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DSTC-00023 Online (e-LMS) Graduate / Intermediate

AI in Research

by - DSTC

Use AI to accelerate every stage of the research process.

★★★★★ Be the first to review 4 Weeks · 40 hrs e-Certificate Included
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From ₹2,500 + GST

Programme Parameters

Educational Level:
Graduate / Intermediate
Duration & Workload:
4 Weeks (40 Hrs)
Delivery Mode:
Online (e-LMS)
Prerequisites:
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.

About This Course

AI in Research is a practical guide to using artificial intelligence as a research accelerator across disciplines. You learn where AI genuinely helps the research lifecycle: searching and synthesising literature, analysing and visualising data, generating and testing hypotheses, and drafting and refining scientific writing. Equally important, the course confronts the risks head-on — hallucinated citations, reproducibility, bias and the ethics of AI-assisted authorship — so you use these tools with rigour and integrity. You finish able to integrate AI into your research workflow responsibly and productively. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.

🎯 Program Aim

This course shows how AI accelerates academic research — literature review, data analysis, experiment design and scientific writing — with a strong focus on rigour and integrity.

📋 Course Objectives

1. Use AI to search and synthesise literature.
2. Apply AI to data analysis and visualisation.
3. Support hypothesis generation and experiment design.
4. Draft and refine scientific writing with AI.
5. Uphold rigour, reproducibility and research ethics.

👥 Who Should Enroll?

• Researchers and academics across disciplines
• PhD scholars and postgraduate students
• Research-support and library professionals
• Anyone conducting rigorous investigation

🚀 Key Learning Outcomes

• A responsible, productive AI research workflow.
• Time saved across the research lifecycle.
• A clear view of AI’s risks in scholarship.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.

💎 What You'll Gain

🎥

Live & Recorded Sessions

Lifetime access to class recordings
🎓

e-Certificate on Completion

Cryptographically verified credential
💬

Post-Programme Support

Direct access to mentors & council
💻

Hands-On Experience

Notebooks, real-world code & datasets

Curriculum Outline

Module 1 Outline

AI Fundamentals, Mathematics, and Ai In Research Foundations

Implement ai-in-research with faculty-development for practical ai fundamentals, mathematics, and ai in research foundations applications and outcomes. • Design higher-education with research-design for practical ai fundamentals, mathematics, and ai in research foundations applications and outcomes. • Analyze ai-in-research with faculty-development for practical ai fundamentals, mathematics, and ai in research foundations applications and outcomes.

Module 2 Outline

Data Engineering, Preprocessing, and Feature Pipelines

Implement ai-in-research with faculty-development for practical data engineering, preprocessing, and feature pipelines applications and outcomes. • Design higher-education with research-design for practical data engineering, preprocessing, and feature pipelines applications and outcomes. • Analyze ai-in-research with faculty-development for practical data engineering, preprocessing, and feature pipelines applications and outcomes.

Module 3 Outline

Model Architecture, Algorithm Design, and Ai In Research Methods

Implement ai-in-research with faculty-development for practical model architecture, algorithm design, and ai in research methods applications and outcomes. • Design higher-education with research-design for practical model architecture, algorithm design, and ai in research methods applications and outcomes. • Analyze ai-in-research with faculty-development for practical model architecture, algorithm design, and ai in research methods applications and outcomes.

Module 4 Outline

Training, Hyperparameter Optimization, and Evaluation

Implement ai-in-research with faculty-development for practical training, hyperparameter optimization, and evaluation applications and outcomes. Gain hands-on experience and produce real-world projects. • Design higher-education with research-design for practical training, hyperparameter optimization, and evaluation applications and outcomes. Gain hands-on experience and produce real-world projects. • Analyze ai-in-research with faculty-development for practical training, hyperparameter optimization, and evaluation applications and outcomes. Gain hands-on experience and produce real-world projects.

Module 5 Outline

Deployment, MLOps, and Production Workflows

Implement ai-in-research with faculty-development for practical deployment, mlops, and production workflows applications and outcomes. Gain hands-on experience and produce real-world projects. • Design higher-education with research-design for practical deployment, mlops, and production workflows applications and outcomes. Gain hands-on experience and produce real-world projects. • Analyze ai-in-research with faculty-development for practical deployment, mlops, and production workflows applications and outcomes. Gain hands-on experience and produce real-world projects.

Module 6 Outline

Ethics, Bias Mitigation, and Responsible AI Practices

Implement ai-in-research with faculty-development for practical ethics, bias mitigation, and responsible ai practices applications and outcomes. • Design higher-education with research-design for practical ethics, bias mitigation, and responsible ai practices applications and outcomes. • Analyze ai-in-research with faculty-development for practical ethics, bias mitigation, and responsible ai practices applications and outcomes.

Module 7 Outline

Industry Integration, Business Applications, and Case Studies

Implement ai-in-research with faculty-development for practical industry integration, business applications, and case studies applications and outcomes. • Design higher-education with research-design for practical industry integration, business applications, and case studies applications and outcomes. • Analyze ai-in-research with faculty-development for practical industry integration, business applications, and case studies applications and outcomes.

Technical Specifications

ParameterRequirement
Covered Tool / Platformai-in-research
Covered Tool / Platformhigher-education
Covered Tool / Platformresearch-design

Frequently Asked Questions

This is an Online (e-LMS) course delivered via our e-LMS platform. You will have access to pre-recorded video lectures, reading materials, assignments, quizzes, and hands-on projects that you can complete at your own pace.

Yes! Upon successful completion of all modules, assignments, and assessments, you will receive an e-Certification along with an e-Marksheet from DSTC (DSTC) that you can showcase on your CV and LinkedIn profile.

Learners should have a foundational understanding of Science & Technology concepts. Familiarity with basic tools and programming is recommended.

You will have access to all course materials for the duration of 4-6 Weeks. The self-paced format allows you to learn according to your own schedule through our online learning management system.

Yes, dedicated mentor support is available throughout the course. You can reach out for doubt-clearing sessions, project guidance, and career advice related to Science & Technology. Our mentors are industry experts and experienced professionals. Enroll in AI in Research today and take the next step in your professional journey. With expert-curated content, practical projects, and industry-recognized certification, this course is your gateway to mastering Science & Technology skills that matter.

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