Bright Future Through Data Science Certification_theknowledgereview

Bright Future Through Data Science Certification

The continuous growth in Science and Technology gave birth to the giant in the field of information management called, “Data Science.”. Data Science has ranked as the fastest growing and hottest skill of the decade in technology promises. According to the major publication around the world Data Science jobs are called the hottest job in the world, bagging the highest salaries in the regular job market.  This course provides you with numerous opportunities such as the privilege to be an advisor to the top management of your company, making the highest salaries in the industry, working on interesting challenges every day and also an opportunity to work remotely when you wish to.

According to Harvard Business Review Data Science was called as “the sexy job of 21st Century”.

There is nothing more promising in future than to lay your hands on any certificate with good credits in Data Science.

The Fundamental Topics in Data Science: –  

The Introduction to Data Science in Colleges and Universities was mainly to grant a powerful insight into the internal and external features of foundational topics like Data Manipulation. Data Communication with Information Visualization. Data Analysis with Statistics and Machine Learning.

Data Manipulation: –

To control the flow of data in the storage and retrieval facility is achieved under the concept of Data Manipulation. To be able to do this in the technology systems and databases, you need a certain machine understandable bit of binary codes; DML; data manipulation language is a combination of several syntaxes similar to a computer programming language that can be used to select, insert, delete and update data in a database. There is another concept known as, “Performing read-only query of data,” this is sometimes also considered as a component of DML.

Data Analysis / Analytics

To understand the full concept of data analysis, you have to look into the meaning of the word, “Analysis”, Analysis is simply the breaking of a whole element into its separate component for individual examination. Data analysis involves the process of obtaining raw data and converting it into information useful for decision-making by the users. The main motive of collecting and analyzing data is answer questions, test hypotheses or disprove theories.

The concept of gaining access to every bit of data and trying to carve out meaningful information, to solve every problem that you may come across and leave a suggestion of how the possible growth of element can be added to these facts of examination is called as Data Analysis.

Data analysis: –

This is a process of inspecting, transforming, cleansing, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making. Is also known as Analysis of Data or Data Analytics.

It can also be defined as the process of systematically applying statistical and logical techniques to provide a concrete and informative description to illustrate, recap, condense and evaluate data. Data analysis can be found in business, social science and science domains.

Data mining is a type data analysis technique that deals with modeling and knowledge discovery. It’s mainly used for predictive rather than purely descriptive purposes. Business Intelligence in Data analysis covers the field that relies heavily on aggregation, focusing on business information. Data analysis is divided into descriptive statistics, confirmatory data analysis (CDA) and exploratory data analysis (EDA). EDA specializes on discovering new features in the data and CDA deals on confirming or falsifying existing hypotheses. Predictive analytics is the field of Specialty on the application of statistical models to predict forecasting or classification, while the concept of text analytics applies statistical, structural techniques and linguistic to extract and classify information from textual domains. Textual sources/domains can be described as a species of unstructured data.

Data analysis has a close link to data visualization and data dissemination and is sometimes used as a synonym for data modeling.

Machine Learning: –

The method of allotting the computer the access abilities to data without the involvement of the binary procedures or algorithmic strategies, to realize all of its sequence of arrangement and details of communication abilities and resources is called machine learning.

Through the concept of Machine Learning, the computers apply statistical learning techniques to automatically identify the patterns in data. These techniques are used for accurate predictions.

Machine learning is also a subfield of computer science that, as cited by Arthur Samuel in 1959, stating that it gives the computers the ability to learn without being explicitly programmed.

Data Communication and Visualization:-

Data communications incorporate the detailed study of digital data transmission between two or more computers. The possible transmission of data can only be possible when there is a network, a computer network or data network is a telecommunications network that allows computers to exchange data between several systems.

  • Information visualizationor information visualization: –

Deals with the study of interactive visual representations of abstract data, which helps to reinforce human cognition. The abstract data is made up of both numerical and non-numerical data, such as textual elements and geographic information.

  • Data visualizationor data visualization: –

It is best regarded as the modern equivalent of visual communication that enables users to analyze and reason about data and evidence. It deals with the creation and study of the visual representation of data, meaning “abstracted information, which includes attributes or variables for the units of information”.

Data visualization aids clear and efficient communication of data via statistical graphics, information graphics, and plots. Numbers are encoded using dots, lines, or bars for quantitative measurement. Data visualization is regarded as both an art and a science. It is viewed as either descriptive statistics nor as a grounded theory development tool. The Increase in the amount of data created by Internet activity and an expanding number of sensors brought about the concept of “Big Data or Internet of things. The art of processing, communicating and analyzing the data causes ethical and analytical challenges for data visualization, but this challenge is met by the practitioners called data scientists.

By – Chidiebere Moses Ogbodo