Generative AI  /  Beginner to Mastery

Data Analytics with Generative AI

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Course Duration

375 Hours

 

Course Material

Live. Online. Interactive.

Opens doors to diverse roles in data science, AI, and business analysis.

Offers skills that are applicable across various industries and job roles.

Provides opportunities to network with peers and professionals in the data and AI fields.

Keeps you future-ready by aligning your expertise with emerging technologies and market trends.

KEY HIGHLIGHTS OF DATA ANALYTICS WITH GENERATIVE AI PROGRAM

1) Weekly sessions with industry professionals

2) Dedicated Learning Management Team

3) 375 hours of hands-on learning experience

4) Over 120 hours live sessions spread across 05 months

5) 150 hours of self-paced Learning

6) Learn from Industry Experts.

🔺More than 40+ industry-related projects and case studies

🔺24*7 Support

🔺1:1 Mock Interview

🔺Designed for both working professionals and fresh graduates

🔺Competitive Edge and Innovation

🔺 Personalised mentorship sessions with industry experts

🔺Dedicated Learning Management Team

🔺No-Cost EMI Option

🔺High Demand and Career Opportunities

🔺Problem-Solving and Critical Thinking

WHY JOIN DATA ANALYTICS WITH GENERATIVE AI PROGRAM?

Comprehensive Coverage

Learn a wide range of essential tools and techniques, from data analytics to cutting-edge AI technologies.

Industry-Relevant Skills

Gain highly sought-after skills that enhance your employability in today’s job market.

Hands-On Learning

Apply your knowledge through practical projects and real-world scenarios.

Career Growth

Open doors to advanced career opportunities and higher earning potential in data science and AI.

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Data Analytics with Generative AI OVERVIEW

This program helps participants to be proficient in both data analysis and AI. The program includes Advanced Excel, Python, Basic Statistics, SQL & data visualization with Tableau/Power BI. It also delves into Generative AI technologies such as Hugging Face Transformers, LLMs and DALL-E 2, MidJourney, GANs, RAG, LangChain to give you a strong foundation in data management and cutting-edge advanced AI tools.

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Data Analytics with Generative AI Objectives

The Program is aimed at offering essential knowledge in the key field areas related to data analysis, Python programming and statistical techniques useful for decision making based on data. This meant they wanted me to work more on SQL for database management and Tableau for data visualization. Further, the course also exposes learners to state-of-the-art Generative AI technologies such as: Hugging Face Transformers, LLMs, DALL-E 2, GANs so they could use more advanced tools in their projects. The programme aims to make the participants well versed with data analytics and AI.

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Why Learn Data Analytics with Generative AI ?

Master Python & Data Analysis

Gain proficiency in Python, statistical analysis, and data visualization to effectively explore and understand data.

Explore Advanced AI

Dive into deep learning, NLP, and reinforcement learning to design solutions for complex tasks.

Hands-On with Generative AI

Develop skills with tools like DALL-E 2, MidJourney, and GANs for creative image generation and data synthesis.

Innovate with LLMs & LangChain

Leverage Large Language Models, GPT, and LangChain to create and manage cutting-edge AI applications.

Enhance Communication

Effectively convey complex information and findings using data visualization techniques.

Real-World Projects

Apply your knowledge through industry-focused projects and case studies, bridging theory with practical experience.

Career Advancement

Equip yourself with future-ready skills that open opportunities in AI, data science, and advanced analytics roles across industries.

Drive Innovation

Contribute to the development of cutting-edge technologies and solutions through a deep understanding of these tools.

Program Advantages

✅ Master essential tools like Advanced Excel, Python, SQL, Tableau, and advanced Generative AI technologies.

✅ Learn up-to-date practices that match current market demands.

✅Apply knowledge through real-world projects and hands-on exercises.

✅ Develop advanced skills in Generative AI, including LLMs, DALL-E 2, and GANs.

✅ Equip yourself with versatile skills applicable across various sectors.

✅ Stay ahead with cutting-edge tools that shape the future of data and AI.

✅ Master essential tools like Advanced Excel, Python, SQL, Tableau, and advanced Generative AI technologies.

✅ Learn up-to-date practices that match current market demands.

✅ Develop advanced skills in Generative AI, including LLMs, DALL-E 2, and GANs.

✅ Equip yourself with versatile skills applicable across various sectors.

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Data Analytics with Generative AI program Certifications

Data Analytics with Generative AI Curriculum

Module 01 - Python
Lecture 01: Introduction to Python, Why Python, Variables, Data Types, Type Casting, Strings, Indexing
Lecture 02: Operators and Conditional Statements, Looping Statements and its Control Statement
Lecture 03: Lambda Functions, *args, **kwargs, Functions
Lecture 04: Data Structures – List, Tuple, and List Comprehensions
Lecture 05: Data Structures – Set and Dictionaries
Lecture 06: Classes, Objects and Constructors, Inheritance
Lecture 07: Polymorphism, Abstraction and Encapsulation
Lecture 08: Connecting to Databases, Establishing Connections to Databases, Executing SQL Queries, ORM (Object-Relational Mapping), Working with NoSQL Databases
Lecture 09: Introduction to Numpy and Pandas
Lecture 10: Introduction to Seaborn and Matplotlib
Module 02 - Statistics
Lecture 11: Introduction to Statistics, Descriptive Statistics, Sample, Population, Major of Central Tendency, Standard Deviation,
Lecture 12: Variance, Range, IQR, Outliers, Correlation, Covariance Skewness, Kurtosis, Probability
Lecture 13: Probability, Probability distributions, Central Limit Theorem, Binomial and Poisson Distribution
Lecture 14: Normal Distribution, Type I & Type II Error
Lecture 15: T-test, Z-test, Hypothesis Testing
Interview Questions
Module 03 - SQL
Lecture 16: Basics of Database, Types of Database, Data Types, SQL Operators, Expressions, Create, Insert
Lecture 17: Drop, Truncate, Delete, Alter, Update, Select, Range, Operator, IN, Wildcard, Like, Clause
Lecture 18: Constraint, Aggregation Function, Group By, Order By, Having
Lecture 19: Joins, Case, Complex Queries, Doubt Clearing
Module 04 - Tableau
Lecture 20: Tableau Desktop, Tableau Products
Lecture 21: Data Import, Measures, Filters
Lecture 22: Data Transformation, Marks, Dual Axis
Lecture 23: Manage Worksheets, Data Visualization, Dashboarding, Project
Module 05 - Advanced Excel (Self Paced and Complementary)
Lecture 24: Introduction to Data Analytics, Scope of Data Analytics
Lecture 25: Microsoft Excel Overview, Basic Navigation and Usage, Cell Referencing, Formatting Excel, Advanced Formatting, Shortcuts and Basic Formulas
Lecture 26: Sorting, Filtering, Advanced Filtering, Charts, Types of Charts, Advanced Charting Techniques, and Pivot Tables (Creating, Grouping and Summarizing Data)
Lecture 27: Lookup Functions, VLOOKUP, Using VLOOKUP with Multiple Criteria, HLOOKUP, Combining HLOOKUP with Other Functions, MATCH Function, Using MATCH for Dynamic Referencing
Lecture 28: Introduction to VBA & Macros, Understanding VBA Basics, Debugging and Error Handling, Advanced VBA Techniques, Integrating VBA with Excel Functions, Designing Effective Dashboards, Building a Dashboard
Lecture 29: Understanding the Basics of Data Analysis, Data Import and Cleaning, Using Formulas and Functions, Data Visualization, Descriptive Statistics
Lecture 30: Advanced Data Analysis Techniques, DAX, Scenario and Sensitivity Analysis, Dashboards and Reports, Case Studies and Real-World Applications, Practical Examples of Data Analysis in Excel

 

Module 06 - Foundations of Generative AI
Lecture 31: Evolution of AI (Rule-based → ML → GenAI → Agentic AI), Hype vs Reality, Industry Adoption of GenAI, Ethical & Responsible AI
Lecture 32: How Generative AI Works: LLM intuition, Tokens, Embeddings, Context Window, Capabilities & Limitations (Hallucination, Bias, Cost)
Lecture 33: Multimodal AI Systems: Text, Image, Tables, Documents. Industry Applications. Case Study: Invoice and financial report understanding
Lecture 34: Core Generative AI Tasks: Text generation, Classification, Summarization, Question Answering. Hands-on Case Study: Resume screening and document summarization
Lecture 35: Prompt Engineering Fundamentals: Zero-shot, Few-shot, Role Prompting, Prompt Templates. Case Study: Marketing content generation
Module 07 - Prompt Engineering, RAG & Multimodal RAG
Lecture 36: Advanced Prompting: Prompt Debugging, Guardrails, Prompt Evaluation, Response Optimization. Hands-on Case Study: Improving incorrect chatbot responses
Lecture 37: Retrieval-Augmented Generation (RAG): Embeddings, Vector Search, RAG Architecture. Hands-on Case Study: Chat with company policy documents
Lecture 38: Multimodal RAG: Text + Image + Table Retrieval, Document Intelligence. Case Study: Invoice and scanned document Q&A system
Module 08 - Agentic AI Systems & Framework Internals
Lecture 39: Introduction to Agentic AI: Agent vs Chatbot Workflow, Agent Lifecycle, Levels of Autonomy, Human-in-the-loop Systems. Case Study: AI Research Assistant
Lecture 40: Agent Architecture & Design Patterns: Planner-Executor-Evaluator, ReAct Pattern, Tool-Use Pattern, Reflection. Hands-on Case Study: Recruiter Agent design
Lecture 41: Agent Memory, Tools & Planning: Short-term vs Long-term Memory, Tool Calling, Feedback Loops. Case Study: Customer support agent with memory
Lecture 42: Agent Framework Internals (Conceptual): How frameworks manage chains, agents, tools and memory. Positioning of LangChain. Design considerations without deep syntax
Module 09 - Fine-Tuning, No-Code Agents & Capstone Project
Lecture 43: LLM Fine-Tuning from an Industry Perspective: Prompting vs RAG vs Fine-Tuning, PEFT and LoRA concepts, Cost, Risk and Governance Considerations
Lecture 44: No-Code and Low-Code Agentic AI: Use cases, Benefits and Limitations, Visual Agent Design. Demo: No-code content or support agent
Lecture 45: Capstone Design Session: End-to-End Generative AI and Agentic AI Solution. Hands-on Project: AI Customer Support Supervisor Agent

Data Analytics with Generative AI Skills Covered

Data Analytics with Generative AI Tools Covered

Data Analytics with Generative AI Program Benefits

Cutting-Edge Knowledge
Stay ahead with the latest advancements in AI and generative technologies.
Hands-On Experience
Apply your learning in real-world scenarios using state-of-the-art tools.
Comprehensive Learning
Gain a holistic understanding of AI, from foundational concepts to advanced applications.
Career Growth
Enhance your employability in a rapidly evolving and high-demand field.
Expert Support
Learn from industry professionals with deep expertise in AI and machine learning.
Practical Applications
Develop the ability to create and deploy AI solutions that can be applied across various industries.
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Admission Process

The application process consists of three simple steps. An offer of admission will be made to selected candidates based on the feedback from the interview panel. The selected candidates will be notified over email and phone, and they can block their seats through the payment of the admission fee.