Big Data Essentials
Big Data embraces technology, decision-making and public policy. Supplying the technology is a fast-growing market, increasing at more than 30 percent a year and likely to reach $24 billion by 2016, according to a forecast by IDC, a research firm. Big Data is poised to be the next big trend in management.
This certificate will focus on managing Big Data analytics to understand customers, develop new products and cut operational costs. Learn how to gather and analyze large amounts of data, and how to use that data to manage and make important financial decisions.
Most of the jobs emerging in Big Data require knowledge of programming and the ability to develop applications, as well as an understanding of how to meet business needs. This certificate will provide entrepreneurs or managers the opportunity to advance in a strong new growth area, and provide consulting services to companies expanding into Big Data.
Who would be suited to take this program?
People currently working in computing who want to advance their knowledge and catch up in the growing field of Big Data. Skills most often mentioned in connection with Big Data jobs include math, statistics, data analysis, business analytics and even natural language processing.
Demand is brisk for people with data skills. The McKinsey Global Institute, the research arm of the consulting firm, projects that the United States needs 140,000 to 190,000 more workers with "deep analytical" expertise and 1.5 million more data-literate managers, whether retrained or hired, by 2020.
What Will I Learn?
Efficiency, powerful programming techniques and algorithms to transform large quantities of information into business intelligence.
In this program you’ll gain 12 credits that can be applied to NJIT master’s programs, and learn:
- Mathematical properties and usage of database programming languages.
- Methods of database design, conceptual modeling, and physical storage for Big Data sets.
- Fundamental notions of concurrency control and recovery in database systems.
- Goal tree search, logic and deduction, uncertainty, fuzzy logic, knowledge representations, machine learning, vision, and action planning.
- Methods for association and dependency analysis, classification and predication, and clustering analysis.
- Current trends in data mining, and data mining for scientific, medical and engineering applications.
Why Study Big Data Essentials at NJIT?
One of the nation's leading public technological universities, New Jersey Institute of Technology (NJIT) prepares students to be leaders in the technology-dependent economy of the 21st century. The university's multidisciplinary curriculum and computing-intensive approach to education provides the technological proficiency, business know-how and leadership skills that future CEOs and entrepreneurs will need to succeed. With an enrollment of almost 10,000 graduate and undergraduate students, NJIT offers small-campus intimacy with the resources of a major public research university.
The graduate certificate’s narrow focus allows you to dig deep into this specific topic, and start applying your knowledge sooner. It’s possible to earn the certificate entirely through online courses, so you can more easily fit it into your busy life. And whether you take courses online or on campus, you’ll learn from NJIT’s distinguished professors and instructors of the Department of Computer Science.
Prerequisites
An undergraduate Computer Science degree from an accredited institution is recommended. Three years or more in programming or database work.
NJIT’s standard admission requirements apply to this graduate certificate.
Related Degree Programs
Credits from this graduate certificate can be applied toward the NJIT MS in Data Science or the MS in Computer Science
COURSE DESCRIPTIONS
IS 601. Web Systems Development. 3 credits, 3 contact hours.
Prerequisites: NONE Students will gain experience in open source web development through an intensive hands-on project, applying real-world problem-solving skills to meeting information systems requirements. Students will learn Web development principles, as well as professionally relevant skills including industry standards, conventions, and procedures within large-scale programming projects. Also covered are the communication tools, technologies, and practices that individuals use to coordinate and collaborate within the open source software development community.
IS 650. Data Visualization and Interpretation. 3 credits, 3 contact hours.
The course will focus on training students in data visualization techniques and relevant tools. They will learn theoretical aspects of visualization design, and gain practical experience in interpreting data as well as critiquing and comparing visualization techniques. They will develop interactive visualization interfaces as part of a class project. Students will also gain a broad understanding of how visualization can enhance data interpretation and play a key role in the data science pipeline. Finally, recent advances will be presented in the areas of information visualization, visual analytics, and human-data interaction.
IS 657. Spatiotemporal Urban Analytics. 3 credits, 3 contact hours.
Prerequisite: IS 665 or equivalent. This course teaches essential concepts and skills needed to efficiently develop spatiotemporal thinking, create a spatiotemporal model, and visualize/model the urban spatiotemporal relationships in the open source environment. Students will learn about big data analytic skills that integrate large open source data and traditional data by investigating the relationship between virtual and physical worlds in the built environment.
IS 665. Data Analytics for Info System. 3 credits, 3 contact hours. Prerequisite: IS 601 This course gives a graduate level introduction to data analysis, probability and statistics from an information systems perspective, including many of the techniques that are most relevant to the profession of Data Scientist for business, data and web analytics, as well as current data sets. We will learn and conduct Python, MATLAB and R based manipulation of data. Course topics include the rudiments of probability and random variables, estimation, special distribution and sampling, Markov processes, hypothesis testing, graphics and visualization.
What are the Required Courses?
| Code | Title | Credits |
|---|---|---|
| Core Courses | ||
| CS 644 | Introduction to Big Data | 3 |
| CS 636 | Data Analytics with R Program | 3 |
| Electives | ||
| Select two of the following: | 6 | |
| Machine Learning | ||
| Artificial Intelligence | ||
| Advanced Machine Learning | ||
| High Performance Analytics Dat | ||
| Applied Statistics | ||
| Web Systems Development | ||
| Data Visualization and Interpretation | ||
| Spatiotemporal Urban Analytics | ||
| Data Analytics for Info System | ||
| Total Credits | 12 | |
