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Intermediate
Advanced
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Certification Training Certificate
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How It Works

1. Learn at Your Pace

Access interactive content and study on your own schedule with structured course materials.

2. Complete Assignments

Reinforce your knowledge by working on practical tasks and skill-based exercises.

3. Live Lessons

Join live sessions with expert instructors to clarify doubts and receive personal guidance.

4. Custom Project

Work on a real-world project tailored to your learning path and career goals.

5. Certification & Beyond

Receive your certificate and start applying your skills to job opportunities or personal ventures.

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What Will You Learn?

Explore Our Comprehensive Course Catalogue

Web Development

  • Introduction to UX Design
  • Building Mockups for Web Design

  • Getting Started with HTML: Introduction and Basics
  • HTML Tags, Metadata, and Forms: Essential Elements for Web Development
  • HTML vs. HTML5: Understanding the Differences
  • Developing with HTML: Tips and Tools for Success

  • Getting Started with CSS3
  • CSS3 Syntax and Usage
  • Using CSS3 for Responsive Web Design
  • Exploring CSS3 Frameworks: Bootstrap and Tailwind
  • Creating Stunning Visuals with CSS3 Animations

  • Data types and flow control
  • Functional programming
  • Objects and prototypes
  • Error Handling
  • Refactoring and Debugging

  • Inheritance, Constructors and Destructor
  • Encapsulation
  • Polymorphism

  • SQL and NoSQL databases
  • SQLite3
  • Data Modeling
  • Django ORM

    • Introduction to Server-Side JavaScript with TypeScript
    • Working with NPM (Node Package Manager)
    • JavaScript Build Processes for Node.js
    • Understanding GraphQL in Node.js
    • Building APIs with Node.js

    • Data Management
    • Communication Implementation
    • Aggregation Framework
    • CRUD Operations
    • Communication Implementation
    • Database Configuration

    • Beginner
    • Intermediate
    • Advanced

    Data Science

    • Overview of Data Science
    • Importance of Data Science
    • Environment Setup for Data Science
    • Introduction to Python for Data Science
    • Python Basics for Data Science
    • Fundamentals of Python for Data Science
    • Data Types in Python
    • Loops and Functions in Python
    • Numerical Computing with NumPy
    • Data Manipulation using Pandas

    • Understanding Data: Types, Classification, and Properties
    • Mathematical Foundations for Data Science
    • Probability and Statistics for Data Analysis
    • Linear Algebra for Machine Learning
    • Gradient Descent and Optimization Algorithms
    • Calculus for Data Science

    • K-NN classification
    • Forecasting and prediction using regression
    • Linear Regression
    • Logistic Regression

    • Introduction: Importing and Cleaning Data
    • Data Cleaning Techniques: Cleaning and Preparing Datasets
    • Data Manipulation: Merging, Splitting, and Reshaping Datasets
    • Data Aggregation and Summarization: Descriptive Statistics and Data Visualization

    • Machine Learning Algorithm Implementation
    • Algorithmic Analysis and Evaluation
    • Deploying and Serving Machine Learning Models

    • Jupyter Notebook & IBM Watson Studio
    • RStudio & Tableau
    • PyCharm & Power BI
    • Spyder & Apache Spark
    • Hadoop & Git/Github

    • Beginner
    • Intermediate
    • Advanced

    Data Science

    • Overview of Data Science
    • Importance of Data Science
    • Environment Setup for Data Science
    • Introduction to Python for Data Science
    • Python Basics for Data Science
    • Fundamentals of Python for Data Science
    • Data Types in Python
    • Loops and Functions in Python
    • Numerical Computing with NumPy
    • Data Manipulation using Pandas

    • Understanding Data: Types, Classification, and Properties
    • Mathematical Foundations for Data Science
    • Probability and Statistics for Data Analysis
    • Linear Algebra for Machine Learning
    • Gradient Descent and Optimization Algorithms
    • Calculus for Data Science

    • K-NN classification
    • Forecasting and prediction using regression
    • Linear Regression
    • Logistic Regression

    • Introduction: Importing and Cleaning Data
    • Data Cleaning Techniques: Cleaning and Preparing Datasets
    • Data Manipulation: Merging, Splitting, and Reshaping Datasets
    • Data Aggregation and Summarization: Descriptive Statistics and Data Visualization

    • Machine Learning Algorithm Implementation
    • Algorithmic Analysis and Evaluation
    • Deploying and Serving Machine Learning Models

    • Jupyter Notebook & IBM Watson Studio
    • RStudio & Tableau
    • PyCharm & Power BI
    • Spyder & Apache Spark
    • Hadoop & Git/Github

    • Beginner
    • Intermediate
    • Advanced

    Android Development

    • Android Introduction
    • History of Android
    • Prerequisites for Learning Android
    • Introduction to Java Programming in Android

    • High-Level Overview of Android Architecture
    • Understanding the Application Framework in Android
    • Introduction to Libraries in Android

    • System Requirements
    • Installing and Setting up Android Studio
    • Overview of Android Studio and its Tools
    • Understanding the Android Build System

    • Getting started with Android app development
    • Building your first Android app
    • Creating a simple Android application
    • Introduction to Android app development
    • Developing your first Android project
    • Writing your first Android application

    • Understanding Android Application Components
    • Intents and Intent Filters
    • Activities and their Lifecycle
    • Services in Android
    • Broadcast Receivers in Android
    • Content Providers in Android
    • Creating App Widgets
    • Working with Processes and Threads in Android

    • Introduction to User Interface Components
    • Views and Layouts in Android
    • Understanding Input Controls
    • Handling Input Events in Android

    • Advanced User Interaction: Gestures, Drag and Drop
    • Fragments and Custom Views: Fragments, Creating Custom Components
    • Graphics and Accessibility: Canvas, Web View, Accessibility
    • UI Styling and Theming: Styles and Themes

    • Beginner
    • Intermediate
    • Advanced

    Machine Learning

    • Introduction to Machine Learning
    • Types of Machine Learning Algorithms
    • Supervised Learning
    • Unsupervised Learning
    • Classification
    • Regression

    • Numbers: Integer, Float, Complex Numbers
    • Boolean
    • Strings: operations and methods
    • Lists: methods
    • Tuples
    • Sets
    • Dictionaries

    • Introduction to Data Preprocessing
    • Importing a Dataset in Python
    • Handling Missing Values
    • Handling Categorical Variables
    • Splitting Data into Training and Testing Sets
    • Feature Scaling and Normalization
    • Normalizing Variables using Python Code
    • Summary

    • Simple Linear Regression
    • Multiple Linear Regression
    • Decision Trees
    • Random Forest
    • Decision Trees and Random Forest – Python Implementation

    • Introduction to Classification
    • kNN Algorithm - Working and Theory
    • Implementing kNN in Python
    • Decision Tree Classifier - Working and Theory
    • Random Forest Classifier - Working and Theory in Python
    • Support Vector Machines (SVM) - Working and Theory
    • Implementing SVM in Python

    • Training the model on the training set
    • Evaluating the performance of the model on the testing set
    • Tuning the hyperparameters of the model to improve its performance
    • Using cross-validation to validate the model
    • Dealing with overfitting and underfitting
    • Deploying the model in a real-world setting.

    • Beginner
    • Intermediate
    • Advanced

    Cyber Security

    • Introductory Classes
    • Importance of Cyber Security
    • Applications of Cyber Security
    • Malicious Codes and Terminologies
    • Cyber Security Breaches

    • Information Security (IS) Services: Overview and Categories
    • Types of Cyber Attacks and Their Impact
    • E-commerce Security: Risks and Measures
    • Introduction to Cyber Forensics: Role and Significance

    • Introduction to Linux: Basic Commands and Concepts
    • Local Area Networks (LAN): Definition and Components
    • Wide Area Networks (WAN): Overview and Types
    • Working of the World Wide Web (WWW): Protocols and Architecture
    • Introduction to the Internet: History and Infrastructure

    • What is Ethical Hacking? & Types of Hackers
    • Phases of Ethical Hacking & Footprinting and Reconnaissance
    • Scanning Networks & Enumeration
    • Vulnerability Analysis & System Hacking
    • Malware Threats & Sniffing
    • Social Engineering & Denial of Service (DoS) Attacks
    • Evading IDS, Firewalls, and Honeypots
    • Session Hijacking & Hacking Web Servers
    • Hacking Web Applications & SQL Injection
    • Hacking Wireless Networks & Hacking Mobile Platforms
    • IoT Hacking

    • Definition of security threats and vulnerabilities
    • Types of security threats (e.g. malware, phishing, ransomware, DDoS attacks)
    • Common vulnerabilities (e.g. weak passwords, unpatched software, social engineering)
    • Risks associated with security threats and vulnerabilities
    • Importance of identifying and mitigating security threats and vulnerabilities.

    • Cryptography and its importance
    • Symmetric key cryptography
    • Asymmetric key cryptography
    • Concurrency in cryptography
    • Protocol security in cryptography

    • Beginner
    • Intermediate
    • Advanced

    A digital studio crafting learning experiences that inspire

    Get hands-on with real-world data and understand the core concepts of Data Analysis.

    Master HTML tables and layout structures to create clean, organized web pages.

    Learn the building blocks of HTML to structure content for any modern website.

    Set up your local development environment and start coding with confidence.

    Advance your CSS skills to design responsive, professional-looking layouts.

    Learn how to transform static designs into interactive websites with real functionality.

    Course Pricing

    Affordable Plans for Every Learner

    Choose a course that fits your goals. Learn from industry experts at competitive prices tailored for Indian learners.

    Beginner Bundle

    AED 999

    ~ $270 USD / €250 EUR

    Covers HTML, CSS, and basic Web Development. Ideal for absolute beginners.

    Enroll Now
    Full Stack Mastery

    AED 2,499

    ~ $680 USD / €630 EUR

    Frontend + Backend with hands-on projects. Best for intermediate learners.

    Enroll Now
    Career Pro Package

    AED 4,999

    ~ $1,360 USD / €1,270 EUR

    Includes DSA, System Design, Resume Review & Mock Interviews. Ideal for job-ready candidates.

    Enroll Now

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    Our courses are ideal for students, freshers, working professionals, or anyone interested in gaining practical software development skills. No prior coding experience is required for beginner-level tracks.

    Courses are delivered via live online sessions, hybrid formats, and fully remote modes — designed for flexible learning across all locations.

    Yes. Upon successful completion, students receive a Training Certificate. Based on performance, you may also qualify for an Internship Certificate or Certificate of Excellence.

    Absolutely. Every course includes capstone projects, real-world assignments, and live coding challenges to ensure practical understanding.

    Yes, we offer placement assistance, mock interviews, resume reviews, and referrals through our partner network for top-performing students.

    Course duration ranges from 6 to 12 weeks, depending on the track you choose. Weekend and weekday batches are available.

    Just head over to our contact page or email us at support@relokisoft.com. We’ll guide you through the next steps.
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