PROFICIENCY DIPLOMA IN BIG DATA ANALYTICS

Total Duration: 4-6 months
Course Fee: 30,000 Kenyan Shillings

weekly commitment: 2 hours per week

Course Overview
The Proficiency Diploma in Big Data Analytics equips students with the skills and knowledge required to analyze large datasets and derive valuable insights. Participants will learn about data processing techniques, statistical analysis, machine learning algorithms, and data visualization tools. This program prepares students for careers in data analysis, business intelligence, and data-driven decision-making.

Semester 1

Week 1: Introduction to Big Data Analytics

Overview of big data and its significance
Applications of big data analytics in various industries

Week 2: Data Collection and Storage

Methods for collecting and storing big data
Introduction to databases and data warehouses

Week 3: Data Cleaning and Preprocessing

Techniques for cleaning and preprocessing data
Handling missing values and outliers

Week 4: Introduction to Statistical Analysis

Basic statistical concepts for data analysis
Descriptive and inferential statistics

Week 5: Data Visualization Techniques

Importance of data visualization in analytics
Tools and libraries for data visualization

Week 6: Exploratory Data Analysis (EDA)

Techniques for exploring and summarizing data
Visualizing relationships and patterns in data

Week 7: Introduction to Machine Learning

Fundamentals of machine learning algorithms
Supervised vs. unsupervised learning approaches

Week 8: Regression Analysis

Linear and logistic regression models
Application of regression in predictive analytics

Week 9: Classification and Clustering

Classification algorithms (e.g., decision trees, k-nearest neighbors)
Clustering algorithms (e.g., k-means, hierarchical clustering)

Week 10: Time Series Analysis

Analyzing time-dependent data patterns
Forecasting techniques in time series analysis

Week 11: Mid-Semester Review and Assessment

Review of topics covered in the first semester
Mid-semester exams and practical assessments

Week 12: Big Data Tools and Technologies

Overview of big data processing frameworks (e.g., Hadoop, Spark)
Hands-on experience with big data tools

Semester 2

Week 13: Advanced Data Analytics Techniques

Advanced statistical methods (ANOVA, MANOVA)
Feature engineering and dimensionality reduction

Week 14: Machine Learning for Big Data

Scaling machine learning algorithms for big data
Deep learning techniques and applications

Week 15: Natural Language Processing (NLP)

Processing and analyzing text data
Applications of NLP in sentiment analysis, chatbots, etc.

Week 16: Data Mining and Pattern Recognition

Techniques for mining patterns and associations in data
Application of data mining in business intelligence

Week 17: Big Data Security and Ethics

Security challenges in big data environments
Ethical considerations in big data analytics

Week 18: Real-time Data Analytics

Processing and analyzing streaming data
Implementing real-time analytics solutions

Week 19: Big Data Project Management

Project management principles for big data projects
Agile methodologies in big data analytics

Week 20: Final Big Data Project and Presentation

Developing and presenting a comprehensive big data analytics project
Final project evaluation and feedback

Certification
Upon successful completion, students will receive a proficiency diploma in Big Data Analytics.