Data & Database Management System
About Me
The Data & Database Management Track equips learners with the skills to design, build, manage, and analyze data-driven systems that support intelligent decision-making.
This track blends foundational database concepts with applied data analytics, artificial intelligence, and automation, ensuring that students develop both technical mastery and analytical insight.
The curriculum covers everything from database design and SQL programming to data visualization, machine learning, and AI integration. Graduates will be proficient in managing data across traditional and cloud-based platforms using modern tools such as MySQL, PostgreSQL, MS SQL Server, Power BI, Python, and TensorFlow.
Fees & Program Breakdown
Investing in your tech career is one of the smartest decisions you can make, your skills will open doors to global opportunities, high-income jobs, and long-term career growth. Our pricing is structured to remain affordable while still providing world-class training, mentorship, and internship placement.
Register In Your Preferred Currency
Registration Fee: $20
A mandatory one-time payment required before filling out the enrollment form.
Deposit
we require a deposit 50% of your entire tuition fee to secure your spot in the program. This deposit is part of your tuition not an extra fee and will be deducted from the total tuition balance.
Tuition Fee $300
Our tuition fees is $300. Students, you can choose to pay: Full payment upfront or Installment plan which spread your balance into monthly payments.
Program Details
This Program is span for an entire 3-months, it's including:
2 months Intensive Coursework
1 Month Final Project
Course 01.
Database Design & Management Systems Syllabus
This syllabus introduces the theory and practice of designing, implementing, and maintaining structured and non-structured database systems.
Learners will understand data models, normalization, and storage strategies while mastering the administration and security of databases across both on-premise and cloud infrastructures.
Detailed Modules & Topics
Module 1: Database Fundamentals and Architectur
Introduces data concepts, database architectures, and types such as RDBMS, NoSQL, and NewSQL. Objective: Understand how databases store, organize, and retrieve data efficiently.
Module 2: Data Modeling and Normalization
Covers Entity-Relationship (ER) modeling, primary/foreign keys, and normalization forms for optimal schema design. Objective: Design well-structured databases that reduce redundancy and enhance performance.
Module 3: Database Administration and Security
Focuses on user management, roles and privileges, encryption, backup, and recovery. Objective: Secure and maintain databases to ensure data integrity and availability.
Module 4: Cloud Database Deployment
Teaches database hosting and scaling using AWS RDS, Google Cloud SQL, and Azure Database Services. Objective: Deploy and manage scalable, cloud-based database environments.
Module 5: Performance Optimization and Automation
Explores indexing, query tuning, and automation tools for database monitoring and performance improvement. Objective: Optimize databases for high availability and automated operations.
Capstone Project
Course 02.
SQL Programming & Database Development Syllabus
This syllabus focuses on Structured Query Language (SQL) as the foundation of database interaction and data manipulation.
Students will master SQL using MySQL, PostgreSQL, and Microsoft SQL Server, gaining practical skills in query writing, database scripting, and performance optimization for both local and cloud databases.
Detailed Modules & Topics
Module 1: Introduction to SQL and Database Setup
Covers SQL fundamentals, environment setup, and the structure of relational databases. Objective: Establish a strong understanding of SQL syntax and database relationships.
Module 2: Data Definition and Manipulation
Teaches DDL and DML operations—creating, altering, inserting, updating, and deleting data. Objective: Build and modify database structures using SQL commands.
Module 3: Query Writing and Optimization
Covers SELECT, JOIN, GROUP BY, subqueries, and indexing for performance optimization. Objective: Write efficient queries to retrieve and analyze data accurately.
Module 4: Advanced SQL Concepts
Explores stored procedures, functions, views, triggers, and transactions for automation and data consistency. Objective: Implement reusable SQL components and maintain transactional integrity.
Module 5: Database Connectivity and Integration
Teaches connecting SQL databases to programming environments like Python, PHP, and Power BI. Objective: Integrate SQL databases with applications and analytics tools.
Capstone Project
Course 03.
Data Analysis & Visualization Syllabus
This syllabus builds learners’ analytical and visualization capabilities for data-driven decision-making.
Students learn to clean, transform, and interpret datasets while creating interactive dashboards and visual reports using tools such as Python, Excel, Power BI, and Tableau.
Detailed Modules & Topics
Module 1: Introduction to Data Analytics
Covers the data analytics process, data sources, and business applications. Objective: Understand how to use analytics to support data-driven decision-making.
Module 2: Data Cleaning and Transformation
Teaches data wrangling, missing value handling, and data formatting using Pandas, NumPy, and Excel. Objective: Prepare clean, structured data for analysis.
Module 3: Exploratory Data Analysis (EDA)
Explores statistical summaries, data distribution, and correlation analysis. Objective: Identify trends and patterns through exploratory analysis.
Module 4: Data Visualization and Reporting
Covers Power BI, Tableau, and Matplotlib for creating dynamic dashboards. Objective: Present analytical insights through effective visual storytelling.
Module 5: Applied Analytics Projects
Students execute a capstone project analyzing a dataset and presenting findings. Objective: Apply analytics skills in a real-world, results-oriented project.
Capstone Project
Course 04.
Artificial Intelligence & Machine Learning Syllabus
This syllabus introduces learners to the principles and applications of artificial intelligence (AI) and machine learning (ML).
Students will understand how data fuels intelligent systems, and they will develop, train, and deploy models using tools like TensorFlow, Keras, and Scikit-learn.
Detailed Modules & Topics
Module 1: AI & ML Fundamentals
Covers AI evolution, ML types, and the relationship between data, algorithms, and intelligence. Objective: Understand key AI/ML concepts and their practical applications.
Module 2: Data Preparation and Feature Engineering
Explores data preprocessing, feature selection, and scaling techniques. Objective: Prepare datasets for accurate model training.
Module 3: Model Building and Evaluation
Teaches supervised and unsupervised models including Regression, Decision Trees, and Clustering. Objective: Train and evaluate ML models using real-world data.
Module 4: Deep Learning and Neural Networks
Introduces ANN, CNN, and RNN for complex learning tasks. Objective: Develop deep learning solutions using TensorFlow and Keras.
Module 5: Model Deployment and MLOps
Covers model deployment using Flask APIs, Streamlit, and cloud ML services. Objective: Deploy, monitor, and maintain AI models in production.
Capstone Project
Course 05.
Intelligent Data Systems Integration Syllabus
This syllabus focuses on combining database systems, analytics, and AI models into fully integrated, intelligent enterprise platforms.
Students learn how to build end-to-end systems capable of automating data workflows, generating insights, and driving real-time decisions.
Detailed Modules & Topics
Module 1: Data Integration and APIs
Teaches RESTful APIs, data pipelines, and system interoperability between databases and AI applications. Objective: Integrate data systems for seamless data exchange and automation.
Module 2: Automation and Workflow Orchestration
Explores Robotic Process Automation (RPA), ETL orchestration, and intelligent agents. Objective: Automate data operations for scalability and consistency.
Module 3: Data Governance and Compliance
Covers data privacy, security, and ethical AI implementation. Objective: Apply governance frameworks to responsible data system management.
Module 4: Cloud Integration for Intelligent Systems
Teaches deployment of AI-integrated data systems on AWS, Azure, and Google Cloud. Objective: Deploy and scale AI data systems within cloud environments.
Module 5: Capstone Project – Intelligent Data Solution
Students design and build a complete intelligent platform integrating databases, analytics, and AI automation. Objective: Demonstrate comprehensive skill integration in a practical project.
