Data Automation

Build scripts to process and automate pipelines. Show skills in Python, Node, and data efficiency. Drive enterprise workflows with intelligent automation logic.

Robust Scripting

Write clean, modular, and fault-tolerant scripts. Deal with error handling, API rate limits, and unstructured datasets efficiently.

Pipeline Engineering

Design logical workflows that transform raw inputs into structured outputs quickly, reducing manual labor processing times drastically.

Scaling & Optimization

It's not just about getting it to work once; it's about processing 1,000,000 rows as efficiently as 100 rows. Optimize your logic.

Final Submission (Mandatory Outputs)

Participants must submit the following four components, compressed into a single .zip file. Non-compliance with formatting requirements will result in point deductions.

A. Pipeline Output (.csv File)

The final output generated by the automation script execution. Filename: Daily_Action_Report.csv. Mandatory Format (2 Columns Only):

Item_ID: Unique ID for raw materials/inventory.

Action_Status: The final inventory status. Must be populated with one of four options:

- Safe: Stock levels are sufficient for operations.

- Restock: Stock has reached the minimum threshold; reordering is required.

- Invalid Data: Raw data row is corrupted, incorrectly formatted, or the Item_ID is unregistered.

- Anomaly: A logical discrepancy exists between sales logs and physical warehouse stock.

B. Executable Code (.py or .ipynb)

The source code script that executes the entire data processing workflow (Zero Human Intervention). Mandatory Requirement: The code must include Code Comments explaining logic for:

Data Ingestion: The process of retrieving and reading raw data.

Data Cleansing: Logic for handling corrupted, incorrectly formatted, or missing data.

Calculation: Mathematical formulas for calculating stock inflows and outflows.

Anomaly Logic: Business rules and logic used to detect inventory mismatches.

C. System Analysis & Design Document (.pdf)

A technical document justifying the system architecture design. It must include the following four points:

Problem Analysis: Identification of current MSME operational weaknesses.

System Solution: A narrative explanation of the logic and business rules utilized within the script.

System Requirements: Specification of Functional and Non-Functional Requirements.

D. Execution Instructions (README)

A brief guide for the judging panel to conduct testing and run the automation script on the committee's computer. Format: Plain text (Markdown or TXT). Content:

Dependencies/Prerequisites: A list of libraries or packages that must be installed (e.g., pandas, numpy, or requirements.txt).

Environment: Specifications of the programming language version or local database used (e.g., Python 3.10, MySQL 8.0).

Execution Steps: A clear sequence of commands to run the script until it generates the Daily_Action_Report.csv file.