DIPLOMA IN Data Management and Analytics (DDMA)

PROFICIENCY DIPLOMA IN Data Management and Analytics (DDMA)

Below is a detailed 6-Month Diploma in Data Management and Analytics (DDMA) course structure, broken down by weeks. The curriculum covers essential topics in data management, analytics, visualization, and business intelligence

Course Duration: 4 to 6 months (16 to 24 weeks)
Course Fee: 30,000 Kenyan Shillings
Weekly Commitment: Minimum of 2 hours per day
Course Overview:

in Data Management and Analytics (DDMA) – Course Outline (6 Months)

Duration: 6 Months (24 Weeks)
Total Modules: 6
Total Units: 24
Mode: Online/In-Class

Module 1: Introduction to Data Management & Analytics (DM-101)

(Weeks 1-4)
This module introduces students to the basics of data management and analytics concepts.

• Week 1: Foundations of Data Management (DM-101A)

• • What is Data Management?
  • Types of Data (Structured vs. Unstructured)
  • Data Lifecycle & Governance
  • Introduction to Relational Databases

• Week 2: Introduction to Analytics & Data Processing (DM-101B)

• • Data Analytics Overview
  • Types of Analytics (Descriptive, Diagnostic, Predictive, Prescriptive)
  • Data Collection and Cleaning
  • Data Processing Basics

• Week 3: SQL for Data Management (DM-101C)

• • Introduction to SQL
  • CRUD Operations (Create, Read, Update, Delete)
  • SQL Joins and Aggregation Functions
  • Data Querying and Filtering

• Week 4: Data Warehousing & ETL Concepts (DM-101D)

• • Data Warehouse vs. Database
  • ETL (Extract, Transform, Load) Processes
  • Data Lake vs. Data Warehouse
  • Popular ETL Tools (Informatica, Talend, etc.)

Module 2: Programming for Data Analytics (PA-102)

(Weeks 5-8)
This module focuses on programming skills required for data analytics.

• Week 5: Python for Data Analysis – Part 1 (PA-102A)

• • Python Basics (Variables, Data Types, Loops)
  • Functions and Modules
  • Introduction to Pandas & NumPy

• Week 6: Python for Data Analysis – Part 2 (PA-102B)

• • Data Wrangling with Pandas
  • Data Cleaning & Handling Missing Values
  • Data Visualization with Matplotlib & Seaborn

• Week 7: R for Data Analytics (PA-102C)

• • Introduction to R Programming
  • Data Manipulation with dplyr
  • Data Visualization with ggplot2

• Week 8: Working with APIs & Web Scraping (PA-102D)

• • Introduction to APIs
  • Web Scraping with BeautifulSoup & Selenium
  • Data Extraction from JSON & XML

Module 3: Data Analytics & Business Intelligence (DA-103)

(Weeks 9-12)
This module covers analytics techniques and business intelligence tools.

• Week 9: Statistical Analysis for Data Science (DA-103A)

• • Descriptive & Inferential Statistics
  • Probability Distributions
  • Hypothesis Testing

• Week 10: Exploratory Data Analysis (EDA) (DA-103B)

• • Data Cleaning & Preprocessing
  • Feature Engineering
  • Outlier Detection

• Week 11: Business Intelligence Tools (DA-103C)

• • Introduction to BI Tools (Tableau, Power BI)
  • Creating Dashboards & Reports
  • Data Storytelling

• Week 12: Predictive Analytics & Machine Learning Basics (DA-103D)

• • Introduction to Machine Learning
  • Regression Analysis (Linear & Logistic)
  • Decision Trees & Random Forest

Module 4: Big Data & Cloud Computing (BD-104)

(Weeks 13-16)
This module introduces students to big data technologies and cloud platforms.

• Week 13: Introduction to Big Data (BD-104A)

• • What is Big Data?
  • Characteristics of Big Data
  • Introduction to Hadoop & Spark

• Week 14: Working with NoSQL Databases (BD-104B)

• • Introduction to MongoDB & Cassandra
  • Key-Value Stores vs. Document Stores
  • Querying in NoSQL Databases

• Week 15: Cloud Computing for Data Management (BD-104C)

• • Cloud Platforms (AWS, Azure, Google Cloud)
  • Storage Solutions (S3, Google BigQuery, Azure Data Lake)
  • Cloud-Based Data Warehousing

• Week 16: Data Security & Governance (BD-104D)

• • Data Privacy Laws (GDPR, HIPAA)
  • Role-Based Access Control
  • Encryption & Data Protection

Module 5: Advanced Analytics & AI (AA-105)

(Weeks 17-20)
Students will explore deep learning, NLP, and AI applications.

• Week 17: Time Series & Forecasting (AA-105A)

• • Time Series Analysis Basics
  • ARIMA, Exponential Smoothing
  • Forecasting Models

• Week 18: Introduction to Deep Learning (AA-105B)

• • Neural Networks Basics
  • TensorFlow & Keras
  • Image & Text Analytics

• Week 19: Natural Language Processing (NLP) (AA-105C)

• • Text Preprocessing & Sentiment Analysis
  • Named Entity Recognition
  • Topic Modeling

• Week 20: AI in Business & Ethical AI (AA-105D)

• • AI Use Cases in Industry
  • Ethical Concerns in AI
  • AI Bias & Fairness

Module 6: Capstone Project & Career Preparation (CP-106)

(Weeks 21-24)
Students will apply their learning to real-world data projects and prepare for careers.

• Week 21: Capstone Project Planning (CP-106A)

• • Project Topic Selection
  • Defining Objectives & Scope
  • Data Collection & Preparation

• Week 22: Project Development & Implementation (CP-106B)

• • Applying Data Management & Analytics Techniques
  • Using Visualization & BI Tools
  • Model Building & Evaluation

• Week 23: Report Writing & Presentation (CP-106C)

• • Structuring Data Reports
  • Creating Engaging Data Presentations
  • Insights & Business Recommendations

• Week 24: Career Guidance & Certification (CP-106D)

• • Resume Building & LinkedIn Optimization
  • Mock Interviews & Job Market Trends
  • Certification Exam & Course Completion

Assessment & Grading:

• Assignments & Quizzes – 30%
• Mid-Term Exam – 20%
• Capstone Project – 30%
• Final Exam – 20%

Certification & Career Prospects

Upon completion, students will earn a Diploma in Data Management & Analytics (DDMA).

Graduates can pursue careers as:
✅ Data Analysts
✅ Business Intelligence Analysts
✅ Data Engineers
✅ Database Administrators
✅ AI/ML Engineers