Certificate in Hyperscale Computing

Learn to build scalable systems for cloud, data, and hyperscale computing environments.

Apply Now | Request Info Program available in Newark, Jersey City, and Online

Program Summary

The Hyperscale Computing graduate certificate equips professionals with technical and hands-on experience in designing scalable systems for big data and cloud environments. Designed for those with a computing-related bachelor’s degree, the program covers cloud computing, parallel processing, advanced databases, and big data architectures. The four-course curriculum, typically completed in two semesters, combines core coursework with electives in areas such as data analytics and web mining. Through applied projects and theoretical training, students learn to build fault-tolerant, distributed systems capable of handling massive datasets and high-throughput workloads.

All Credits earned in the certificate can be applied towards the M.S. in Computer Science. Some courses also apply towards other M.S. degrees offered by the Ying Wu College of Computing. 

Essential Information

Detailed curriculum and course requirements for the Certificate in Hyperscale Computing is available in the program catalog.

Admission Prerequisites ▼

The graduate certificate program in Hyperscale Computing requires an undergraduate degree in a computing discipline. At a minimum, such a degree must have exposed the prospective student to materials from at least two from the following courses:

Students without a prior degree in computing may want to consider the Graduate Certificate in Computer Science that is designed to support a smooth transition to computing.

Core Courses & Competencies ▼
Select at least three of the following:
CS/DS 644 Introduction to Big Data
Skills: HDFS, MapReduce, Distributed Computing, Big Data Analytics, Big data Tools
Environments & Tools: Java, Hadoop, Hbase, Spark, Pig, Oozie, AWS
CS 643 Cloud Computing
Skills: Cloud Platform Development, Parallel Computing Techniques, Containerization & Virtualization, Distributed Storage Systems, Cloud Security & Privacy, Data Analytics Architecture, Machine Learning Deployment, DevOps Practices, Cloud Performance Analysis, Serverless Computing
Environments & Tools: AWS EC2, AWS S3, AWS Lambda, Google Cloud Platform, Microsoft Azure, Apache Spark, Apache Hadoop, Docker, Kubernetes, Java, Python, Linux (Amazon), MapReduce, Apache Storm
CS 632 Advanced Database System Design
Skills: Relational Databases; Database Programming; Object-oriented data; XML data; JSON data; NO-SQL databases; Graph databases
Environments & Tools: SQL, PL/SQL, Neo4j, MongoDB
DS 642 Applications of Parallel Computing
Skills: Parallel Algorithm Design, Shared-Memory Programming, GPU Computing with CUDA, Distributed Memory Programming, Message Passing Interface (MPI), OpenMP Programming, Matrix Multiplication Optimization, Fast Fourier Transform, Parallel Graph Algorithms, Dynamic Load Balancing, Performance Analysis and Bottleneck Identification
Environments & Tools: CUDA, OpenMP, MPI, OpenCL, FFTW (Fast Fourier Transform library), ACCESS High-Performance Computing Platform, Bridges-2 Supercomputer, C/C++, Matrix Multiplication Libraries, GPU Programming Tools
Elective Courses & Competencies ▼
Select at most one of the following:
CS 634 Data Mining
Skills: Association Rule Mining, Classification, Clustering, Decision Trees, K-Means Clustering, Graph Clustering, Text Mining, Web Structure Mining, Web Usage Mining, Web Crawling, Time Series Data Mining, Data Warehousing, Algorithm Implementation
Environments & Tools: Python, scikit-learn, Pandas, NumPy, Matplotlib, Jupyter Notebooks
IS 665 Data Analytics for Information Systems
Skills: Statistical Analysis; Data Visualization; Database and Data Warehouse Design; Predictive Modeling; Clustering Analysis; Association Rule Mining; Regression Analysis; Algorithm Implementation
Environments & Tools: Python, scikit-learn, Pandas, NumPy, Matplotlib, Jupyter Notebooks
Program Outcomes ▼
After completing the program, graduates will be able to:
  • Design and implement distributed computing architectures that scale horizontally for high-volume data and user traffic.
  • Apply infrastructure automation, containerization, and orchestration tools to manage large-scale deployments.
  • Diagnose performance bottlenecks and architect fault-tolerant, high-availability systems.
  • Optimize cloud platforms and microservices architectures for efficient resource use and cost control.
Campus Options & Cost ▼
Tuition & Fees by Campus (based on AY 2024-2025 rates)
  • Online: $13,716
  • Jersey City: $13,132-$14,880
  • Newark, NJ residents: $17,192-$18,540
  • Newark, non-NJ residents: $23,900-$25,248

The lower amounts assume the student takes two courses in a summer semester.

For details, see NJIT's Tuition and Fee Schedule.
For information about the cost of living, see Tuition and Costs at NJIT.