Ai powered Data Analytics Engineer Training in Hyderabad
Ai powered Data Analytics
With
Real time projects
What is Ai Powered Data Analytics
๐ The AI-Powered Data Analytics Engineer Training is a professional bridge between traditional data analysis and modern artificial intelligence. It transitions students from foundational data literacy to advanced data engineering and AI-driven insights.
๐ Phase 1: Foundational Data Literacy & Statistics
- ๐ Theoretical Base: Covers the “what” and “why” of data analytics, focusing on analytical thinking.
- ๐ Mathematical Rigor: Deep dive into Descriptive and Inferential Statistics.
- ๐งช Validation: Learning Hypothesis Testing and Regression Analysis to ensure data-driven decisions are mathematically sound.
๐ค Phase 2: AI & Generative AI Integration
- โจ Modern Evolution: Unlike traditional courses, this integrates GenAI and LLMs (Large Language Models) from the start.
- โ๏ธ Prompt Engineering: Mastering the art of communicating with AI to automate complex workflows.
- ๐ ๏ธ AI Automation: Using tools like ChatGPT, Copilot, and Gemini to generate SQL queries, cleanse messy data, and summarize complex dashboard insights.
โ๏ธ Phase 3: Technical Tool Mastery
- ๐ Spreadsheet Power: Advanced Excel, including Power Query and Power Pivot for sophisticated modeling.
- ๐๏ธ Database Command: Comprehensive SQL training, from basic CRUD operations to advanced Window Functions.
- ๐ Visualization Duel: Mastery of both Power BI (DAX) and Tableau (LOD & Mapping) to create high-impact business stories.
- ๐ Programming: Core Python basics focused specifically on NumPy and Pandas for data manipulation.
๐๏ธ Phase 4: Data Engineering & Practical Application
- ๐ The ETL Pipeline: Learning the Extract, Transform, Load process to handle raw data at scale.
- ๐ Big Data Scale: Introduction to Hadoop, Spark, and Databricks for processing massive datasets.
- ๐ Real-World Impact: The course culminates in Capstone Projects (e.g., Airbnb analysis, Telecom churn, and IPL analysis) and diverse case studies across Retail, HR, and Finance sectors.
Ai powered Data Analytics Training Course Content
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๐ 1. Introduction to Data Analysis
- ๐งฉ What is Data & Types of Data
- โ What, Why & How of Data Analytics
- ๐ก Analytical Thinking & Problem-Solving
- โ๏ธ Components of Data Analytics
- ๐ ๏ธ Data Analysis Techniques
- ๐ง Popular Tools
๐ง 2. AI Overview
- ๐ฐ๏ธ Introduction & Evolution of AI
- ๐ AI vs ML vs Deep Learning vs GenAI
- ๐ค Machine Learning Basics
- ๐ฃ๏ธ NLP Basics
- ๐ธ๏ธ Deep Learning & Neural Networks
- ๐จ What is Generative AI
- ๐ LLMs & How they are trained
- โ๏ธ Prompt Engineering
- ๐ ๏ธ GenAI Tools (ChatGPT, Copilot, Gemini)
- ๐ผ Use Cases of AI in Data Analytics
- โ๏ธ Ethics, Bias & Limitations
- ๐ต๏ธโโ๏ธ AI Agents
- ๐ Galaxy AI
- ๐งช Julius AI
๐งน 3. Data Pre-Processing & Cleansing
- ๐ Raw Data Characteristics
- ๐ฅ Data Collection Process
- ๐ค AI Tools for Data Collection
- ๐ Types of Data Sources
- ๐ ETL Process
- โฑ๏ธ Batch vs Real-Time Processing
- ๐งผ Data Cleansing (Missing Values, Outliers, Duplicates)
- โ๏ธ Data Transformation (Formatting, Filtering, Aggregation)
- ๐ค Copilot for Data Cleansing
- ๐ Case Study: Sales Data
๐ 4. Exploratory Data Analysis (EDA)
- ๐ Introduction & Need
- ๐ Graphical & Non-Graphical Analysis
- ๐ฏ Data Types & Sampling
- ๐ข Numerical & Categorical Data
- ๐ก Patterns & Insights
- ๐ Charts & Visualization Types
- ๐ค AI Tools for EDA
- ๐ Case Studies: Supermarket Sales, New York Housing Prices
๐ 5. Statistics for Data Analytics
- ๐ Introduction & Types of Statistics
- ๐ฅ Population vs Sample
- ๐ Descriptive Statistics
- ๐ฒ Probability & Distributions
- ๐งช Hypothesis Testing
- โ ๏ธ Type I & II Errors
- ๐ Regression Analysis
- ๐ค AI-based Statistical Analysis
- ๐ค Copilot for Statistics
- ๐ Case Studies: Heart Risk Analysis, Email Campaign Impact
๐ ๏ธ 6. Data Analysis Tools-1
A. Excel & Copilot
- ๐ Excel Basics & Formatting
- ฦ<sub>x</sub> Functions (Text, Logical, Math, Date, Statistical)
- ๐ Lookup Functions & Data Tools
- ๐ช๏ธ Sorting, Filtering & Conditional Formatting
- ๐ Charts, Pivot Tables, Dashboards
- ๐ Power Query, Power Pivot
- ๐ค AI-based Dashboard Summary
- ๐ Case Studies: Employee Analysis, Student Performance, Adidas Sales
B. SQL
- ๐๏ธ DB Concepts, Schema, Tables
- โ CRUD Operations
- ๐ฏ WHERE, ORDER BY
- โ Operators & Functions
- ๐ GROUP BY, HAVING
- ๐ Joins, Subqueries, Window Functions
- ๐ Views, Indexes, Procedures
- โ๏ธ BigQuery SQL
- ๐ค AI-based SQL Query Generation (Gemini)
- ๐ Case Studies: HR Data, Customer Orders, Pizza Delivery
๐ 7. Data Analysis Tools-2
C. Power BI
- ๐ Data Connections
- ๐ Power Query Transformations
- ๐๏ธ Data Modeling & Relationships
- ๐งฎ DAX (Functions, Measures, Time Intelligence)
- ๐ Visualization & Filters
- ๐โโ๏ธ Q&A, Insights using AI
- ๐ Dashboard Publishing
- ๐ Case Study: Adventure Works
D. Tableau
- ๐ก Visualization Concepts
- ๐ Dimensions & Measures
- ๐ Data Connections
- ๐ Relationships, Joins, Blending
- ๐บ๏ธ Visualizations (Charts, Maps, Heatmaps)
- ๐งฎ LOD & Table Calculations
- ๐ Dashboards & Stories
- ๐ Case Studies: Superstore, Bookshop, Amazon Sales Dashboard
E. Python & AI Assisted Analysis
- ๐ Python Basics
- ๐ฆ Data Types, Lists, Tuples, Dict
- ๐ Loops & Functions
- ๐ผ Numpy & Pandas
๐ 8. Big Data & Capstone Projects**
- ๐ Big Data Analytics
- ๐ What is Big Data
- ๐ Hadoop, HDFS, MapReduce
- ๐ Hive, Spark & Spark SQL
- ๐งฑ Databricks Practice
- ๐ Case Study: Olympics Analysis
- ๐ Capstone Projects: Rides Data Analysis, Airbnb Analysis, Telecom Churn, IPL Analysis, Brazilian e-commerce Analysis
Ai powered Data Analytics Training Demo Videos
Job Market for Ai powered Data Analytics
The job market for the AI-powered Data Analytics Engineer role is currently in a state of explosive growth. As of 2026, companies have shifted from simple data collection to requiring “Insight Architects” who can leverage AI to drive strategy.
Here is the market outlook for someone completing this specific curriculum:
๐ Market Demand & Growth
- Massive Opportunity: In India alone, there are projections of 5โ7 lakh job openings in 2026 across tech hubs like Hyderabad and Bengaluru.
- High Growth Rate: AI-augmented data roles are seeing a 25-30% annual growth, significantly outperforming traditional “reporting-only” analyst roles.
- The “AI Gap”: There is a critical shortage of professionals who understand both Data Pipelines (SQL/ETL) and Generative AI (LLMs/Prompt Engineering), making graduates of this dual-threat course highly competitive.
๐ฐ Salary Expectations (India 2026)
The integration of AI skills acts as a massive salary multiplier compared to traditional data roles.
| Experience Level | Traditional Analyst | AI-Powered Analytics Engineer |
| Fresher (0โ2 yrs) | โน4 โ โน6 LPA | โน7 โ โน12 LPA |
| Mid-Level (3โ6 yrs) | โน8 โ โน12 LPA | โน15 โ โน32 LPA |
| Senior (7+ yrs) | โน18 โ โน25 LPA | โน35 โ โน60+ LPA |
๐ข Top Hiring Industries
- FinTech & BFSI: Using AI for real-time fraud detection and automated risk modeling.
- Healthcare: (Fastest Growing) Focusing on predictive patient outcomes and genomic data analysis.
- E-commerce & Retail: Personalized customer journeys using GenAI and inventory optimization.
- Supply Chain: Solving “logistics puzzles” using real-time IoT data and Big Data tools (Spark/Databricks).
๐ท๏ธ Target Job Titles
With the skills from this course, you are qualified for:
- AI Data Analyst / Insight Engineer
- Business Intelligence (BI) Specialist (Power BI/Tableau Focus)
- Data Analytics Engineer
- GenAI Prompt Engineer (Data Focused)
- Analytics Consultant
๐ Why This Curriculum Wins
The market is currently penalizing “manual” workers. Because this course covers Copilot for Data Cleansing, AI-based SQL generation, and Big Data (Spark/Hive), you are positioned not just as someone who reads data, but as someone who automates the entire intelligence lifecycle. Companies like Accenture, Deloitte, Amazon, and JPMorgan Chase are actively prioritizing candidates who can demonstrate the “Human-in-the-loop” AI workflow taught in this program.
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