Diploma in Data Science

A diploma in Data Science, especially one delivered in collaboration with a reputable institution like the State University of New York (SUNY) — Potsdam, and a non-profit organization like the National Education Foundation (NEF), is an increasingly valuable qualification. This interdisciplinary field combines statistical methods, machine learning, and business analytics to extract knowledge and insights from data, which is essential for modern business decision-making. The program combines international academic recognition with practical training and industry-relevant digital credentials. Learners gain the ability to analyze, interpret, and visualize data to derive actionable business insights, build predictive models, and contribute to data-driven decision-making across industries. Delivery & Target Audience: Delivery: Blended learning (online instructor-led sessions, self-paced NEF modules, virtual labs, mentor guidance, and a final capstone project). Target Learners: +2 students aspiring to enter the Data Science field early. Final-year Computer Science / Engineering students seeking practical data science experience. Recent graduates or early-career IT professionals looking to transition into data analytics and machine learning roles. Individuals with basic programming knowledge, logical reasoning, and analytical aptitude. Duration: Typically 3 –6months, depending on the delivery partner and learning pace.

Core Modules

• Data Science Fundamentals & Analytics Lifecycle
• Programming for Data Science (Python/R)
• Data Wrangling & Preprocessing
• Data Visualization & Business Intelligence
• Statistical Analysis & Applied Mathematics
• Machine Learning I (Supervised Learning)
• Machine Learning II (Unsupervised Learning & Clustering)
• Deep Learning & AI Applications
• Big Data & Cloud Analytics
• Data Ethics, Governance & Security
• Capstone Project: End-to-End Data Science Solution
• Career Readiness Module (Portfolio, Resume, Mock Interviews)
• Mathematics & Statistics for Data Science: Probability, statistics, linear algebra, and calculus fundamentals.
• Programming & Data Handling: Python, R, SQL, and data wrangling using libraries such as Pandas, NumPy, and Scikit-learn.
• Data Visualization & Reporting: Tableau, Power BI, and Matplotlib/Seaborn for actionable insights.
• Machine Learning & Predictive Analytics: Regression, classification, clustering, and model evaluation techniques.
• Deep Learning Basics: Neural networks, CNNs, RNNs, and introduction to AI applications.
• Big Data & Cloud Analytics: Working with large datasets, Hadoop, Spark, and cloud-based tools (AWS, GCP, Azure).
• Data Ethics & Governance: Data privacy, ethical considerations, and compliance standards.
• Capstone Project: Hands-on real-world project demonstrating complete Data Science lifecycle: data acquisition → cleaning → analysis → visualization → predictive modeling.
• Programming: Python (with libraries like Pandas, NumPy, Scikit-learn) and R.

Learning Outcomes

• Apply statistical methods and machine learning algorithms to real-world problems
• Collect, clean, and preprocess large, complex datasets.
• Develop predictive models and interpret their results
• Communicate data-driven insights effectively to technical and non-technical stakeholders.
• Analyze large structured and unstructured datasets to extract actionable insights.
• Apply statistical and machine learning techniques for predictive and prescriptive analytics.
• Develop data-driven dashboards and visual reports for business decisions.
• Work with Big Data and cloud platforms to manage and process large datasets.
• Ensure data ethics, privacy, and compliance in analytics projects.
• Deliver a professional capstone project demonstrating applied Data Science skills to potential employers.

Specialization

Machine Learning, Business Analytics, Big Data, Data Engineering, and Artificial Intelligence