Diploma in Data Science and Analytics | Units, Syllabus & Study Materials - YNetStudyHub
Diploma (TVET Level 5)

Diploma in Data Science and Analytics Verified Syllabus

Master Diploma 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 172 Curriculum Topics 2-3 Years TVET Level 5 CDACC / KNEC Aligned 24/7 AI Tutor Enabled
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10 Units
172 Syllabus Topics
2-3 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
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2 Data Collection and Preprocessing
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3 Data Mining and Machine Learning
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4 Data Storage and Management
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5 Data Visualization
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6 Ethical and Privacy Issues in Big Data Analytics
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7 Introduction to Big Data Analytics Free Preview
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8 Real-time Analytics
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9 Sentiment Analysis and Text Mining
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1 Choosing a Capstone Project Topic Free Preview
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2 Conducting Literature Review for Capstone Project
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3 Creating Visual Aids and Presentations
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4 Data Collection and Analysis Methods
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5 Developing a Capstone Project Proposal
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6 Final Presentation and Defense
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7 Peer Review and Feedback
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8 Writing the Capstone Project Report
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1 Choosing the Right Visualization
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2 Data Interpretation through Visualization
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3 Data Visualization Best Practices
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4 Data Visualization Tools
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5 Design Principles in Data Visualization
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6 Geographic Data Visualization
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7 Interactive Data Visualization
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8 Introduction to Data Visualization Free Preview
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9 Real-world Applications of Data Visualization
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10 Storytelling with Data
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1 Data Cleaning Techniques
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2 Data Integration and Aggregation
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3 Data Transformation Methods
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4 Dimensionality Reduction Techniques
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5 Exploratory Data Analysis (EDA)
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6 Handling Imbalanced Data
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7 Handling Time Series Data
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8 Introduction to Data Wrangling Free Preview
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1 Activation Functions
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2 Artificial Neural Networks
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3 Backpropagation
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4 Convolutional Neural Networks (CNNs)
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5 Convolutional Neural Networks (CNNs)
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6 Convolutional Neural Networks (CNNs)
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7 Deep Reinforcement Learning
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8 Deep Reinforcement Learning
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9 Ethical Considerations in Deep Learning
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10 Ethical Considerations in Deep Learning
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11 Ethical Considerations in Deep Learning
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12 Future Trends in Deep Learning
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13 Future Trends in Deep Learning
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14 Generative Adversarial Networks (GANs)
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15 Generative Adversarial Networks (GANs)
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16 Generative Adversarial Networks (GANs)
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17 Introduction to Deep Learning Free Preview
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18 Introduction to Deep Learning
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19 Introduction to Deep Learning
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20 Loss Functions
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21 Neural Networks
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22 Neural Networks
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23 Optimization Techniques in Deep Learning
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24 Recurrent Neural Networks (RNNs)
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25 Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM)
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26 Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM)
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27 Training Deep Learning Models
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28 Transfer Learning
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29 Transfer Learning
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30 Transfer Learning and Fine-Tuning
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Past examination papers for this unit are being compiled.
1 Big Data and Data Mining
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2 Big Data and Data Science
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3 Data Acquisition and Cleaning
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4 Data Analysis Techniques
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5 Data Collection and Cleaning
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6 Data Ethics and Privacy
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7 Data Preprocessing
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8 Data Science Applications
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9 Data Science Lifecycle
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10 Data Science Project Management
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11 Data Types and Data Sources
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12 Data Visualization
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13 Data Visualization
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14 Data Visualization
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15 Ethical Considerations in Data Science
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16 Ethics and Privacy in Data Science
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17 Exploratory Data Analysis (EDA)
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18 Exploratory Data Analysis (EDA)
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19 History of Data Science
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20 Introduction to Machine Learning
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21 Introduction to Python for Data Science
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22 Introduction to R for Data Science
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23 Machine Learning Basics
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24 Machine Learning Fundamentals
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25 Model Evaluation and Validation
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26 Model Evaluation and Validation
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27 Statistical Analysis for Data Science
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28 What is Data Science? Free Preview
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29 What is Data Science?
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30 What is Data Science?
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Past examination papers for this unit are being compiled.
1 Data Preprocessing
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2 Ethical Considerations in Machine Learning
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3 Introduction to Machine Learning Free Preview
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4 Model Deployment and Monitoring
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5 Model Evaluation and Selection
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6 Neural Networks and Deep Learning
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7 Supervised Learning
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8 Unsupervised Learning
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Past examination papers for this unit are being compiled.
1 Chatbots and Conversational Agents
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2 Ethical and Social Implications of NLP
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3 Ethical Considerations in NLP
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4 Ethical Considerations in NLP
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5 Introduction to Natural Language Processing Free Preview
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6 Introduction to Natural Language Processing
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7 Introduction to Natural Language Processing
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8 Language Modeling
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9 Language Modeling and Word Embeddings
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10 Language Models in NLP
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11 Machine Translation
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12 Named Entity Recognition
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13 Named Entity Recognition
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14 Named Entity Recognition (NER)
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15 Natural Language Generation
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16 Preprocessing Text Data
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17 Sentiment Analysis
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18 Sentiment Analysis
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19 Sentiment Analysis
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20 Sequence-to-Sequence Models
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21 Sequence-to-Sequence Models
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22 Text Classification
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23 Text Classification
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24 Text Classification
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25 Text Generation
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26 Text Preprocessing in NLP
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27 Text Preprocessing in NLP
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28 Transformer Models
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29 Transformer Models
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30 Word Embeddings
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Past examination papers for this unit are being compiled.
1 Case Studies in Predictive Analytics
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2 Data Collection and Preparation for Predictive Analytics
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3 Ethical Considerations in Predictive Analytics
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4 Exploratory Data Analysis (EDA) for Predictive Analytics
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5 Feature Selection and Engineering
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6 Introduction to Predictive Analytics Free Preview
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7 Model Deployment and Monitoring
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8 Model Evaluation and Selection
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9 Predictive Modeling Techniques
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Past examination papers for this unit are being compiled.
1 ANOVA and Experimental Design
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2 Bayesian Statistics
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3 Bayesian Statistics
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4 Bayesian Statistics
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5 Correlation Analysis
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6 Correlation and Regression Analysis
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7 Data Visualization
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8 Data Visualization
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9 Data Visualization
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10 Descriptive Statistics
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11 Descriptive Statistics
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12 Experimental Design
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13 Hypothesis Testing
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14 Inferential Statistics
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15 Inferential Statistics
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16 Introduction to Statistics Free Preview
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17 Introduction to Statistics
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18 Introduction to Statistics
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19 Machine Learning and Statistics
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20 Probability Theory
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21 Probability Theory
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22 Probability Theory
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23 Regression Analysis
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24 Regression Analysis
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25 Sampling and Estimation
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26 Sampling Methods
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27 Sampling Techniques
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28 Statistical Testing
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29 Time Series Analysis
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30 Time Series Analysis
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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 Diploma 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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