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Woodcroft University was founded with a mission to make high-quality education accessible to learners everywhere. Built for the digital era, Woodcroft embraces innovation, flexibility, and global connection, offering students a modern academic experience grounded in excellence. Through immersive online learning, industry-aligned programs, and a diverse international community, Woodcroft continues to empower learners to grow, advance, and create meaningful impact in their fields. 

7901 4th St N, Suite 300,
St. Petersburg, FL 33702, USA info@woodcroftedu.us
Address

7901 4th St N, Suite 300,
St. Petersburg, FL 33702, USA

Email

info@woodcroftedu.us

Phone Number

+1 850-312-3081

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Master of Science (MSc) in Data Science

Duration: 1 Year (12 Months)  |  Mode: 100% Online (Blended Asynchronous + Live Faculty Sessions)  |  Total Credits: ~60 (Equivalent)

Program Philosophy

This Master’s program develops end-to-end data science professionals with strong foundations in statistics, machine learning, data engineering, and applied AI—combined with real-world projects, research exposure, and executive-level decision-making skills. Graduates will be capable of designing, deploying, evaluating, and governing data-driven systems across industries.

Program Structure Overview

Phase Duration Focus
Semester I Months 1–4 Core Foundations
Semester II Months 5–8 Advanced Data Science & AI
Semester III Months 9–12 Capstone, Research & Specialization

Semester I – Core Foundations (Months 1–4)

Module 1: Mathematical Foundations for Data Science
Credits: 6
Topics Covered
  • Linear Algebra (Vectors, Matrices, Eigenvalues)
  • Probability Theory & Random Variables
  • Statistical Distributions
  • Optimization Techniques
  • Numerical Methods
Learning Outcomes
  • Build mathematical intuition behind ML algorithms
  • Apply optimization techniques to model training
Module 2: Programming for Data Science
Credits: 6
Topics Covered
  • Python for Data Science (NumPy, Pandas)
  • Data Structures & Algorithms (DSA essentials)
  • Functional & Object-Oriented Programming
  • Code Optimization & Debugging
  • Version Control (Git)
Learning Outcomes
  • Write efficient, production-ready code
  • Manage collaborative data science projects
Module 3: Statistics & Exploratory Data Analysis
Credits: 6
Topics Covered
  • Descriptive & Inferential Statistics
  • Hypothesis Testing
  • Sampling Techniques
  • Exploratory Data Analysis (EDA)
  • Visualization using Matplotlib & Seaborn
Learning Outcomes
  • Perform statistical reasoning on real datasets
  • Identify data patterns, biases, and anomalies
Module 4: Data Management & Databases
Credits: 6
Topics Covered
  • SQL & Relational Databases
  • NoSQL Databases (MongoDB)
  • Data Warehousing Concepts
  • ETL Pipelines
  • Cloud-based Data Storage
Learning Outcomes
  • Design scalable data storage systems
  • Integrate structured and unstructured data sources

Semester II – Advanced Data Science & AI (Months 5–8)

Module 5: Machine Learning
Credits: 8
Topics Covered
  • Supervised Learning (Regression, Classification)
  • Unsupervised Learning (Clustering, PCA)
  • Model Evaluation & Validation
  • Feature Engineering
  • Bias-Variance Trade-off
Learning Outcomes
  • Build and evaluate ML models
  • Apply ML pipelines to business problems
Module 6: Deep Learning & Neural Networks
Credits: 6
Topics Covered
  • Artificial Neural Networks (ANN)
  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN, LSTM)
  • Transfer Learning
  • Introduction to Transformers
Learning Outcomes
  • Develop deep learning models for complex tasks
  • Understand modern AI architectures
Module 7: Big Data & Distributed Systems
Credits: 6
Topics Covered
  • Hadoop Ecosystem
  • Apache Spark
  • Distributed Computing Principles
  • Real-Time Data Processing
  • Cloud Platforms (AWS / Azure / GCP)
Learning Outcomes
  • Process large-scale datasets
  • Deploy scalable analytics solutions
Module 8: Applied AI & Domain Applications
Credits: 6
Topics Covered
  • Natural Language Processing (NLP)
  • Computer Vision
  • Recommender Systems
  • Time-Series Forecasting
  • AI in Finance, Healthcare, Marketing
Learning Outcomes
  • Apply AI techniques to industry use cases
  • Design domain-specific data science solutions

Semester III – Capstone, Research & Specialization (Months 9–12)

Module 9: Data Ethics, Governance & Responsible AI
Credits: 4
Topics Covered
  • Data Privacy Laws (GDPR, HIPAA)
  • Ethical AI Frameworks
  • Model Explainability (XAI)
  • Bias & Fairness in AI
  • Risk & Compliance
Learning Outcomes
  • Build responsible and compliant AI systems
  • Understand governance requirements in real deployments
Module 10: Research Methods & Scientific Writing
Credits: 4
Topics Covered
  • Research Design
  • Quantitative & Qualitative Methods
  • Literature Review Techniques
  • Academic Writing & Referencing
  • Reproducible Research
Learning Outcomes
  • Conduct independent research
  • Prepare publishable technical reports
Module 11: Capstone Project (Mandatory)
Credits: 8
Structure
  • Industry-oriented or Research-oriented project
  • Problem Definition & Proposal
  • Data Collection & Engineering
  • Model Development & Evaluation
  • Final Thesis & Defense
Example Capstone Themes
  • Predictive Analytics Platform
  • AI-Driven Recommendation Engine
  • Large-Scale Data Pipeline
  • Research-based ML Optimization Study
Learning Outcomes
  • Demonstrate full data science lifecycle expertise
  • Build a portfolio-grade project

Assessment Methodology

  • Assignments & Case Studies
  • Practical Labs & Coding Tasks
  • Mid-Term & Final Evaluations
  • Capstone Project & Viva Voce
  • Peer & Faculty Review

Graduate Outcomes

Graduates will be prepared for roles such as:

  • Data Scientist
  • Machine Learning Engineer
  • AI Analyst
  • Data Engineer
  • Business Intelligence Lead
  • Research Associate

Tools & Technologies Covered

  • Python, R (Optional)
  • SQL, MongoDB
  • TensorFlow, PyTorch
  • Spark, Hadoop
  • Power BI / Tableau
  • Cloud Platforms (AWS / Azure / GCP)