DIPLOMA IN BIG DATA

A Diploma in Big Data is typically a focused, short-to-mid-term program (often 6 months to 1 year) designed to equip students with the practical skills and theoretical knowledge needed to manage, process, analyze, and interpret large, complex datasets—known as Big Data. The program bridges the gap between traditional data handling and modern, scalable cloud and distributed computing technologies. 1. What is this Program? (Based on SUNY/NEF-associated programs) The programs generally associated with the SUNY/NEF collaboration are often affordable, short-term certificates or diplomas focused on high-demand technology and business skills. Objective: To provide a solid, practical foundation in Data Science, Big Data Analytics, Machine Learning (ML), and Artificial Intelligence (AI) principles, making graduates immediately valuable in the data-driven job market. Focus: Strong emphasis on hands-on, project-based learning using industry-standard tools and programming languages like Python, R, and SQL. Duration: Typically a few months to a year. Recognition: Graduates usually receive a Certificate/Diploma and sometimes a shareable digital badge, often recognized for credit transfer toward a Master's degree (subject to specific university rules).

Core Modules

• Foundational Programming & Math Python/R programming fundamentals, Statistical Methods, Linear Algebra, Probability. Proficiency in statistical computing, data structure manipulation, mathematical modeling.
• Big Data Ecosystem Introduction to Big Data (Volume, Velocity, Variety, Veracity), Hadoop, MapReduce, Apache Spark, Kafka. Expertise in designing and managing scalable data architectures, distributed processing.
• Data Storage & Management SQL (Relational Databases), NoSQL databases (e.g., MongoDB, Cassandra), Data Warehousing, Data Lakes. Ability to query, manage, and optimize data storage for Big Data applications.
• Data Analysis & Modeling Data Mining, Machine Learning (Supervised, Unsupervised Learning, e.g., Regression, Classification, Clustering), Deep Learning (Neural Networks, CNNs). Skill in developing predictive and prescriptive models to extract business insights.
• Data Visualization & BI Tools like Tableau, Power BI, or Matplotlib/Seaborn (in Python). Ability to create effective dashboards and visual stories to communicate complex results to stakeholders.
• Data Engineering/Prep Data Cleaning, ETL/ELT pipelines, Feature Engineering, Cloud Computing basics (AWS/Azure/GCP). Mastery in preparing raw data for analysis and building robust data infrastructure.
• Capstone ProjectA real-world, industry-relevant project integrating all learned concepts.Practical experience, portfolio development, and problem-solving under real constraints.

Learning Outcomes

• Design and manage scalable Big Data architectures.
• Clean, transform, and analyze large, unstructured datasets using programming languages like Python/R.
• Implement and fine-tune various machine learning and deep learning models.
• Communicate data findings effectively through compelling visualizations and reports.
• Apply critical thinking and problem-solving to real-world business challenges using data.

Specialization

Data Science, Big Data Engineering, Business Intelligence (BI) Analytics