Data Science and Data Analyst
Introduction:
Data Science is the art and science of extracting actionable insight from raw data. We can define data science as multidisciplinary blend of data inference, algorithm development, and technology in order to solve analytically complex problems.
“Data Science is when you are dealing with Big Data, large amounts of data”.
• Data Science is mining large amounts of structured and unstructured data to identify patterns.
• Data Science includes a combination of programming, statistical skills, Machine Learning Algorithm.
• Data science is all about uncovering findings from data through different process, tools and techniques involved to identify patterns from raw data. These raw data are basically Big Data in form of structured, semi structured and unstructured data.
• Data science is the study of where information comes from, what it
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Data Science is a new interesting software technology, which is used to apply critical analysis, provide the ability to develop sophisticated models, for massive data sets and drive the business insights. Data science is an umbrella term used to describe how the scientific method can be applied to data in a business setting. Data science is also playing a growing and very important role in the development of artificial intelligence and machine learning. Although the differences exist, both data science and data analytics are important parts of the future of work and data. Data Analysts take direction from data scientists, as the former attempts to answer questions posed by the organization as a whole. Both terms should be embraced by companies that want to lead the way to technological change and successfully understanding the data that makes their organizations run. Company need both data scientists and data analytics in their project. Both are part of company’s
Data Analytics has significantly grown in less than two years, this quick growth has caused the company to evaluate the IT environment and its ability to support the growth and secure the data of the company. The CEO is expecting the company to grow 60% over the next two years; with the success of the company it has been determined that a change to the current IT environment and infrastructure must occur to better support the employees and the customer base.
Big Data is characterized by four key components, volume, velocity, variety, and value. Furthermore, Big Data can come from an array sources such as Facebook, Twitter, call
Computer Science is the study of information and how that information is represented, stored, and manipulated for other purposes. Consider how a personal computer uses an operating system to store, access, and run other programs to view, manipulate, replicate, and share information. That is what computer science is, essentially, it is the backbone of all that is computing.
8.) Data - means facts or information. People use data as a basis for drawing conclusions about the topic or theme they are studying.
You may ask what big data analytics is. Well according to SAS, the leading company in business analytics software and services describes big data analytics as “the process of examining big data to uncover hidden patterns, unknown correlations and other useful information that can be used to make better decisions.” As the goal of many companies which is to seek insights into the massive amount of structured, unstructured, and binary data at their disposal to improve business decisions and outcomes, it is evident why big data analytics is a big deal. “Big data differs from traditional data gathering due to that it captures, manages, and processes the data with low-latency. It also one or more of the listed characteristics: high volume, high velocity, or high variety. Big data comes from sensors, devices, video/audio, networks, log files, web, and social media which much of it is generated in real time and in a very large scale.”(IBM) In other words, companies moving towards big data analytics are able to see faster results but it continues to reach exceptional levels moving faster than the average person can maintain.
As noted above , Big Data that is a collection of data capacity in excess of those assumed applications and traditional tools . Size of Big Data is increasing day by day , and by 2012 , it size was estimated around a few dozen terabytes to multiple petabytes ( 1 petabyte = 1024 terabytes ) only for a set of data only.
Currently the world has a wealth of data, stored all over the planet (the Internet and Web are prime examples), but it is needed to be understand that data. It has been stated that the amount of data doubles approximately
A data warehouse comprised of disparate data sources enables the “single version of truth” through shared data repositories and standards and also provides access to the data that will expand frequency and depth of data analysis. Due to these reasons, data warehouse is the foundation for business intelligence.
Science is the observation of natural events and conditions in order to discover facts about them and to formulate laws and principles based on these facts. Academic Press Dictionary of Science & Technology --------------------------------------------------------------------- Science is an intellectual activity carried on by humans that is designed to discover information about the natural world in which humans live and to discover the ways in which this information can be organized into meaningful patterns. A primary aim of science is to collect facts (data).
For the past couple of decades the majority of businesses have wanted to construct a data-driven organization or company. Furthermore, companies around the world are considering harnessing data as a basis of competitive advantage over other companies. As a result, business intelligence and data science use are popular in many organizations today. The increase in adoption of these data systems is in response to the heavy rise in communications abilities the world over. Which, in turn ,has increased the need for data products. Indeed, the Data Scientist profession is emerging to be one of the better-paying professions due to the urgent need of their labor. This paper is going to discuss what business intelligence is all about and explain data science that is usually confused to be similar to business intelligence. I will tackle a brief overview of data scientists and their role in organizations.
Big data will then be defined as large collections of complex data which can either be structured or unstructured. Big data is difficult to notate and process due to its size and raw nature. The nature of this data makes it important for analyses of information or business functions and it creates value. According to Manyika, Chui et al. (2011: 1), “Big data is not defined by its capacity in terms of terabytes but it’s assumed that as technology progresses, the size of datasets that are considered as big data will increase”.
Companies have transformed technology from a supporting tool into a strategic weapon.”(Davenport, 2006) In business research, technology has become an essential means that many organizations use in their daily operations. According to the article, Analytics is a major technological tool used. It is described as “the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions."(Davenport, 2006) Data is compiled to enhance business practices. When samples are taken, they are used to examine research and understand how to solve problems or why situations are as they are. Furthermore, in this article, Thomas Davenport discusses analytics from a business standpoint. He refers to organizations that have been successful in their usage of data and statistical analysis. In addition, he also discusses how data and statistics can be vital in the efforts to improve the operations of businesses.
Business intelligence, or BI, is an umbrella term that refers to a variety of software applications used to analyze an organization’s raw data. BI as a discipline is made up of several related activities, including data mining, online analytical processing, querying and reporting. Data mining is the process of sorting through large amounts of data and picking out relevant information. It is usually used by business intelligence organizations, and financial analysts, but is increasingly being used in the sciences to extract information from the enormous data sets generated by modern experimental and observational methods.
Qualitative and quantitative research methods take different approaches to gathering and analysing information. Whether it is a qualitative or quantitative study, the research study begins with a question or series of questions. Both use rigorously designed studies to get the most accurate, detailed and complete results. Qualitative studies common methods are interviews, surveys and observation. A qualitative study aims to provide a detailed description of the study results, often using pictures and written descriptions to describe what the research revealed. A qualitative study looks at the big picture, helping researchers to narrow in on points of interest that then can be followed up on in a quantitative study. While a quantitative study has a narrower focus, it attempts to provide a detailed explanation of the study focus, along with this using numbers and statistics. And the results from a quantitative study can reveal bigger questions that call for qualitative study. Or vice versa a qualitative study may reveal at analysis that a more focus and direct approach may be needed. With both methods analysis is a key part of any study whether qualitative or quantitative.
Both computer forensic or forensic science and Biometrics sometimes may apply same identification methods; however, they do so for different purposes. Computer forensics or forensics science is based on history and a forensic investigator does not just pick a method in advance. In other words, forensics investigators are unaware of what they will find as evidence. In addition, the manner in which forensics tools and evidence are handled may have critical implications, which can make or break a case.