Graduate Certificate in Predictive Analytics
Students can obtain a Graduate Certificate in Predictive Analytics as part of a broader program of study or as a stand-alone certificate, which requires less expense and is less time-consuming than a Master's degree. Learn about the common courses and admittance requirements for these certificates.
Program Information for Graduate Certificates in Predictive Analytics
Predictive analytics goes a step beyond data analytics to focus on making predictions based on the past. Professional certificates in predictive analytics at the graduate level offer flexibility; students can earn these certificates online, in a classroom setting, or using a combination of both learning environments. Earning a Graduate Certificate in Predictive Analytics takes approximately nine months and requires about 9-16 credit hours. Coursework includes education on topics like the following.
Typically, the course predictive analytics, or one with a similar title, provides students the fundamentals of the art and science of predictive analytics, its appropriate uses and limitations, and the relationship between predictive analytics and improved business performance. Oftentimes, this type of course explores the regulatory, ethical and compliance issues related to data-driven business problems and solutions. Usually an introductory or fundamental course in a program, predictive analytics educates students on the use of big data in business and methods for data storage and extraction.
In a course like prescriptive analytics, students explore, analyze and leverage data to implement the best course of action in any given business situation. This course commonly focuses on using data analytics software to draw up specific recommendations for actionable plans. In-depth study of methodology, regression, machine learning tools, risk and mitigation concepts are also subjects explored in this type of course. Students evaluate a wide range of business scenarios, using data analysis, as they relate to appropriate solutions or company improvements.
Data Mining and Analysis
Data mining is a tool for discovering patterns and relationships that might be useful in decision-making problems. Coursework in data mining and analysis helps students develop the quantitative and analytical skills necessary to solve industry problems through modeling, analyzing and interpreting data. Students in this course will explore statistical significance in big data, how to develop and use predictive models for large databases or through web mining, and how to turn this knowledge into actionable data for a company. Other potential topics in this class include: using statistical methods to extract meaning, strategic decision-making processes, graphical and visual data, and the R statistical environment.
Data Management and Data Warehousing
In a course like data management and data warehousing, students are often given the opportunity to participate in hands-on labs and student-led discussions. The focus of this class is the examination of data management principles and the study of different types of data, whether it's structured in a SQL database or unstructured in a NoSQL database system. Other subjects of study often include: data governance, stewardship, database administration, data quality management, document management and issues in data warehousing. Students can learn best practices for data warehousing and combining raw data with theories that can translate into business intelligence.
Business Intelligence and Data and Text Visualization
This class commonly focuses on business intelligence (BI), which comprises technologies, artificial intelligence, machine tools, applications, and processes with the goal of improving visualization and modeling data. The ultimate goal of BI is to aid users in making informed decisions using data science and analysis. Students participating in this kind of course are often asked to work with data and text visualization within an open-source programming environment as well as build interactive visualizations for the web.
Admittance Requirements for Graduate Certificates in Predictive Analytics
To be admitted to a graduate certificate program in predictive analytics, applicants typically must produce transcripts of a Bachelor's degree displaying a 3.0 or higher GPA. While a specific B.A. or B.S. degree is usually not required, admission to some certificate programs does require some prerequisite coursework in a relevant field, like statistics, or two years of related work experience. GRE scores are not often needed for the application, however a statement of purpose, resume or CV, and references are generally part of the process. Non-native English speakers will need to produce scores from the TOEFL exam.
Whether seeking a Graduate Certificate in Predictive Analytics as a terminal goal or as part of a broader program of study, there are common courses and admittance requirements to consider. Graduate certificate programs in predictive analytics (and similarly titled certificates) offer students knowledge and skills in predictive and prescriptive analytics, data mining, principles of data management, warehousing and more.