whose basic data element is an array that does not require dimensioning. This allows you to solve many technical computing problems, especially those with matrix and vector formulations, in a fraction of the time it would take to write a program in a scalar noninteractive language such as C or Fortran.

The name MATLAB stands for matrix laboratory. MATLAB was originally written to provide easy access to matrix software developed by the LINPACK and EISPACK projects, which together represent the state-of-the-art in software for matrix computation.
MATLAB has evolved over a period of years with input from many users. In university environments, it is the standard instructional tool for introductory and advanced courses in mathematics, engineering, and science. In industry, MATLAB is the tool of choice for high-productivity research, development, and analysis.

MATLAB features a family of application-specific solutions called toolboxes. Very important to most users of MATLAB, toolboxes allow you to learn and apply specialized technology. Toolboxes are comprehensive collections of MATLAB functions (M-files) that extend the MATLAB environment to solve particular classes of problems. Areas in which toolboxes are available include signal processing, control systems, neural networks, fuzzy logic, wavelets, simulation, and many others.
The MATLAB System

The MATLAB system consists of five main parts:

1. The MATLAB language

This is a high-level matrix/array language with control flow statements, functions, data structures, input/output, and object-oriented programming features. It allows both "programming in the small" to rapidly create quick and dirty throw-away programs, and "programming in the large" to create complete large and complex application programs.

2. The MATLAB working environment.

This is the set of tools and facilities that you work with as the MATLAB user or programmer. It includes facilities for managing the variables in your workspace and importing and exporting data. It also includes tools for developing, managing, debugging, and profiling M-files, MATLAB's applications.

3. Handle Graphics.

This is the MATLAB graphics system. It includes high-level commands for two-dimensional and three-dimensional data visualization, image processing, animation, and presentation graphics. It also includes low-level commands that allow you to fully customize the appearance of graphics as well as to build complete Graphical User Interfaces on your MATLAB applications.

4. The MATLAB mathematical function library.

This is a vast collection of computational algorithms ranging from elementary functions like sum, sine, cosine, and complex arithmetic, to more sophisticated functions like matrix inverse, matrix eigenvalues, Bessel functions, and fast Fourier transforms.

5. The MATLAB Application Program Interface (API).

This is a library that allows you to write C and Fortran programs that interact with MATLAB. It include facilities for calling routines from MATLAB (dynamic linking), calling MATLAB as a computational engine, and for reading and writing MAT-files.

Introducing SPSS Statistics

SPSS is the abbreviation of Statistical Package for Social Sciences and it is used by researchers to perform statistical analysis. As the name suggests, SPSS statistics software is used to perform only statistical operations.

SPSS software is used to perform quantitative analysis and is used as a complete statistical package that is based on a point and click interface. This software has been widely used by researchers to perform quantitative analysis since its development in the 1960s by Norman H. Nie, in collaboration with C. Hadlai Hull and Dale Bent.

SPSS software can read and write data from other statistical packages, databases, and spreadsheets. When entering data into the software, one has to click on “variable view.” The variable view enables the user to customize it by data type and consists of the following headings: Name, Type, Width, Decimals, Label, Values, Missing, Columns, Align, and Measures. These headings enable the user to characterize the data.

SPSS is most often used in social science fields such as psychology, where statistical techniques are involved at a large scale. In the field of psychology, techniques such as crosstabulation, t-test, chi square test, etc., are available in the “analyze” menu of the software.

There is also an option in the software called “split file,” which is given in the “data” menu. This option is very useful for researchers who are performing comparative studies. Suppose researchers want to know the literacy rate of three regions. In this case, the split file option will help them get the result of three regions separately so that they can interpret and compare the literacy rate of the three regions.
SPSS software has a technique called missing value analysis, and this technique helps in making better decisions about the data. This technique enables the user to fill in the missing blanks in order to create better models to estimate the data. The analysis provides the user with procedures for data management and preparation.

SPSS involves some sophisticated inferential and multivariate statistical procedures such as factor analysis, discriminant analysis, analysis of variance, etc. SPSS, as the name suggests, is software for performing statistical procedures in the social sciences field. The major limitation of SPSS is that it cannot be used to analyze a very large data set. A researcher often gets a large data set in the field of medicine and nursing, so in those fields, the researcher generally uses SAS instead of SPSS to analyze the clinical data.

Data Science vs. Big Data vs. Data Analytics

Author: Avantika Monnappa

Data Science: Dealing with unstructured and structured data, Data Science is a field that comprises of everything that related to data cleansing, preparation, and analysis.

Data Science is the combination of statistics, mathematics, programming, problem-solving, capturing data in ingenious ways, the ability to look at things differently, and the activity of cleansing, preparing and aligning the data.

In simple terms, it is the umbrella of techniques used when trying to extract insights and information from data.

Big Data: Big Data refers to humongous volumes of data that cannot be processed effectively with the traditional applications that exist. The processing of Big Data begins with the raw data that isn’t aggregated and is most often impossible to store in the memory of a single computer.

A buzzword that is used to describe immense volumes of data, both unstructured and structured, Big Data inundates a business on a day-to-day basis. Big Data is something that can be used to analyze insights which can lead to better decisions and strategic business moves.

The definition of Big Data, given by Gartner is, “Big data is high-volume, and high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation”.

Data Analytics: Data Analytics the science of examining raw data with the purpose of drawing conclusions about that information.

Data Analytics involves applying an algorithmic or mechanical process to derive insights. For example, running through a number of data sets to look for meaningful correlations between each other.

It is used in a number of industries to allow the organizations and companies to make better decisions as well as verify and disprove existing theories or models.

The focus of Data Analytics lies in inference, which is the process of deriving conclusions that are solely based on what the researcher already knows.