Data Science And Analytical Engineering
Data Science is a multi-disciplinary field that convert the data (various format) acquired from various sources into a common structure and focuses to achieve suitable insights from large amounts of data.

Big Data refers to a very large amount of data that cannot be effectively processed by existing traditional applications. The processing of Big Data starts with the collection of raw data that is impossible to store in the memory of a single computer.

When the world entered the age of big data, how and where such data would be stored was a major concern for businesses until 2010. Later, when Hadoop and similar systems successfully solved the storage problem, the new focus was on how to handle this big data. At this stage, the science of data has come into play.

Most of the data you process as a standard is structured and small; It is possible to analyze with simple BI tools. However, nowadays, most of the data is in unstructured or semi-structured form.

It is possible to identify your customers need in a timely manner as a result of the accurate analysis of the data (e.g. past purchase trends, age, income, periodic demands) obtained from your customers. With data science and analytical analysis, you can create more effective models using your structured or unstructured data and present your products to your customers more precisely.

Data science is the future of Artificial Intelligence. Understanding how data science adds value to your business will affect your company's future.

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Data Science - Data Analytics - Machine Learning

Demand in the areas of Machine Learning, Data Science and Data Analytics is increasing rapidly. Nowadays, it is crucial for companies to give right business decisions quickly and efficiently by acquiring meaningful results from big data

  • What is data science: Data science is a concept used to handle big data; it includes data cleaning, preparation and analysis. Data scientist has strong knowledge and experience about machine learning, understanding multiple analytic functions, SQL database coding, Python, SAS, R, Scala.

  • Who is a Data Analyst: A data analyst is a person with basic statistical background, who can visualize data and associate data points for results. The data analyst understands data complexity, mathematical statistics, PIG / HIVE, R and Python fluently.

  • What is Machine Learning: Machine Learning can be defined as the application of using algorithms to to learn the relevant subject from the data and then it predicts future trends related with this subject. The Artificial Learning specialist is familiar with basic computer systems, data modeling and evaluation, probability and statistics, and deep programming knowledge and skills.

What is Data Science?

People have been trying to define data science for a long time; I think the best way to answer and visualize this question is to use the Venn diagram. This Venn scheme, created by Hugh Conway in 2010, consists of three main frameworks: Knowledge of mathematics and statistics, substantive expertise and hacking skills. If you have full knowledge of all three, you already have enough knowledge in the field of data science.

Data science is a concept used to handle big data; it includes data cleaning, preparation and analysis. Data scientists find critical information from the datasets acquired from various sources by using predictive analysis and analysis techniques. They understand the data in accordance with business perspective and provide accurate predictions that can be used to strengthen critical business decisions.

Anyone who wants to build a strong career in this field must have skills in these three areas: Analytical, programming and substantive expertise. Those who want to go one level further must have strong knowledge of Python, SAS, R, Scala, SQL database coding experience, the ability to work with unstructured data from a variety of sources, such as video and social media, understanding multiple analytic functions, and machine learning.

Venn diagram
Venn Diyagram
Source: Drewconway
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