Courses

12-Week Python Full Stack Development Curriculum

Goal: To equip students with the skills to build fully functional, data-driven web applications using Python and modern web technologies.

Weekly Curriculum:

✅ Week 1: Introduction to Programming with Python

  • Computational Thinking
  • Python syntax and variables
  • Data types, operators, conditionals
  • Loops (for, while)
  • Math Functions
  • Practice Tasks:

✅ Week 2: Python Data Structures and Exception Handling

  • Strings, Lists, Tuples, Sets, Dictionaries
  • Exception handling
  • Practice Tasks:

✅ Week 3: Functions, Files and Regular Expressions

  • Functions and modules
  • Working with files
  • Regular Expression
  • Data and Time
  • Mini Project:

✅ Week 4: Object Oriented Programming

  • Object-Oriented Programming
  • Classes
  • objects
  • inheritance
  • polymorphism
  • Abstraction
  • Data Encapsulation
  • Practice Tasks:

✅ Week 5: Web Basics

  • How the web works (HTTP, DNS, client-server model)
  • Basic HTML
  • Structure, tags, forms
  • Basic CSS
  • Selectors, colors, layout, responsive design
  • Mini Project: Host a basic static HTML resume page on GitHub Pages

✅ Week 6: Front-End Development – HTML, CSS, JavaScript

  • Intermediate HTML & CSS (flexbox, grids)
  • JavaScript Basics
  • Variables and Data Types (var, let, const, strings, numbers, booleans, null, undefined)
  • Operators (arithmetic, comparison, logical, assignment)
  • Control Flow (if, else, switch, ternary operator)
  • Loops (for, while, do…while, for…of, for…in)
  • Functions
  • Function declarations & expressions
  • Arrow functions
  • Parameters & return values
  • Scope & Hoisting
  • Arrays & Objects
  • CRUD operations
  • Array methods: map, filter, reduce, forEach, etc.
  • Object manipulation
  • Mini Project: Responsive web form with validation

✅ Week 7: JavaScript Intermediate

  • DOM Manipulation
  • document.querySelector, getElementById
  • Event listeners
  • Creating, updating, deleting DOM elements
  • Events & Event Handling
  • JavaScript in the Browser
  • window, document, navigator, location
  • Asynchronous JavaScript
  • JSON & Local Storage
  • JSON.parse / JSON.stringify
  • localStorage, sessionStorage
  • Fetch API
  • Making HTTP requests
  • Handling responses and errors
  • Project:

✅ Week 8: Relational Databases with Oracle SQL

  • Introduction to SQL and SQLite
  • CRUD operations (Create, Read, Update, Delete)
  • Database models and migrations
  • Project:

✅ Week 10: Advanced Django Features & APIs

  • Core Framework
  • Django Architecture
  • Routing
  • Middleware
  • ORM (Django ORM)
  • Template engines
  • REST API development (Django REST Framework)
  • Authentication & authorization
  • Authentication & User Management
  • Project:

✅ Week 11: SDLC and STLC

  • Testing
  • SDLC
  • STLC
  • Bug life cycle
  • Introduction to pytest
  • Unix Basics

✅ Week 12: Capstone Project + Interview Prep

  • Capstone Projects: Final presentation & code review
  • Resume building (tech-focused)
  • Mock technical interviews
  • GitHub profile and LinkedIn optimization

🎯 Learning Outcomes

By the end of this course, students will:
• Build responsive web apps using Python, Django, HTML, CSS, JavaScript
• Work with databases using Oracle SQL
• Create and consume REST APIs
• Deploy real-world projects to the cloud
• Be job-ready with project portfolio and interview practice

12-Week Data Analyst Curriculum

Goal: Each week includes theory, hands-on practice, and assignments.

Weekly Curriculum:

✅ Week 1: Introduction to Data Analytics & Excel Basics

  • Role of a data analyst
  • Types of analytics (descriptive, diagnostic, etc.)
  • Business context of data analysis
  • Excel Fundamentals
  • Functions (IF, VLOOKUP, COUNTIF, etc.)
  • Pivot tables & charts
  • Data cleaning in Excel
  • Assignment: Analyze a sales dataset in Excel and present findings.

✅ Week 2: SQL for Data Analysis – Basics

  • Introduction to relational databases
  • SELECT, WHERE, ORDER BY
  • Aggregate functions (SUM, AVG, COUNT)
  • GROUP BY, HAVING
  • Practice: Query a mock employee or sales database.

✅ Week 3: SQL for Data Analysis – Intermediate to Advanced

  • Joins (INNER, LEFT, RIGHT, FULL)
  • Subqueries
  • Window functions (ROW_NUMBER, RANK, etc.)
  • CTEs and temporary tables
  • Assignment: Complex multi-table queries from a business dataset.

✅ Week 4: Data Manipulation with pandas & numpy

  • Jupyter Notebook / Google Colab setup
  • Working with files (CSV, Excel)
  • DataFrames, Series
  • Data cleaning (missing values, duplicates, formatting)
  • Grouping, filtering, merging datasets
  • Mini Project: Analyze a dataset (e.g., customer churn or e-commerce).

✅ Week 5: Data Visualization with Python

  • matplotlib and seaborn for plotting
  • Histograms, bar charts, line graphs, heatmaps
  • Visualizing trends and distributions
  • Assignment: Visual storytelling using charts.

✅ Week 6: Statistics & Data Interpretation

  • Basic Statistics
  • Descriptive statistics
  • Probability basics
  • Hypothesis testing (t-test, chi-square)
  • Correlation and linear regression
  • Assignment:

✅ Week 7: Power BI / Tableau – Building Dashboards

  • Data loading and transformation
  • Interactive visuals and charts
  • Slicers, filters, custom measures (DAX)
  • Publishing and sharing dashboards
  • Project: Create a sales dashboard for a fictional company.

✅ Week 8 and Week 9: Data Analysis Project 1 – Exploratory Data Analysis (EDA)

  • End-to-end EDA using Python
  • Feature selection and pattern discovery
  • Presenting findings with charts
  • Live Project:

✅ Week 10: Introduction to Machine Learning for Analysts

  • Overview of ML in analytics
  • Supervised vs unsupervised learning

✅ Week 11: Resume Building, Communication & Soft Skills

  • Building an analytics-specific resume
  • Writing project descriptions
  • Communication & presentation skills
  • Presenting findings to non-technical stakeholders
  • Activity: Students present one project to the class.

✅ Week 12: Final Live Project + Interview Preparation

  • Capstone project (data cleaning + analysis + visualization)
  • Mock interviews
  • Problem-solving exercises and case studies
  • Q&A on industry tools, roles, and career guidance
  • Capstone Project: Full pipeline project (EDA + dashboard + business insight)

🎯 Outcomes by End of Course:

• Strong grasp of SQL, Excel, Python, and BI tools • Portfolio with 2 real-world projects • Interview-ready resume • Confidence in technical and soft skills

4-Week Machine Learning Foundations Curriculum

Goal: Each week includes theory, hands-on practice, and assignments.

Weekly Curriculum:

✅ Week 1: ML Foundations & Supervised Learning Basics

  • Focus: Concepts + Regression Models
  • Topics:
    • What is Machine Learning?
    • Types of ML: Supervised vs Unsupervised
    • Key concepts: Overfitting, Underfitting, Bias-Variance, Train/Test Split
    • Linear Regression (Simple & Multiple)
    • Model evaluation: MSE, RMSE, R²
    • Introduction to scikit-learn
  • Projects/Tasks:
    • Linear Regression on housing dataset
    • Visualize predictions vs actuals
  • Resources:
    • Hands-On ML with Scikit-Learn
    • scikit-learn documentation for Linear Models

✅ Week 2: Classification Models

  • Focus: Core Classification Algorithms
  • Topics:
    • Logistic Regression
    • K-Nearest Neighbors
    • Decision Trees
    • Confusion Matrix, Accuracy, Precision, Recall, F1
    • ROC Curve and AUC
    • Cross-validation
  • Projects/Tasks:
    • Titanic survival prediction (Kaggle dataset)
    • Visualize decision boundaries
  • Resources:
    • Hands-On ML (Ch. 3–4)
    • sklearn model evaluation guides

✅ Week 3: Model Tuning & Advanced Models

  • Focus: Improving performance + Ensemble Methods
  • Topics:
    • Hyperparameter tuning (GridSearchCV, RandomizedSearchCV)
    • Feature scaling & engineering
    • Random Forests
    • Gradient Boosting (Intro to XGBoost or LightGBM)
    • Model pipelines
  • Projects/Tasks:
    • Use GridSearchCV on a classification problem
    • Train a Random Forest on a real dataset (e.g., diabetes or heart disease)
  • Resources:
    • Hands-On ML (Ch. 5–6)
    • XGBoost or LightGBM docs (optional, for stretch learning)

✅ Week 4: Unsupervised Learning & Final Project

  • Focus: Clustering & Dimensionality Reduction
  • Topics:
    • K-Means Clustering
    • Hierarchical Clustering
    • PCA (Principal Component Analysis)
    • t-SNE (for visualization)
  • Final Project:
    • Choose a dataset (from Kaggle, UCI, etc.)
    • Complete EDA, preprocessing, model training, and evaluation
    • Write a short report or blog-style summary
  • Resources:
    • Hands-On ML (Ch. 8–9)
    • UCI ML Repository or Kaggle Datasets

🛠 Tools to Use Throughout:

  • Jupyter Notebooks / Google Colab
  • Matplotlib / Seaborn for visualizations
  • scikit-learn for modeling
  • Kaggle for practice datasets & notebooks