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Overview

Data Science is an interdisciplinary field that incorporates computer science, statistics, and mathematical modeling, and has applications in business, government, the life sciences, social sciences and many other areas. It is a broad term that encompasses math, statistics, and other tools applied to data sets in order to extract knowledge and insight from said data. Data analytics takes a more focused view than data science. Data analysts have a specific goal in minding when they are sorting through data. Industries like healthcare, gaming, and travel use data science, while data science is common in internet searches and digital advertising.

Data analytics is the pursuit of extracting meaning from raw data using specialized computer systems. These systems transform, organize, and model the data to draw conclusions and identify patterns. The difference between data analysis and data analytics is that data analytics is a broader term of which data analysis forms a subcomponent. Data analysis refers to the process of compiling and analyzing data to support decision making, whereas data analytics also includes the tools and techniques used to do so.

Undergraduate Programs

A Bachelor of Science in Data Analytics prepares students for careers in the emerging high-demand field of big data. You will learn the tools and techniques to collect, manage, explore and analyze large, complex data-sets, and you will be able to effectively communicate your findings to help companies in a wide range of industries make better business decisions. A typical curriculum includes courses in data analytics, computer science, statistics, mathematics, and communication. Some programs combine data science and analytics, which may include courses in data mining, machine learning, visualization techniques, predictive modeling, and statistics.

Data Analytics is a subset of data science and focuses on the analysis of data and its utilization to support insight and decision-making. You may expect coursework in predictive analytics, linear programming, network analysis, and data visualization. Predictive analytics that focus on correlative analysis, predicts relationships between known random variables or sets of data in order to identify how an event will occur in the future. For example, identifying the where to sell personal power generators and the store locations as a function of future weather conditions (e.g., storms). Data visualization is prevalent in business, for example, a company may implement data visualization software to track their marketing initiatives.

Employment Prospects

A degree in data science or analytics is not the only degree needed for employment. There are data analysts who have undergraduate degrees in mathematics, statistics, political science, business, economics, and others. Job sites, such as GlassDoor, confirm this statement as there are data analyst jobs seeking those with degrees in math, computer science, engineering, and physics.

Jobs may be on the rise in this field along with the pervasiveness of automation and artificial intelligence. According to Oxford University researchers, automation may affect approximately 47 percent of U.S. jobs within the next two decades. This will require more business decisions from the analysis provided by data analysts. Computer software programs can produce the data but making sense of the data requires the expertise of a data analyst.

According to PayScale, an Entry-Level Data Analyst earns an average salary of $55,754 per year. The skills that increase pay the most for this job are Python, R, and Tableau Software. Glassdoor, the employment website, reports the median salary for a data analyst at $65,470. The same site lists the average base pay for a data scientist at $120,931.