Big data is extremely large data sets generated by various forms of technology typically studied by data analysts with the aid of specialized data processing software. These data sets can be anything from political mailing lists to the stats of a professional sports team. Data analytics is used to reveal patterns, trends and associations, especially relating to human behavior and interactions.
When beginning to examine the information, analysts collect and study the data, sorting through it to find relevant sets. Using the sifted sets of relevant data, they make predictions or advise clients on possible strategies. Examining data often falls into two phases: exploratory and confirmatory. Exploratory data analysis (EDA) and confirmatory data analysis (CDA) operate most effectively when they proceed side-by-side.
Exploratory Data Analysis
Exploratory (versus confirmatory analysis) is the method used to explore the big data set that will yield conclusions or predictions. According to the business analytics company Sisense, exploratory analysis is often referred to as a philosophy, and there are many ways to approach it. The process entails “figuring out what to make of the data, establishing the questions you want to ask and how you’re going to frame them, and coming up with the best way to present and manipulate the data you have to draw out those important insights.” Using exploratory analysis, data analysts are looking for clues and trends that will help them come to a conclusion.
The processes of EDA involve a myriad of tasks, including spotting mistakes and missing data; identifying important variables in the data set; testing a hypothesis related to a specific model; and establishing a model that can explain the data in the most succinct way possible. It also involves determining the best way to present the final assessment.
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Confirmatory Data Analysis
CDA is the process used to evaluate evidence by challenging their assumptions about the data. This part of the process is where they work backward from their conclusions and weigh the merits of the results of their work. It’s like examining evidence and questioning witnesses in a trial, trying to determine the guilt or innocence of the defendant.
CDA involves processes like testing hypotheses, producing estimates, regression analysis (estimating the relationship between variables) and variance analysis (evaluating the difference between the planned and actual outcome).
A Career in Data Analysis
Businesses are hungry for employees who can help them crunch numbers, dissect budgets and improve their bottom line. Business analysts identify trends and examine Big Data to streamline processes and make better decisions. Many marketing departments, for example, use data analytics to decide how best to sell their products to consumers. Experts look at existing trends in their customers’ demographics and make choices about where and on whom to spend their marketing budget.
Huge companies, like those in the health care and manufacturing industries, rely on entire departments of analysts, but even small companies benefit from staffing them. An increasing adoption of cloud computing – which is the practice of using a network of remote servers hosted on the Internet to store, manage, and process data, rather than a local or personal server – and a rise in IT services in health care settings is expected to increase the demand for data analysts. While the demand in the field is expected to grow, the current need testifies to the impact analysts can have on a company.
One career branch of data analytics is computer system analyst, and the job market for this position is experiencing steady growth with an expected 9 percent increase in jobs by 2026. That’s about 54,400 additional jobs. Computer systems analysts study computer systems and design solutions to help organizations operate more efficiently. Median annual pay for this position was $88,270 in 2017.
A general knowledge of computer programming is beneficial for those looking for a career in this field. Candidates with a master’s degree will enter the field at a higher rate of pay.
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Learn more about the benefits and differences between exploratory versus confirmatory analysis. Notre Dame of Maryland University Online can help you pursue a career in the challenging and in-demand field of data science. Our fully online Master of Science in Analytics can help you become an asset in your current role or prepare you for the jobs of the future. NDMU Online has strong networks with regional businesses, and 75 percent of graduates are directly applying their research projects to their jobs.