Post Graduate Diploma in
Data Science For Logistics
Duration:
1 Year
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Program Type
Hybrid
Intake
Open
Eligibility Criteria:
Bachelor Degree in Any Stream
Language:
English
Degree Title:
Post Graduate Diploma
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With the rapid digital transformation of logistics, data-driven decision-making is reshaping supply chain management, demand forecasting, and operational efficiency. Recognizing this industry shift, the School of Skill Education (SSE), Tata Institute of Social Sciences (TISS), in collaboration with LSC and Redington - COLTE, introduces the Post Graduate Diploma in Data Science for Logistics.

The Post Graduate Diploma in Data Science for Logistics integrates data analytics, artificial intelligence, and machine learning to enhance logistics efficiency and decision-making. This program is tailored for individuals looking to master big data analytics, predictive modeling, and AI-driven logistics optimization. Through a combination of simulation-based training and real-world applications, students will gain the analytical skills required to reduce costs, improve supply chain efficiency, and mitigate risks. This diploma prepares graduates for roles in logistics analytics, digital supply chain management, and AI-powered logistics solutions.

Learning Outcomes

Enhance port and airport efficiency with real-time monitoring and predictive maintenance to reduce delays.
Optimize inventory, procurement, and warehouse management with data-driven methods for optimal stock levels and allocation.
Master machine learning concepts for demand forecasting and anomaly detection in logistics operations.
Utilise data analytics for project management to improve timelines and outcomes.
Apply advanced demand forecasting techniques to anticipate market trends and optimise resources.
Develop data-driven risk models for green supply chains to ensure
sustainability and resilience.
Explore blockchain applications for logistics to improve transparency and security.
Use IoT data for real-time monitoring and optimization, enhancing visibility and efficiency.
Analyse data to optimise revenue and distribution, maximising
profitability and market reach.
Curriculum
Semester 1
No. Course Hours Credits
01 Supply chain management 30 2
02 Data Analytics and Statistics 30 2
03 Inventory control 30 2
04 Fundamental of Logistics 30 2
05 Optimisation Technique 1 30 2
06 Plant location and Layout 30 2
07 Project 60 2
08 Skill Training 300 10
09 Domain Practicum 120 NC
Semester 2
No. Course Hours Credits
01 Supply Chain Applications of Block Chain 30 2
02 Concept and Applications of Internet of Things 30 2
03 Logistic Documentation 30 2
04 Advanced Logistics 30 2
05 Machine Learning concepts 60 4
06 Revenue and Distribution Management 30 2
07 Project 60 2
08 Skill Training 300 10
09 Domain Practicum 120 NC

Domain Practicum:

*Compulsory and Non-Credit, non-evaluative component

Skill Training:

*The Skill Training component is 50% to 60% ranging from 600 hours to 720 hours per year depending upon the industry requirement*

Job Prospects

Project Manager

Leads logistics projects using data-driven insights.

Quality Assurance Analyst

Utilises data analytics to ensure product quality and compliance with industry standards throughout the supply chain.

⁠Operations Analyst

Improves port and airport operations efficiency.

Compliance Manager

Ensures regulatory compliance and industry standards, using data analytics to monitor and enforce logistics processes.

Sustainability Specialist

Utilises data analytics to ensure product quality and compliance with industry standards throughout the supply chain.

Global Compliance Lead

Ensures compliance with International trade regulations, manages risks, and streamlines cross-border logistics using data analytics.

Blockchain Logistics Expert

Implements blockchain for transparency and security.

Forecasting Analyst

Uses predictive modeling to forecast demand, inventory, and transportation needs, optimizing resources and reducing stockouts.

IoT Analyst

OverMonitors and optimises logistics processes in real-time.

Vendor Relations Manager

Manages supplier relationships, analyzes performance data, negotiates contracts, and optimizes procurement for cost and efficiency.

Documentation Specialist

Streamlines logistic documentation processes.

CX Manager

Customer Experience Manager analyzes data to enhance satisfaction, loyalty, and optimize deliveries with personalized services.

Supply Chain Manager

Optimises supply chain networks and decision-making.

Data Analyst

Analyses transportation data and optimises supply chain efficiency.

Machine Learning Engineer

Develops AI solutions for logistics optimisation.

Revenue Manager

Analyse data to optimise revenue and distribution strategies.

Supply Chain Analyst

Optimises inventory management and procurement processes.