Data Science

 Three Pillars Mantra of Data Science:


Pillar: 1
    
    1. IT/Computer Science

Computer science is the study of algorithmic processes, computational machines and computation itself. As a discipline, computer science spans a range of topics from theoretical studies of algorithms, computation and information to the practical issues of implementing computational systems in hardware and software.

    2.  Maths and Statistics

Mathematical statistics is the application of probability theory, a branch of mathematics, to statistics, as opposed to techniques for collecting statistical data.

    3.  Domain/Business Knowledge

Business knowledge is a business owner's extensive reservoir of understanding on customers' needs and preferences, business environments and their dynamics, staff skills, experiences and potentials, and the business' overall foreseeable direction.

Pillar: 2

    1. Machine Learning

Machine learning is the study of computer algorithms that improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence.

    2.  Traditional Research

Traditional research, also known as experimental or quantitative research, as I remember it as a high schooler and as an elementary teacher, is a very systematic process that demands a standard scientific method.

    3. Software Development

Software development is the process of conceiving, specifying, designing, programming, documenting, testing, and bug fixing involved in creating and maintaining applications, frameworks, or other software components.


Pillar: 3

    1. Data Science

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains.




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