Description
Certification Name: Certificate in Logistics Data Analyst
Course Id: CLDA/Q0001.
Eligibility: Graduation or Equivalent.
Objective: The Certified Logistics Data Analyst course is designed to equip participants with the analytical and technical skills needed to manage and interpret large volumes of logistics and supply chain data. The course covers key topics such as data collection methods, KPIs in logistics (e.g., order accuracy, delivery times, inventory turnover), data visualization, dashboard creation, and the use of tools like Excel, SQL, Power BI, and Python.
Duration: Three Month.
How to Enroll and Get Certified in Your Chosen Course:
Step 1: Choose the course you wish to get certified in.
Step 2: Click on the “Enroll Now” button.
Step 3: Proceed with the enrollment process.
Step 4: Enter your billing details and continue to course fee payment.
Step 5: You will be redirected to the payment gateway. Pay the course and exam fee using one of the following methods:
Debit/Credit Card, Wallet, Paytm, Net Banking, UPI, or Google Pay.
Step 6: After successful payment, you will receive your study material login ID and password via email within 48 hours of fee payment.
Step 7: Once you complete the course, take the online examination.
Step 8: Upon passing the examination, you will receive:
• A soft copy (scanned) of your certificate via email within 7 days of examination.
• A hard copy (original with official seal and signature) sent to your address within 45 day of declaration of result.
Step 9: After certification, you will be offered job opportunities aligned with your area of interest.
Online Examination Detail:
Duration- 60 minutes.
No. of Questions- 30. (Multiple Choice Questions).
Maximum Marks- 100, Passing Marks- 40%.
There is no negative marking in this module.
| Marking System: | ||||||
| S.No. | No. of Questions | Marks Each Question | Total Marks | |||
| 1 | 10 | 5 | 50 | |||
| 2 | 5 | 4 | 20 | |||
| 3 | 5 | 3 | 15 | |||
| 4 | 5 | 2 | 10 | |||
| 5 | 5 | 1 | 5 | |||
| 30 | 100 | |||||
| How Students will be Graded: | ||||||
| S.No. | Marks | Grade | ||||
| 1 | 91-100 | O (Outstanding) | ||||
| 2 | 81-90 | A+ (Excellent) | ||||
| 3 | 71-80 | A (Very Good) | ||||
| 4 | 61-70 | B (Good) | ||||
| 5 | 51-60 | C (Average) | ||||
| 6 | 40-50 | P (Pass) | ||||
| 7 | 0-40 | F (Fail) | ||||
Key Benefits of Certification- Earning a professional certification not only validates your skills but also enhances your employability. Here are the major benefits you gain:
Practical, Job-Ready Skills – Our certifications are designed to equip you with real-world, hands-on skills that match current industry demands — helping you become employment-ready from day one.
Lifetime Validity – Your certification is valid for a lifetime — no renewals or expirations. It serves as a permanent proof of your skills and training.
Lifetime Certificate Verification – Employers and institutions can verify your certification anytime through a secure and reliable verification system — adding credibility to your qualifications.
Industry-Aligned Certification –All certifications are developed in consultation with industry experts to ensure that what you learn is current, relevant, and aligned with market needs.
Preferred by Employers – Candidates from ISO-certified institutes are often prioritized by recruiters due to their exposure to standardized, high-quality training.
Free Job Assistance Based on Your Career Interests – Receive personalized job assistance and career guidance in your preferred domain, helping you land the right role faster.
Assessment Modules:
Module 1: Fundamentals of Logistics and Supply Chain Analytics: Introduction to logistics functions and data-driven decision-making, Key concepts in supply chain operations and data types, Role of a logistics data analyst in modern supply chains, Understanding data flows across procurement, warehousing, and distribution, Basics of KPIs and performance metrics in logistics, Overview of digital transformation and analytics in logistics.
Module 2: Data Collection, Cleaning, and Management Techniques: Data sources in logistics: ERP, WMS, TMS, RFID, GPS, EDI, Techniques for data extraction and integration across platforms, Data cleaning, validation, and preparation for analysis, Master data management and data governance principles, Structured vs unstructured logistics data handling, Tools used: Excel, SQL, Power Query, and data connectors.
Module 3: Descriptive and Diagnostic Analytics in Logistics: Applying statistical techniques to analyze logistics performance, Inventory levels, order cycle time, and transportation cost analysis, Root cause analysis of logistics problems using data, Dashboard creation using Excel/Power BI/Tableau for operational insights, Analyzing trends, outliers, and historical data for process improvement, Case study: analyzing warehouse efficiency using data.
Module 4: Predictive and Prescriptive Analytics in Logistics: Forecasting techniques for demand and shipment planning, Regression analysis and time series models in logistics forecasting, Optimization models for inventory and route planning, Scenario analysis and decision support systems, Introduction to simulation and what-if analysis in logistics, Applications of machine learning in logistics (introductory level).
Module 5: Logistics KPIs, Reporting, and Visualization: Key logistics KPIs: OTIF, fill rate, freight cost per unit, inventory turnover, Building dynamic dashboards for logistics performance tracking, Automating reports and alerts for real-time decision-making, Storytelling with data: designing visuals for logistics insights, Tools: Power BI, Tableau, Excel dashboards for logistics reporting, Communicating data-driven insights to logistics stakeholders.
Module 6: Real-World Applications, Risk Analysis, and Future Trends: Analyzing logistics disruptions and risk mitigation using data, Data-driven vendor and carrier performance evaluation, Using analytics in reverse logistics and returns management, Integration of IoT, AI, and blockchain in logistics analytics, Ethical considerations and data privacy in logistics analytics, Capstone project: data-driven improvement plan for logistics operation.
