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Data Analysis and Visualization Class

Data Analysis Cohort - 2025

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Begin your journey with foundational knowledge crucial for analyzing and interpreting data effectively.

Our goal is to gain a strong foundation in the same tools used by professional data analysts across various industries. By the end of this course, you'll have the understanding you need to work comfortably with data, extract insights, and create impactful visualizations.

- Introduction to Excel for Data Analysis
- Data Cleaning and Formatting
- Pivot Tables and Charts
- Basic Statistical Analysis in Excel

Excel for Data Analysis

- Introduction to Databases and SQL
- Writing Basic to Advanced SQL Queries
- Data Aggregation and Joins
- Working with Large Datasets

SQL for Data Analysis

- Introduction to Python and Pandas
- Data Manipulation and Cleaning
- Exploratory Data Analysis (EDA)
- Data Visualization with Matplotlib and Seaborn

Python for Data Analysis

- Introduction to Power BI Interface
- Creating Interactive Dashboards
- Data Modeling and DAX Functions
- Connecting PowerBI to MSSQL
- Storytelling with Data

Power BI for Data Visualization

Data Analysis Cohort

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Software Development Class

Software Development Class - 2025

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### **Software Engineering Beginner's Course with Darey.IO**

**Master Fullstack Web Development from Scratch!**

**This course is designed for absolute beginners who want to break into software engineering by learning how to build and deploy full-stack web applications. We focus on hands-on projects and use the same technologies that power modern applications.**

**By the end of this course, you'll have the confidence and skills to develop real-world applications using HTML, CSS, JavaScript, React, Git, and deployment strategies.**

📌 Objective: Build a strong foundation in web development with core frontend technologies.

🔹 HTML Basics

Understanding HTML structure and elements

Creating headings, paragraphs, links, images, and forms

Best practices for semantic HTML

🔹 CSS Fundamentals

Introduction to styling and layout techniques

CSS selectors, properties, and specificity

Responsive design with media queries

Flexbox and Grid for modern layouts

🔹 JavaScript Essentials

Variables, data types, and operators

Functions and event handling

Working with arrays and objects

Understanding the Document Object Model (DOM)

JavaScript ES6+ features (arrow functions, template literals, destructuring)

Module 1: Frontend Fundamentals

📌 Objective: Learn how to build dynamic, interactive applications using React.

🔹 Introduction to React

What is React and why use it?

Understanding JSX and React components

Functional vs. Class components

🔹 React State

Managing component state with useState

Handling user inputs and UI updates

Lifting state up and passing props

🔹 Fetching Data with useEffect

Understanding React lifecycle

Making API requests with fetch orCreate a "Hello World" React app. Axios

Handling loading and error states

 Module 2: Introduction to React

📌 Objective: Improve code quality, enhance styling, and master version control.

🔹 Explore Different Ways to Style React Applications

Inline styles vs. CSS files

CSS Modules vs. Tailwind CSS

Styled Components in React

🔹 Testing in React

Why testing is important

Writing unit tests with Jest and React Testing Library

Testing components and API calls

🔹 Git and GitHub Mastery

Understanding Git and version control

Cloning, committing, and pushing changes

Creating and merging pull requests

🔹 Installing and Configuring Git

Setting up Git on Windows/Mac/Linux

Connecting GitHub with SSH authentication

Module 3: Advanced React & Software Development Practices

📌 Objective: Apply all skills learned to build and deploy a full application.

🔹 Building and Deploying React Apps

Best practices for structuring React projects

Using React Router for navigation

State management with Context API

Module 4: Fullstack Development & Capstone Project

Software Development 

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The Data Science Track is a comprehensive 4-month cohort programme designed to provide a hands-on, practical foundation in data science. The course covers essential skills such as Python programming, data analysis, statistics, exploratory data analysis (EDA), data visualisation, and an introduction to machine learning. As a student, you will progress from basic programming concepts to implementing machine learning models and deploying these models using tools like Streamlit. The course also includes several mini projects and capstone projects that allow you to apply your skills to real-world problems.

Python Programming Basics 

Introduction to Object Oriented Programming 

Numpy and Pandas for data manipulation 

Basics of visualization in python 

Matplotlib and Seaborn for data visualization 

MODULE 1 & 2 

Descriptive Statistics 

Probability fundamentals 

Handling missing values and outliers 

Exploratory Data Analysis 

MODULE 3 & 4 

 Data distributions 

Sampling, estimations and Cental Limit Theorem 

Hypothesis testing and Statistical tests 

Data Preparation and feature engineering 

Machine learning algorithms 

Deployment on streamlit 

MODULE 5 & 6 

Data Science

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AI/Machine Learning Class - 2025

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Embark on a transformative journey into the world of Artificial Intelligence and Machine Learning with our comprehensive 5-month Cohort program. This course is meticulously designed to provide you with the foundational knowledge and advanced skills required to excel in the rapidly evolving field of AI and ML. From understanding the basics of machine learning to mastering advanced techniques and deploying real-world AI solutions, this program covers it all.

You'll gain hands-on experience with data preprocessing, model evaluation, and advanced ML techniques such as reinforcement learning and natural language processing. By the end of the program, you will be equipped to tackle complex problems, build intelligent systems, and deploy scalable AI solutions, preparing you for roles such as Machine Learning Engineer, Data Scientist, or AI Specialist.

AI and Machine Learning are at the forefront of technological innovation, with increasing demand for skilled professionals, competitive salaries, and vast opportunities for career growth.



This module introduces the fundamental concepts of machine learning, including supervised and unsupervised learning, regression, and classification. You'll learn essential skills such as data preprocessing and regression techniques, and get hands-on experience by working on a project to predict sales success using marketing data.

 Introduction to Machine Learning

Dive deeper into machine learning with advanced concepts like cross-validation, dimensionality reduction, and ensemble methods. This module also covers reinforcement learning, recommendation systems, and anomaly detection. You'll apply these techniques to solve complex classification or regression problems and work on projects such as stock price forecasting and personalized movie recommendation systems.

 Machine Learning Techniques & Advanced Machine Learning Techniques

Explore the fascinating fields of Natural Language Processing (NLP) and Computer Vision. Learn about language modeling, text classification, and chatbots in NLP, and image classification, object detection, and generative models in Computer Vision. You'll work on projects like building a text classifier for spam detection and developing an intelligent traffic monitoring and analysis system.

NLP and Computer Vision

This module focuses on handling large datasets and optimizing machine learning models. You'll learn introduction to distributed computing, data pipelines, and cloud-based AI solutions using tools like PySpark on AWS or Google Cloud. Additionally, you'll gain skills in model deployment, real-time data streaming, and AI ethics. The module culminates in a project where you deploy a real-time prediction model using Docker and Kafka.

School of Artificial Intelligence and Data Science

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