Data Eng.+ Data Analytics + Python AI

Integrated Program: Data Engineering + Data Analytics + Python AI

Foundations of Data & Analytics

  • Understanding data types: structured, semi-structured, unstructured
  • Role of Data Engineering vs Data Analytics vs AI
  • Real-world enterprise data flow overview
  • Industry use cases and project mindset

Python Programming for Data

  • Python basics for data handling
  • Data structures, functions, and modules
  • File handling (CSV, JSON, Excel)
  • Exception handling and best practices
  • Writing clean, reusable, production-ready code

Data Analytics with Python

  • Data analysis using Pandas & NumPy
  • Data cleaning, transformation, and preprocessing
  • Exploratory Data Analysis (EDA)
  • Data visualization using Matplotlib & Seaborn
  • Business insights generation from datasets

SQL & Databases for Analytics

  • Database concepts and architecture
  • Writing complex SQL queries
  • Joins, subqueries, views, indexes
  • Performance optimization basics
  • Analytics-driven query design

Data Engineering Concepts

  • Data pipelines and ETL/ELT concepts
  • Data ingestion from multiple sources
  • Batch vs streaming data processing
  • Data validation and quality checks
  • Enterprise data architecture overview

Big Data & Cloud Exposure

  • Introduction to Big Data ecosystem
  • Basics of Hadoop, Spark concepts
  • Cloud data services overview (AWS/Azure/GCP)
  • Data storage: Data Lake, Data Warehouse
  • Real-time data processing fundamentals

Python for AI & Machine Learning

  • Introduction to AI & Machine Learning
  • Supervised & unsupervised learning concepts
  • Model building using Python libraries
  • Data preparation for ML models
  • Model evaluation and optimization basics

Automation & Intelligent Data Processing

  • Automating data workflows using Python
  • Scheduling and monitoring data jobs
  • AI-driven insights generation
  • Real-world automation use cases
  • Industry best practices

Mini Projects & Case Studies

  • Data Analytics project (business dataset)
  • Data Engineering pipeline case study
  • Python AI model implementation
  • End-to-end project explanation
  • Resume-oriented project documentation

Industry Readiness & Career Guidance

  • Resume building for Data roles
  • Interview preparation (technical + HR)
  • Real interview questions & scenarios
  • Role mapping: Data Analyst, Data Engineer, AI Engineer
  • Job referral and industry guidance support