Datacom Automation AI Accelerator: From Co-pilot to Autonomous Agent

Why complete this Job Simulation

A risk-free way to experience work on the job with us at Datacom. Practice your skills with example tasks and build your confidence to ace your applications.

Self-paced

4-5 hours

No grades

No assessments

Intermediate

Welcome to the Datacom Automation AI Accelerator! In this interactive job simulation, you will step into the role of a Graduate Developer assigned to a high-impact innovation team at Datacom. Your mission is to tackle a project that mirrors the real-world evolution of software development—a journey that will transform your relationship with technology. You won't just be writing code; you'll be partnering with intelligent AI systems to solve problems faster, smarter, and more strategically than ever before. This experience is designed to be a 'taster' that provides practical, real-world experience with the methodologies shaping our industry.

At Datacom, our purpose is to help customers and communities navigate the complexity of an ever-changing world by connecting people and technology. A core part of that mission is recognising that AI is not just a tool for code completion, but a fundamental collaborator in the entire development lifecycle. The most valuable developers of tomorrow will be those who can fluidly switch between different modes of AI partnership. We know that those entering their careers now are already more productive and efficient thanks to AI; this programme is your training ground for channelling that fluency into strategic impact.

The curriculum is structured to guide you up a ladder of increasing abstraction and leadership. You will begin by learning to be a hands-on 'AI Pilot,' using conversational AI IDEs for rapid, interactive debugging and creative coding. Next, you will transition to the role of an 'AI Architect,' where you will use spec-driven development environments to translate high-level business needs into precise, machine-executable specifications, letting an AI agent handle the complex implementation. Finally, your journey will culminate in thinking like a 'Business Strategist,' where you will design an autonomous AI agent capable of analysing and solving a real business challenge from end to end. This is your opportunity to move beyond simply using AI and to start leading with it, gaining the confidence to solve real problems and shape the future of technology.

How it works

Complete tasks guided by pre-recorded videos and example answers from our team at Datacom. No live sessions, all self-paced.
Earn a certificate and add it to your resume and LinkedIn as an extra curricular activity.
Stand out in applications. Confidently answer interview questions and explain why you’re a good fit for our team.

Skills you will learn and practice:

Critical Thinking

Data Visualization

Problem Solving

Process Improvement

Requirements Analysis

Introduction from Datacom

Tasks

Task One: AI-Powered Debugging and Refactoring

1-2 hoursIntermediate

Learn what process automation is in order to meet your client's needs

What you'll learn

How to effectively leverage an AI co-pilot to rapidly understand the function and structure of unfamiliar or legacy code.
The art and science of crafting effective, context-rich prompts specifically for debugging and refactoring tasks.
How to work iteratively with an AI, treating its suggestions as hypotheses to be tested and refined, rather than as infallible commands.
The critical importance of maintaining human oversight, applying critical thinking, and verifying all AI-generated code fixes before acceptance.

What you'll do

The participant will be provided with a buggy and inefficient Python script designed for data processing. They will use a conversational AI IDE to diagnose the issues, write unit tests to confirm the bugs, refactor the code for clarity and performance, and document their interactive process with the AI.

Deliverable: A file submission containing three distinct artifacts:
1. The final, corrected, and refactored Python script, named process_data_final.py.
2. A detailed markdown file named DEBUG_LOG.md, which documents their interaction with the AI. This log should include their key prompts, the corresponding AI responses, and brief annotations on their thought process.
3. A Python file named TEST_CASES.py containing the unit tests they authored with the AI's assistance to identify the original bug and verify the final fix.

Task One: AI-Powered Debugging and Refactoring

1-2 hoursIntermediate

Learn what process automation is in order to meet your client's needs

What you'll learn

How to effectively leverage an AI co-pilot to rapidly understand the function and structure of unfamiliar or legacy code.
The art and science of crafting effective, context-rich prompts specifically for debugging and refactoring tasks.
How to work iteratively with an AI, treating its suggestions as hypotheses to be tested and refined, rather than as infallible commands.
The critical importance of maintaining human oversight, applying critical thinking, and verifying all AI-generated code fixes before acceptance.

What you'll do

The participant will be provided with a buggy and inefficient Python script designed for data processing. They will use a conversational AI IDE to diagnose the issues, write unit tests to confirm the bugs, refactor the code for clarity and performance, and document their interactive process with the AI.

Deliverable: A file submission containing three distinct artifacts:
1. The final, corrected, and refactored Python script, named process_data_final.py.
2. A detailed markdown file named DEBUG_LOG.md, which documents their interaction with the AI. This log should include their key prompts, the corresponding AI responses, and brief annotations on their thought process.
3. A Python file named TEST_CASES.py containing the unit tests they authored with the AI's assistance to identify the original bug and verify the final fix.

Reviews

4.5

I liked that the simulation was based on a realistic business problem and required practical AI automation thinking. It allowed me to analyze a manual process, Analyze and Debug a code, and build real world app. I also appreciated the focus on a low-risk, read-only Proof of Concept, as it demonstrated how AI can be introduced safely into a real business environment

Student, Kaduna State University

I liked how each task used a different muscle: Task 1 was forensic debugging (chasing a bug that turned out to be impossible, which led me to the real one), Task 2 was architecture under ambiguity, and Task 3 was pure communication — no code, just making a case. That progression from fixing to designing to pitching was the most interesting part.

Student, University of Hertfordshire

I enjoyed the hands-on tasks and realistic workplace scenarios. The simulation helped me improve my understanding of software development, debugging, AI-assisted development, and business process automation. It provided practical experience that closely reflects real-world projects and strengthened my confidence in applying these skills.

Student