2 YR ORDINARY DIPLOMA IN DATA SCIENCE AND BIG DATA ANALYTICS

Course Duration: 2 Years (18-24 months)(4 semesters, including holidays and attachment)
Course Fee: 30,000 Kenyan Shillings per semester (each semester takes 3 months)

weekly commitment: minimum of 2 hours per day

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.