Generative AI in Data Science

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Generative AI in Data Science

COURSE OVERVIEW

The Generative AI in Data Science course helps learners understand how AI is transforming modern data science, analytics, research, and decision-making. Today, data professionals need faster preparation, smarter modeling, automated workflows, and clearer insights. Therefore, practical GenAI skills are becoming essential for future-ready data roles.

Through this course, learners explore how Generative AI can support data cleaning, preprocessing, code generation, feature engineering, AutoML, predictive modeling, synthetic data generation, visualization, and storytelling. In addition, the course connects AI concepts with real data science use cases.

Moreover, learners understand both the opportunities and responsibilities of using AI in data-driven environments. As a result, participants become better prepared to automate workflows, generate insights faster, and use AI responsibly in analytical decision-making.

WHAT YOU'LL LEARN

By completing this Generative AI in Data Science course, learners will understand how AI can improve data handling, modeling, analytics, visualization, and research workflows.

Automate data handling, preprocessing, and code generation using AI tools
Use Generative AI for feature engineering, AutoML, and predictive modeling
Generate and visualize synthetic data for research and analysis
Apply responsible and explainable AI techniques in data-driven environments
Use AI tools for faster data preparation, smarter modeling, and storytelling

Overall, this course helps learners empower data, innovate faster, automate workflows, and elevate data science with Generative AI.

Empower Your Data - Innovate, Automate, and Elevate with Generative AI.

CURRICULUM

The Generative AI in Data Science curriculum is divided into nine practical learning sections. Each section helps learners understand how Generative AI, analytics, automation, modeling, visualization, and responsible data science work together.

In addition, the course begins with GenAI fundamentals and gradually moves toward data automation, AI-enhanced analytics, synthetic data, explainability, visualization, and advanced data science applications. As a result, learners build both conceptual clarity and practical data-focused AI skills.

Module 1: Introduction to Generative AI and Its Evolution

Topics Covered:

This section introduces Generative AI and explains why it has become important across industries. It also helps learners understand the history, limitations, and ethical concerns connected with AI adoption.

Meaning of Generative AI
History and milestones in AI development
Traditional AI versus Generative AI
Key drivers such as big data, compute power, and neural networks
Real-world applications of GenAI
Limitations and ethical considerations

As a result, learners build a strong foundation before moving into advanced AI applications in data science and analytics.

Module 2: Fundamentals of Large Language Models (LLMs)

Topics Covered

Large Language Models are central to many modern GenAI applications. Therefore, this section explains how models such as GPT, BERT, T5, and PaLM work behind the scenes.

Transformers and attention mechanism
GPT, BERT, T5, and PaLM comparisons
Training versus inference
Prompt engineering basics
Zero-shot, few-shot, and fine-tuning approaches
Responsible use of LLMs

In addition, learners understand how LLMs can be used responsibly in data analysis, research, reporting, automation, and decision-support workflows.

Module 3: Generative AI Tools and Platforms

Topics Covered

This section introduces popular AI tools and platforms used across professional domains. Instead of focusing only on theory, learners understand how different tools support analytics, automation, coding, and business goals.

ChatGPT, Claude, Bing AI, and GitHub Copilot
Image generation tools such as DALL·E, Midjourney, and Stable Diffusion
AI tools for video, music, and voice
Open-source and proprietary GenAI tools
Tool selection based on professional goals

Moreover, this section helps learners choose suitable GenAI tools for productivity, data workflows, research, visualization, and analytical decision-making.

Module 4: Prompt Engineering and Task Design

Topics Covered

Prompt engineering helps professionals get better results from AI tools. This section focuses on writing clear prompts, designing useful tasks, and reducing weak or misleading outputs.

Anatomy of an effective prompt
Zero-shot, few-shot, and structured prompting strategies
Task types such as summarization, translation, code generation, and analysis
Tool-specific prompting examples
Reducing hallucinations and bias through prompt tuning

Consequently, learners become more confident in using AI tools for data preparation, coding support, exploratory analysis, model explanation, and business communication.

Module 5: Ethical, Legal, and Societal Implications of GenAI

Topics Covered

AI must be used with responsibility, fairness, and professional judgment. Therefore, this section explores the ethical and legal boundaries of Generative AI.

AI hallucinations and misinformation
Plagiarism and content ownership concerns
Bias and fairness in LLMs
AI and job displacement
Global perspectives on AI regulation
Responsible AI frameworks, including OECD and EU AI Act references

Furthermore, learners understand why transparency, privacy, explainability, and ethical decision-making are important in AI-powered data science.

Module 6: GenAI in Everyday Productivity & Collaboration

Topics Covered

Generative AI can improve workplace productivity when used correctly. This section explains how professionals can use AI as a practical assistant for communication, documentation, and collaboration.

Writing, summarization, and content creation with AI
AI in Excel, PowerPoint, CRM, and documents
Workflow automation with Zapier and Make
AI for meetings, brainstorming, and reporting
Chatbots and virtual assistants

As a result, learners understand how AI can support everyday work before applying it to advanced data science and analytics tasks.

Module 7: Automating Data Handling with GenAI

Topics Covered

This section connects Generative AI with data preparation, preprocessing, augmentation, and workflow automation. Learners explore how AI can reduce manual effort and improve data handling efficiency.

AI-assisted data cleaning
Data preprocessing and augmentation with AI
Automated code generation for data pipelines
Natural language queries for databases
Structured and unstructured data workflow use cases

In addition, this section shows how AI can help data teams save time, reduce repetitive work, and prepare data more effectively for analysis.

Module 8: AI-Enhanced Analytics & Modeling

Topics Covered

Modern analytics requires stronger modeling, faster experimentation, and clearer interpretation. Therefore, this section focuses on how GenAI can support feature engineering, AutoML, and predictive modeling.

GenAI for feature engineering
AI-supported model selection
AutoML with GenAI-powered assistants
Natural language explanation of model results
Predictive modeling use cases with AI support

Moreover, learners understand how AI can make modeling workflows more efficient, explainable, and accessible for different business and research needs.

Module 9: Advanced AI Research & Applications

Topics Covered

Advanced data science increasingly depends on simulation, synthetic data, multimodal analytics, and responsible AI practices. This section explores how GenAI supports innovation in research and applied analytics.

AI for simulation and hypothesis generation
Synthetic datasets for research and analysis
Generative models in text, image, and multimodal analytics
AI-driven visualization and storytelling
Responsible AI in data science, including bias, interpretability, and explainability

Finally, learners understand how to use AI responsibly for advanced data science, research innovation, visualization, and data-driven storytelling.

WHO SHOULD ENROLL

The Generative AI in Data Science course is suitable for learners and professionals who want to understand how AI can improve data science, analytics, automation, modeling, and research workflows.

Data scientists, analysts, and machine learning engineers
AI and data professionals exploring automation and next-generation tools
Researchers and academics working on simulation or hypothesis generation
Students and early professionals aiming to specialize in AI-powered data science
Business analysts who want to use AI for data-driven decision-making

Therefore, this course is ideal for learners who want practical exposure to AI-powered analytics, automation, and applied data science skills.

WHAT YOU'LL GET

The project submission process helps learners apply data science and Generative AI concepts in a practical and structured way. Therefore, participants complete project work to demonstrate their understanding, originality, and professional application.

Choose Your Project Topic

First, explore the project topics available in the LMS. Review the project description, learning outcomes, and required skills before selecting your topic.

If required, ask your guide or mentor for support. After that, finalize your topic and begin your project work.

Submit Your Project

Once your project report is complete, follow the project submission guidelines for formatting, font, spacing, citations, and originality.

Your report will go through a plagiarism check. Therefore, keep the work original, properly structured, and clearly written.

Finally, upload your final PDF report and any supporting files to the LMS.

Project Evaluation

After submission, your project is evaluated by an organization mentor and an internal faculty supervisor.

The evaluation focuses on report quality, depth of understanding, concept application, problem-solving skills, professional behaviour, and timely submission. In some cases, a viva or presentation may also be required.

Credits, Certificates and Results

Credits may be awarded as per applicable UGC NEP 2020 guidelines. In addition, your internship certificate will be issued after report approval, evaluation completion, and uploading of the required organization certificate.

Thereafter, learners can download the internship completion certificate directly from the LMS.

PROJECT SUBMISSION

Choose Your Project Topic

First, explore the project topics available in the LMS. Review the project description, learning outcomes, and required skills before selecting your topic.

If required, ask your guide or mentor for support. After that, finalize your topic and begin your project work.

Submit Your Project

Once your project report is complete, follow the project submission guidelines for formatting, font, spacing, citations, and originality.

Your report will go through a plagiarism check. Therefore, keep the work original, properly structured, and clearly written.

Finally, upload your final PDF report and any supporting files to the LMS.

Project Evaluation

After submission, your project is evaluated by an organization mentor and an internal faculty supervisor.

Credits, Certificates and Results

Thereafter, learners can download the internship completion certificate directly from the LMS.

OUTCOME

After completing the Generative AI in Data Science course, learners will be ready to automate and optimize key data science processes using Generative AI.

In addition, participants will understand how AI supports faster data preparation, smarter modeling, workflow automation, synthetic data generation, explainability, and AI-driven storytelling.

Overall, this course prepares learners to become forward-thinking data professionals who can use AI responsibly for automation, research, analytics, and future-ready decision-making.

KNOW MORE ABOUT

Explore Internship Courses – Benefits and Offerings to understand how SkillGroom programs support practical learning, project submission, certification, and career-focused development.

Also, learners can browse related GenAI internship courses to build wider expertise in finance, marketing, HR, operations, data science, IoT, cybersecurity, and AI foundations.

Finally, choose the course that best matches your career goals and begin building practical skills for the future of data science and work.

INTERNSHIP COURSES - BENEFITS AND OFFERINGS

Course Price: ₹1950

Who Should Enroll

Data scientists, analysts, and ML engineers seeking to integrate Generative AI into workflows
AI and data professionals exploring automation and next-gen tools
Researchers and academics working on simulation or hypothesis generation
Students and early professionals aiming to specialize in AI-powered data science