2 YR ORDINARY DIPLOMA IN DATA SCIENCE AND BIG DATA ANALYTICS

Course Duration: 2 Years (4 semesters, including holidays and attachment)
Course Fee: 30,000 Kenyan Shillings per semester (each semester takes 3 months)

Course Overview:

This course focuses on data analysis, machine learning, and big data technologies, equipping students with skills in data-driven decision-making and analytics.

Year 1

Semester 1: Introduction to Data Science

Week 1-2: Introduction to Data Science

Overview of Data Science Concepts
Role of Data Scientist
Data Science Workflow
Setting Up Data Science Environment

Week 3-6: Data Wrangling and Preprocessing

Data Cleaning and Preparation
Exploratory Data Analysis (EDA)
Data Transformation Techniques
Practical Exercises

Week 7-10: Statistical Analysis

Descriptive Statistics
Inferential Statistics
Hypothesis Testing
Mini Project

Week 11-14: Introduction to Machine Learning

Machine Learning Basics
Supervised vs Unsupervised Learning
Model Evaluation and Selection
Practical Exercises

Week 15-16: Semester Review and Assessment

Review of Key Concepts
Practice Problems
Mid-Semester Exam

Semester 2: Big Data Analytics

Week 1-4: Introduction to Big Data

Big Data Concepts and Challenges
Hadoop Ecosystem (HDFS, MapReduce)
Big Data Storage and Processing
Practical Exercises

Week 5-8: Data Visualization

Principles of Data Visualization
Data Visualization Tools (Tableau, Power BI)
Designing Effective Data Visualizations
Mini Project

Week 9-12: Advanced Machine Learning

Advanced Machine Learning Algorithms (Decision Trees, SVM, Neural Networks)
Feature Engineering and Selection
Model Optimization Techniques
Practical Exercises

Week 13-14: Real-Time Analytics and Stream Processing

Stream Processing Frameworks (Apache Kafka)
Real-Time Data Analytics
Applications of Real-Time Analytics
Practical Exercises

Week 15-16: Semester Review and Assessment

Review of Key Concepts
Practice Problems
End-of-Semester Exam

Year 2

Semester 3: Applied Data Science

Week 1-4: Data Mining and Text Analytics

Text Mining Techniques
Sentiment Analysis
Topic Modeling
Practical Exercises

Week 5-8: Natural Language Processing (NLP)

Introduction to NLP
NLP Techniques (Tokenization, Named Entity Recognition)
Text Classification and Sentiment Analysis
Mini Project

Week 9-12: Deep Learning

Introduction to Deep Learning
Deep Neural Networks (CNNs, RNNs)
Transfer Learning
Practical Exercises

Week 13-14: Big Data Platforms and Technologies

Spark and Distributed Computing
NoSQL Databases (MongoDB, Cassandra)
Big Data Analytics in Industry
Practical Exercises

Week 15-16: Semester Review and Assessment

Review of Key Concepts
Practice Problems
Mid-Semester Exam

Semester 4: Capstone Project and Industry Applications

Week 1-4: Capstone Project

Project Planning and Design
Implementation of Data Science Solutions
Testing and Evaluation
Final Presentation and Evaluation

Week 5-8: Industry Attachment

Attachment in Data Science Roles
Applying Skills in Real-World Data Projects
Industry Best Practices
Practical Experience

Week 9-12: Professional Development

Career Pathways in Data Science
Building a Data Science Portfolio
Networking and Career Development
Practical Exercises

Week 13-16: Course Review and Final Exam

Comprehensive Course Review
Practice Problems
Final Exam

Attachment

Students will undertake an industry attachment to gain practical experience in data science and big data analytics. This attachment period allows students to apply their knowledge in real-world data projects, preparing them for professional roles in data-driven industries.

Certification

Upon successful completion of the course, students will receive an "Ordinary Diploma in Data Science and Big Data Analytics" certificate, recognizing their expertise and skills in data analysis and big data technologies.