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Hands-on, enterprise-grade training in Python, Data Science & Machine Learning, Oracle Database Administration and DevOps.

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Course Catalog
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Data Science Course
Data Science Course
Python Course
Python Course

Click any course to view the full outline.

Python Fundamental

18 Hours · 6 Days (3 Weeks, Weekend Batch) · 4 modules
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Module 1 — Python Programming Fundamentals

  • Introduction to Python and its Applications, Installing Python and Development Environments (VS Code)
  • Python Execution Modes: Interactive and Script Mode
  • Variables and Data Types, Input and Output Functions
  • Type Conversion and Type Casting
  • Python Operators: Arithmetic, Comparison, Logical, Assignment, Membership, Identity
  • Conditional Statements (if, elif, else), Nested Conditions
  • Loops (for, while), Loop Control Statements (break, continue, pass)
  • Range Function, Nested Loops

Module 2 — Python Data Structures and Applications

  • Introduction to Python Data Structures
  • Creating and Accessing Lists, List Operations and Methods
  • List Slicing and List Comprehensions, Nested Lists
  • Creating and Using Tuples, Tuple Packing and Unpacking
  • Applications of Lists and Tuples
  • Introduction to Sets, Set Operations: Union, Intersection, Difference, Symmetric Difference
  • Applications of Sets in Data Processing
  • Creating and Accessing Dictionaries, Dictionary Methods, Iterating Through Dictionaries

Module 3 — Python Functions, Modules and File Handling

  • Defining Functions, Parameters and Arguments, Return Values
  • Variable Scope, Recursive Functions
  • Modular Programming Concepts, Importing and Using Modules
  • Built-in Modules: math, random, datetime
  • Reading and Writing Text Files, Working with CSV Files
  • File Operations using Lists and Dictionaries
  • Exception Handling (try, except, finally), Custom Exceptions
  • Debugging Basics

Module 4 — Python for Data Analysis and Visualization

  • Introduction to NumPy, Creating and Manipulating Arrays
  • Mathematical and Statistical Operations
  • Introduction to Pandas, Series and DataFrames
  • Data Import and Export, Data Cleaning and Data Preparation
  • Introduction to Matplotlib, Line Charts, Bar Charts, Pie Charts, Histograms
  • Basic Data Storytelling using Visualizations

Hands-on Practice

  • Simple Calculator
  • Temperature Converter
  • BMI Calculator
  • Prime Number Checker
  • Scientific Calculator Functions
  • Password Generator

Course Outcome

  • By the end of this training, participants will be able to write clean, structured Python code, work confidently with core data structures, handle files and exceptions, and use NumPy, Pandas and Matplotlib to analyze and visualize data through hands-on mini projects.

Python Intermediate

36 Hours · 6 Weeks (Weekend Batch) · 6 modules
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Module 1 — Functions (Week 1, 6 Hours)

  • Functions and Function Arguments
  • Return Values
  • Lambda Functions
  • Recursion

Module 2 — Modules & Packages (Week 2, 6 Hours)

  • Modules & Packages
  • pip Package Manager
  • Virtual Environment
  • Python Standard Library

Module 3 — File Handling (Week 3, 6 Hours)

  • File Handling
  • CSV Files
  • JSON Files
  • Exception Handling

Module 4 — Object-Oriented Programming (Week 4, 6 Hours)

  • Object-Oriented Programming
  • Classes & Objects
  • Constructors
  • Instance & Class Variables

Module 5 — OOP Concepts (Week 5, 6 Hours)

  • Inheritance
  • Polymorphism
  • Encapsulation
  • Abstraction
  • Magic (Dunder) Methods

Module 6 — Advanced Python Concepts (Week 6, 6 Hours)

  • Regular Expressions
  • Iterators
  • Generators
  • Decorators
  • Mini Project

Course Outcome

  • By the end of this training, participants will be able to build modular, object-oriented Python applications, work with files, packages and virtual environments, and use decorators, generators and regular expressions in real-world scripts.

Python Advanced

36 Hours · 6 Weeks (Weekend Batch) · 6 modules
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Module 1 — Database Programming (Week 1, 6 Hours)

  • Database Programming, SQL Basics
  • SQLite
  • Oracle Database Connectivity
  • CRUD Operations

Module 2 — REST APIs (Week 2, 6 Hours)

  • REST APIs, Requests Library
  • Working with JSON APIs
  • API Authentication

Module 3 — Web Scraping (Week 3, 6 Hours)

  • Web Scraping, BeautifulSoup
  • Data Extraction
  • Automation Scripts

Module 4 — Multithreading & Logging (Week 4, 6 Hours)

  • Multithreading
  • Multiprocessing
  • Logging
  • Configuration Files

Module 5 — Git & Code Quality (Week 5, 6 Hours)

  • Git & GitHub
  • Code Optimization
  • Debugging Techniques
  • Packaging Python Applications

Module 6 — Industry Project & Career Prep (Week 6, 6 Hours)

  • Industry-Level Project, Resume Project Development
  • Coding Best Practices
  • Interview Questions & Mock Interview
  • Course Completion & Certification

Projects Included

  • Student Management System
  • Library Management System
  • Employee Payroll System
  • File Automation Tool
  • Database CRUD Application
  • REST API Integration Project
  • Web Scraper
  • Final Industry-Level Python Project

Course Outcome

  • By the end of this training, participants will be able to build database-driven and API-integrated Python applications, automate real-world tasks, follow professional coding and version-control practices, and present an industry-level project in interviews.

Complete Python Course

Includes Python Fundamental + Intermediate + Advanced · 3 major modules
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Major Module 1 — Python Fundamental

  • Python basics, control structures, and data types
  • Lists, Tuples, Sets and Dictionaries
  • Functions, modules and file/exception handling
  • NumPy, Pandas and Matplotlib for data analysis & visualization
  • Hands-on: Calculator, BMI Calculator, Password Generator and more

Major Module 2 — Python Intermediate

  • Functions, lambda expressions and recursion
  • Modules, packages, pip and virtual environments
  • File handling with CSV/JSON and exception handling
  • Object-Oriented Programming: classes, inheritance, polymorphism, encapsulation
  • Regular expressions, iterators, generators and decorators

Major Module 3 — Python Advanced

  • Database programming with SQL, SQLite and Oracle connectivity
  • REST APIs, JSON handling and authentication
  • Web scraping and automation scripts
  • Multithreading, multiprocessing and logging
  • Git/GitHub, code optimization and an industry-level capstone project

Course Outcome

  • This complete pathway takes participants from Python fundamentals to advanced, industry-ready development — covering data analysis, OOP, APIs, automation and databases — culminating in a resume-worthy capstone project and interview preparation.

Python + DBA

90 Hours · 15 Weeks (Weekend Batch) · 1 module
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Module 1 — Python for Oracle DBA (Database Automation & Scripting)

  • Python Basics for DBAs: installing Python & VS Code, variables, operators, conditionals, loops, functions, modules, exception handling
  • File Handling & OS Automation: reading/writing files, CSV & Excel, JSON, OS/Shutil/Pathlib modules, environment variables
  • Oracle Database Connectivity: python-oracledb, connection pooling, executing SQL, CRUD operations, transactions, calling PL/SQL procedures
  • Database Monitoring Automation: tablespace, session and user monitoring, blocking session detection, alert log and listener monitoring, database health checks
  • Backup & Recovery Automation: running RMAN from Python, archive log monitoring, Data Pump export/import automation, backup validation
  • Linux Automation for DBAs: shell commands, process/disk/memory/CPU monitoring, cron job automation
  • Email Alerts & Reporting: SMTP automation, HTML email reports, attachments, scheduled reports, error notifications
  • Final Capstone Project: Oracle DBA Automation Toolkit — health checks, tablespace & user monitoring, RMAN backup status, email alerts, scheduled execution

Tools Covered

  • Python 3.x, VS Code
  • Oracle Database 19c/21c, python-oracledb, SQL Developer, SQL*Plus, RMAN
  • Linux (Oracle Linux/Ubuntu)
  • Git & GitHub

Course Outcome

  • By the end of this training, participants will be able to automate Oracle DBA tasks using Python — from health checks and backups to monitoring and email reporting — and build a complete DBA automation toolkit for real production use.

Oracle DBA

54 Hours · Live Online Weekend Batch · 1 module
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Module 1 — Oracle DBA Fundamentals to Advanced Automation

  • Oracle DBA Fundamentals: Oracle architecture, installation & configuration, database creation, instance management, tablespaces, datafiles, users, roles, profiles, password policies
  • Oracle DBA Administration: backup & recovery, RMAN, import/export (Data Pump), flashback technology, performance monitoring, alert log, listener configuration, database security, patching basics
  • Advanced DBA + Automation: Oracle RAC overview, ASM basics, Oracle Cloud introduction, Linux commands for DBAs, shell scripting basics, Python automation for DBA, database health check scripts, backup automation, final capstone project

Tools Covered

  • Python 3.x, VS Code
  • Oracle Database 19c/21c, Oracle SQL Developer, Oracle Enterprise Manager
  • Linux (Oracle Linux/Ubuntu)
  • Git & GitHub

Live Projects

  • Employee Management System (Python + Oracle)
  • Oracle Backup Automation Script
  • Database Health Monitoring Tool
  • Oracle User Management Automation
  • Database Performance Monitoring Dashboard

Course Outcome

  • By the end of this training, participants will be able to install, configure and administer Oracle databases, manage backup/recovery and security, and apply Python-based automation to real-world DBA operations.

Data Science

120+ Hours · 3 Months (Weekend Batch) · 1 module
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Module 1 — Data Science Professional Program

  • SQL for Data Science: database basics, MySQL, CRUD operations, joins, group by, aggregate & window functions — Project: Sales Database Analysis
  • NumPy & Pandas: arrays, DataFrames, data cleaning, missing values, merge/join, group by, feature engineering, Seaborn (heatmaps, pairplot, boxplot, correlation matrix) — Project: Customer Sales Analysis
  • Data Visualization: Matplotlib, Seaborn, Plotly, dashboard design, business reporting — Project: Sales Dashboard
  • Mathematics: basic algebra, probability, statistics (mean, median, mode, standard deviation, variance, correlation, normal distribution)
  • Excel: pivot tables, charts, conditional formatting, VLOOKUP/XLOOKUP, INDEX-MATCH, Power Query, dashboards
  • Data Processing & EDA: missing values, duplicates, outliers, feature scaling, encoding, feature selection, distribution/correlation/uni-bi-multivariate analysis
  • Power BI: interface, data import & cleaning, DAX basics, interactive dashboards — Project: Business Analytics Dashboard
  • Capstone Project & Placement Prep: choose from Customer Churn, Loan Prediction, Sales Forecasting, HR Analytics, Healthcare Prediction, or Movie Recommendation System; resume building, LinkedIn optimization, GitHub portfolio, mock interviews

Tools Covered

  • Jupyter Notebook, VS Code
  • NumPy, Pandas, Matplotlib, Seaborn, Plotly, Scikit-learn
  • MySQL, Power BI, Git & GitHub

Course Outcome

  • By the end of this training, participants will be able to clean, analyze and visualize data using Python, SQL and Power BI, apply core statistics, and deliver a complete data science capstone project ready for job interviews.

Machine Learning (ML)

104 Hours · 18.5 Weeks (Weekend Batch) · 1 module
View outline

Module 1 — Machine Learning Professional Course

  • Introduction to ML: AI vs ML vs Deep Learning, types of ML (supervised, unsupervised, semi-supervised, reinforcement), ML workflow, Python libraries (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch)
  • Mathematics for ML: linear algebra (vectors, matrices, eigenvalues/eigenvectors), calculus (differentiation, gradients), probability & statistics, Bayes theorem
  • Data Preprocessing & EDA: missing/duplicate values, outlier detection, feature scaling, encoding, train-test split, visualization (histograms, boxplots, heatmaps)
  • Feature Engineering: feature selection/extraction, dimensionality reduction, PCA
  • Supervised Learning: regression (linear, polynomial, ridge, lasso, elasticnet) and classification (logistic regression, KNN, Naive Bayes, decision tree, random forest, SVM, gradient boosting, XGBoost, LightGBM, CatBoost)
  • Unsupervised Learning: K-Means, hierarchical clustering, DBSCAN, Apriori/FP-Growth, PCA, t-SNE, UMAP
  • Model Evaluation: MAE/MSE/RMSE/R², accuracy, precision, recall, F1, ROC-AUC, confusion matrix
  • Hyperparameter Tuning & Ensembles: cross-validation, grid/random search, bagging, boosting, stacking, voting classifiers
  • Reinforcement Learning: agent, environment, reward, policy, Q-learning, intro to Deep Q Networks
  • Real-World Projects: House Price Prediction, Loan Approval Prediction, Customer Churn, Fake News Detection, Spam Detection, Face Recognition, Stock Price Prediction, Recommendation System, Disease Prediction, Sales Forecasting

Course Outcome

  • By the end of this training, participants will be able to build, tune and evaluate supervised, unsupervised and ensemble machine learning models, and apply them to real-world industry projects.

Artificial Intelligence (AI)

90 Hours · 15 Weeks (Weekend Batch) · 1 module
View outline

Module 1 — Artificial Intelligence (AI) Professional Course

  • Introduction to AI: what is AI, history & evolution, types of AI, industry applications, AI ethics & responsible AI
  • Python for AI: basics, functions, OOP concepts, file handling, APIs, working with JSON
  • Prompt Engineering: prompt fundamentals, zero/one/few-shot prompting, chain-of-thought prompting, prompt optimization & evaluation
  • Generative AI: large language models (LLMs), AI assistants, text/image/audio/video generation, multimodal AI
  • AI Productivity Tools: ChatGPT, Claude, Gemini, Microsoft Copilot, Perplexity AI, NotebookLM
  • AI for Content Creation: AI writing, presentations, image/video/voice generation, social media automation
  • AI Automation: workflow & email automation, document automation, AI-powered business processes, no-code integrations
  • AI Agents: agent workflows, task planning, multi-agent concepts, business use cases
  • AI APIs & Integrations: OpenAI API basics, authentication, building & integrating AI-powered applications
  • AI Projects: AI Chatbot, Resume Builder, Content Generator, Research Assistant, Email Assistant, Document Summarizer, Customer Support Assistant

Course Outcome

  • By the end of this training, participants will be able to write effective prompts, use generative AI tools and APIs, build AI agents and automations, and deliver practical AI-powered project solutions.

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