Master of Science in Data Science and Analytics | Units, Syllabus & Study Materials - YNetStudyHub

Master of Science in Data Science and Analytics Verified Syllabus

Master Master of Science in Data Science and Analytics with structured academic semester units, topic lecture notes, past examinations, CATs, and interactive 24/7 AI tutor support on YNetStudyHub.

10 Semester Units 133 Curriculum Topics 2 Years Commission for University Education (CUE) Aligned 24/7 AI Tutor Enabled
10 Units
133 Syllabus Topics
2 Years Duration
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Course Units & Curriculum 10 Units

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Big Data Analytics
9 Topics
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1 Case Studies in Big Data Analytics
2 Data Collection and Preprocessing
3 Data Mining and Machine Learning
4 Data Storage and Management
5 Data Visualization
6 Ethical and Privacy Issues in Big Data Analytics
7 Introduction to Big Data Analytics Free Preview
8 Real-time Analytics
9 Sentiment Analysis and Text Mining
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Past examination papers for this unit are being compiled.
1 Data Collection and Preprocessing
2 Data Visualization and Interpretation
3 Exploratory Data Analysis (EDA)
4 Feature Engineering and Selection
5 Introduction to Capstone Project in Data Science Free Preview
6 Machine Learning Modeling
7 Model Evaluation and Validation
8 Project Documentation and Presentation
9 Selecting a Capstone Project Topic
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Past examination papers for this unit are being compiled.
1 Anomaly Detection
2 Association Rule Mining
3 Classification Algorithms
4 Clustering Techniques
5 Data Preprocessing
6 Feature Selection and Dimensionality Reduction
7 Introduction to Data Mining Free Preview
8 Pattern Recognition
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Past examination papers for this unit are being compiled.
1 Choosing the Right Visualization
2 Data Interpretation through Visualization
3 Data Visualization Best Practices
4 Data Visualization Tools
5 Design Principles in Data Visualization
6 Geographic Data Visualization
7 Interactive Data Visualization
8 Introduction to Data Visualization Free Preview
9 Real-world Applications of Data Visualization
10 Storytelling with Data
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Past examination papers for this unit are being compiled.
1 Artificial Neural Networks (ANN)
2 Case Studies in Deep Learning
3 Convolutional Neural Networks (CNN)
4 Ethical Considerations in Deep Learning
5 Generative Adversarial Networks (GANs)
6 Introduction to Deep Learning Free Preview
7 Optimization Techniques in Deep Learning
8 Recurrent Neural Networks (RNN)
9 Regularization and Dropout in Neural Networks
10 Transfer Learning and Fine-Tuning
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Past examination papers for this unit are being compiled.
1 Big Data and Data Mining
2 Big Data and Data Science
3 Data Acquisition and Cleaning
4 Data Analysis Techniques
5 Data Collection and Cleaning
6 Data Ethics and Privacy
7 Data Preprocessing
8 Data Science Applications
9 Data Science Lifecycle
10 Data Science Project Management
11 Data Types and Data Sources
12 Data Visualization
13 Data Visualization
14 Data Visualization
15 Ethical Considerations in Data Science
16 Ethics and Privacy in Data Science
17 Exploratory Data Analysis (EDA)
18 Exploratory Data Analysis (EDA)
19 History of Data Science
20 Introduction to Machine Learning
21 Introduction to Python for Data Science
22 Introduction to R for Data Science
23 Machine Learning Basics
24 Machine Learning Fundamentals
25 Model Evaluation and Validation
26 Model Evaluation and Validation
27 Statistical Analysis for Data Science
28 What is Data Science? Free Preview
29 What is Data Science?
30 What is Data Science?
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Past examination papers for this unit are being compiled.
1 Ethical Considerations in Machine Learning
2 Feature Engineering
3 Introduction to Machine Learning Free Preview
4 Model Deployment and Scaling
5 Model Evaluation and Selection
6 Reinforcement Learning
7 Supervised Learning Algorithms
8 Unsupervised Learning Algorithms
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Past examination papers for this unit are being compiled.
1 Chatbots and Conversational Agents
2 Ethical and Social Implications of NLP
3 Ethical Considerations in NLP
4 Ethical Considerations in NLP
5 Introduction to Natural Language Processing Free Preview
6 Introduction to Natural Language Processing
7 Introduction to Natural Language Processing
8 Language Modeling
9 Language Modeling and Word Embeddings
10 Language Models in NLP
11 Machine Translation
12 Named Entity Recognition
13 Named Entity Recognition
14 Named Entity Recognition (NER)
15 Natural Language Generation
16 Preprocessing Text Data
17 Sentiment Analysis
18 Sentiment Analysis
19 Sentiment Analysis
20 Sequence-to-Sequence Models
21 Sequence-to-Sequence Models
22 Text Classification
23 Text Classification
24 Text Classification
25 Text Generation
26 Text Preprocessing in NLP
27 Text Preprocessing in NLP
28 Transformer Models
29 Transformer Models
30 Word Embeddings
No standalone PDF materials uploaded yet. Use the Topics & Lecture Notes tab above to read notes for each topic.
Past examination papers for this unit are being compiled.
1 ANOVA and Chi-Square Tests
2 Clustering and Classification
3 Data Visualization Techniques
4 Dimensionality Reduction
5 Ethics and Bias in Statistical Analysis
6 Hypothesis Testing
7 Introduction to Statistical Analysis Free Preview
8 Probability Distributions
9 Regression Analysis
10 Time Series Analysis
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Past examination papers for this unit are being compiled.
1 Advanced Time Series Models
2 Introduction to Time Series Analysis Free Preview
3 Seasonal Adjustment
4 Stationarity in Time Series
5 Time Series Components
6 Time Series Decomposition
7 Time Series Forecasting
8 Time Series Model Evaluation
9 Time Series Visualization
No standalone PDF materials uploaded yet. Use the Topics & Lecture Notes tab above to read notes for each topic.
Past examination papers for this unit are being compiled.

Curriculum Architecture

This program is structured into comprehensive academic units combining theoretical foundations, analytical research, practical tutorials, and continuous assessments. Each unit is mapped to key learning outcomes aligned with higher education qualification frameworks.

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Career & Professional Trajectories

Graduates of Master of Science in Data Science and Analytics are qualified for diverse professional opportunities across industry, public service, corporate consulting, research institutions, international organizations, and postgraduate specialization.

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