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
…show more content…
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.
In the past number of years data has grown exponentially. This growth in data has created problems that and a race to better monitor, monetize, and organize it. Oracle is in the forefront of helping companies from different industries better handle this growing concern with data. Oracle provides analytical platforms and an architectural platform to provide solutions to companies. Furthermore, Oracle has provided software such as Oracle Business Intelligence Suite and Oracle Exalytics that have been instrumental in organizing and analyzing the phenomenon known as Big Data.
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.
8.) Data - means facts or information. People use data as a basis for drawing conclusions about the topic or theme they are studying.
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.
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.
In today’s society, technology has become more advanced than the human’s mind. Companies want to make sure that their information systems stay up-to-date with the rapidly growing technology. It is very important to senior-level executives and board of directions of companies that their systems can produce the right and best information for their company to result in a greater outcome and new organizational capabilities. Big data and data analytics are one of those important factors that contribute to a successful company and their updated software and information systems.
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.
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.
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).
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.
The dynamics of our society bring many challenges and opportunities to the business world. Within the last decade, hundreds of jobs have emerged particularly in the technology sector to help keep up with the ever-changing world and to compete on a larger and better scale than the competition. Two key job markets and the basis of this research paper are business intelligence or BI and data mining or DM. These two fields play a very important role in small to large companies and are becoming higher desired sectors within the back offices of the workplace. This paper will explore what the meaning of BI and DM really is, how they are used and what we can expect as workers and learners of the technology and business fields for the future.
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 is a concept that has been misunderstood therefore I will be writing this paper with the intentions of thoroughly discussing this technological concept and all its dimensions with regard to what constitutes big data and how the term came about. The rapid innovations in Information Technology have brought about the realisation of big data. The concept of big data is complex and has different connotations but I intend to clarify its functions. Big data refers to the concept of a collection of large and complex amounts of data that are found extremely difficult to notate or even process by most on-hand devices and database technologies.