Lujin Daniel

Class Location

Online class via Zoom

Student's Home

Tutor's Home

Years of Experience in Microsoft Power BI classes

6

Class Location

Online class via Zoom

Student's Home

Tutor's Home

Years of Experience in Web Designing Classes

6

Teaching Experience in detail in Web Designing Classes

1. Curriculum Development Designed and structured comprehensive modules covering: HTML5 & CSS3 fundamentals for building responsive layouts. JavaScript basics for interactivity and DOM manipulation. Responsive Design Principles using media queries and frameworks like Bootstrap. UI/UX best practices for creating user-friendly interfaces. Introduction to Web Accessibility (WCAG standards) for inclusive design. 2. Practical, Hands-On Training Conducted live coding sessions to demonstrate real-world implementation. Guided students through projects, such as: Personal portfolio websites. E-commerce landing pages. Blog templates with responsive navigation. Emphasized debugging techniques and browser developer tools. 3. Tools & Technologies Taught Code Editors: VS Code, Sublime Text. Version Control: Git & GitHub for collaborative development. Frameworks: Bootstrap for responsive design. Design Tools: Figma for wireframing and prototyping. 4. Teaching Methodology Used interactive learning: quizzes, group discussions, and peer reviews. Incorporated real-world case studies to explain design principles. Provided step-by-step guidance for deploying websites using platforms like Netlify or GitHub Pages. 5. Assessment & Feedback Conducted practical assignments and capstone projects. Offered personalized feedback to improve coding style and design aesthetics. Organized mock client projects to simulate professional scenarios. 6. Outcomes Helped learners build fully functional websites from scratch. Improved students’ understanding of responsive and accessible design. Prepared learners for entry-level web development roles or freelance projects.

Class Location

Online class via Zoom

Student's Home

Tutor's Home

Years of Experience in MS SQL Development Training

6

Teaching Experience in detail in MS SQL Development Training

1. Curriculum Design Developed structured modules covering: Relational Database Concepts: Normalization, keys, constraints. MS SQL Server Fundamentals: Installation, configuration, and management. T-SQL Programming: SELECT, INSERT, UPDATE, DELETE statements. Advanced Queries: Joins, subqueries, Common Table Expressions (CTEs). Stored Procedures, Functions, and Triggers for business logic implementation. Performance Optimization: Indexing strategies, query tuning, execution plans. Data Security: Roles, permissions, encryption basics. Backup & Recovery: Disaster recovery planning and implementation. 2. Practical, Hands-On Training Conducted live demonstrations of database creation and management. Guided learners through real-world projects, such as: Designing normalized schemas for e-commerce or HR systems. Writing complex queries for reporting and analytics. Implementing stored procedures for transactional workflows. Emphasized best practices in query optimization and error handling. 3. Tools & Technologies Taught MS SQL Server Management Studio (SSMS) for database administration. SQL Profiler for performance monitoring. Azure SQL Database for cloud-based solutions. Integration with BI tools like Power BI for reporting. 4. Teaching Methodology Used interactive sessions with problem-solving exercises. Incorporated case studies to explain real-world database challenges. Provided step-by-step guidance for deploying and maintaining SQL databases. 5. Assessment & Feedback Conducted practical assignments on query writing and optimization. Organized capstone projects simulating enterprise-level database systems. Offered personalized feedback to improve coding efficiency and database design. 6. Outcomes Enabled learners to design, develop, and manage SQL databases confidently. Prepared students for roles like Database Developer, SQL Analyst, and DBA. Improved understanding of performance tuning and security best practices.

Class Location

Online class via Zoom

Student's home

Tutor's Home

Years of Experience in Microsoft Azure Training

6

Teaching Experience in detail in Microsoft Azure Training

1. Curriculum Design Developed structured modules covering: Azure Fundamentals: Core cloud concepts, regions, availability zones. Azure Services Overview: Compute (VMs), Storage, Networking, and Databases. Identity and Access Management: Azure Active Directory, RBAC. Azure Resource Management: Resource groups, ARM templates. Azure Networking: Virtual Networks, VPN Gateways, Load Balancers. Azure Security & Compliance: Policies, encryption, monitoring. Azure DevOps & CI/CD Pipelines: Integration with GitHub and automation. Cost Management & Billing: Pricing models and optimization strategies. 2. Practical, Hands-On Training Conducted live demonstrations on: Deploying Virtual Machines and Web Apps. Configuring Azure Storage (Blob, Table, Queue). Setting up Virtual Networks and Network Security Groups. Implementing Azure Monitor and Log Analytics for observability. Guided learners through real-world projects, such as: Hosting a scalable web application on Azure App Service. Building a secure multi-tier architecture using Azure services. Automating deployments with Azure DevOps pipelines. 3. Tools & Technologies Taught Azure Portal for resource management. Azure CLI & PowerShell for automation. Azure Resource Manager (ARM) Templates for infrastructure as code. Azure DevOps for CI/CD and project management. 4. Teaching Methodology Used interactive labs and sandbox environments for hands-on practice. Incorporated case studies to explain enterprise-level cloud solutions. Provided step-by-step guidance for migrating on-prem workloads to Azure. 5. Assessment & Feedback Conducted practical assignments on deploying and configuring services. Organized capstone projects simulating real-world cloud architectures. Offered personalized feedback on resource optimization and security compliance. 6. Outcomes Enabled learners to design, deploy, and manage Azure solutions confidently. Prepared students for Microsoft Certified: Azure Fundamentals and Azure Administrator Associate exams. Improved understanding of cloud governance, security, and cost optimization.

Class Location

Online class via Zoom

Student's Home

Tutor's Home

Years of Experience in Microsoft Excel Training classes

6

Teaching Experience in detail in Microsoft Excel Training classes

1. Curriculum Design Created structured modules covering: Excel Basics: Navigation, formatting, and data entry. Formulas & Functions: SUM, IF, VLOOKUP, INDEX-MATCH, TEXT functions. Data Analysis Tools: PivotTables, PivotCharts, and slicers. Conditional Formatting: Dynamic highlighting and data visualization. Data Validation & Protection: Ensuring data integrity and security. Advanced Excel Features: Named ranges, array formulas, and dynamic tables. Macros & VBA Basics: Automating repetitive tasks. Integration: Linking Excel with other tools like Power BI. 2. Practical, Hands-On Training Conducted live demonstrations for: Building dashboards using PivotTables and charts. Creating dynamic reports with slicers and filters. Automating workflows using simple VBA scripts. Guided learners through real-world projects, such as: Sales performance dashboards. Financial forecasting models. HR data analysis and reporting. 3. Tools & Techniques Taught Excel Desktop & Online versions. Power Query for data transformation. Power Pivot for advanced data modeling. Data Visualization Best Practices for clear reporting. 4. Teaching Methodology Used interactive exercises and case studies for practical learning. Provided step-by-step guidance for complex formulas and automation. Incorporated real business scenarios for relevance and application. 5. Assessment & Feedback Conducted assignments on data analysis and dashboard creation. Organized capstone projects simulating corporate reporting needs. Offered personalized feedback to improve formula efficiency and design. 6. Outcomes Enabled learners to analyze, visualize, and automate data workflows effectively. Prepared students for roles in data analysis, reporting, and business intelligence. Improved understanding of Excel’s advanced features for productivity and decision-making.

Class Location

Online class via Zoom

Student's Home

Tutor's Home

Years of Experience in React JS Training

6

Teaching Experience in detail in React JS Training

1. Curriculum Design Developed structured modules covering: React Fundamentals: Components, JSX, props, and state. Component Lifecycle: Mounting, updating, and unmounting phases. Hooks: useState, useEffect, useContext, and custom hooks. Routing: Implementing navigation using React Router. State Management: Context API and Redux for global state. Performance Optimization: Memoization, lazy loading, and code splitting. Integration: Connecting React with REST APIs and GraphQL. Testing: Unit testing with Jest and React Testing Library. 2. Practical, Hands-On Training Conducted live coding sessions for: Building reusable components and dynamic UIs. Creating single-page applications (SPAs) with routing. Fetching and displaying data from APIs. Guided learners through real-world projects, such as: E-commerce product listing and cart functionality. Interactive dashboards with charts and filters. Social media feed with likes and comments. 3. Tools & Technologies Taught React Developer Tools for debugging. Node.js & npm/yarn for package management. Webpack & Babel for bundling and transpiling. VS Code for development and GitHub for version control. 4. Teaching Methodology Used interactive exercises and pair programming. Incorporated real-world scenarios for practical application. Provided step-by-step guidance for deploying apps on platforms like Netlify or Vercel. 5. Assessment & Feedback Conducted assignments on component design and API integration. Organized capstone projects simulating production-level React apps. Offered personalized feedback on code quality and performance optimization. 6. Outcomes Enabled learners to build scalable, interactive React applications. Prepared students for roles like Front-End Developer and React.js Specialist. Improved understanding of modern JavaScript and component-based architecture.

Class Location

Online class via Zoom

Student's Home

Tutor's Home

Years of Experience in Data Science Classes

6

Teaching Experience in detail in Data Science Classes

1. Curriculum Design Developed comprehensive modules covering: Foundations of Data Science: Statistics, probability, and linear algebra essentials. Python for Data Science: NumPy, Pandas, Matplotlib, and Seaborn. Data Wrangling & Cleaning: Handling missing values, outliers, and data normalization. Exploratory Data Analysis (EDA): Visualization techniques and insights extraction. Machine Learning Basics: Supervised and unsupervised learning algorithms. Model Evaluation & Optimization: Cross-validation, hyperparameter tuning. Big Data & Cloud Integration: Basics of Spark and Azure ML. Data Visualization & Reporting: Using Power BI and Tableau. 2. Practical, Hands-On Training Conducted live coding sessions for: Building predictive models using scikit-learn. Performing EDA on real-world datasets (sales, healthcare, finance). Implementing clustering and classification algorithms. Guided learners through projects, such as: Customer segmentation using K-Means. Predicting sales trends with regression models. Sentiment analysis on social media data. 3. Tools & Technologies Taught Programming: Python (NumPy, Pandas, scikit-learn), Jupyter Notebooks. Visualization: Matplotlib, Seaborn, Plotly. Data Handling: SQL for data extraction and integration. Cloud & Deployment: Azure ML, Google Colab. Version Control: Git & GitHub for collaborative projects. 4. Teaching Methodology Used interactive exercises and real-world datasets for practical learning. Incorporated case studies from business, healthcare, and finance domains. Provided step-by-step guidance for building end-to-end ML pipelines. 5. Assessment & Feedback Conducted assignments on data cleaning, visualization, and model building. Organized capstone projects simulating real-world analytics challenges. Offered personalized feedback on coding practices and analytical approach. 6. Outcomes Enabled learners to analyze, model, and interpret data effectively. Prepared students for roles like Data Analyst, Data Scientist, and ML Engineer. Improved understanding of data-driven decision-making and predictive analytics.