INTRODUCTION TO DATA ANALYTICS & CYBERSECURITY

EMC is a unique Canadian not-for-profit organization dedicated to helping manufacturers grow and become more competitive. Our education, training and development programs build excellence at all levels and every stage of a career.

EMC est un organisme canadien sans but lucratif unique en son genre, qui se consacre à aider les fabricants à croître et à devenir plus concurrentiels. Nos programmes d’éducation, de formation et de perfectionnement favorisent l’excellence à tous les niveaux et à chaque étape d’une carrière.

The learner successfully completes the Session 4 final assessment, including both written practical work and an oral explanation. The instructor considers the combined evidence against the course performance objectives and awards a Pass when it demonstrates foundational competence: accurate record handling, evidence-based interpretation and decisions, clear communication of findings, and safe handling of workplace data, with little to no error and little to no supervision. Passing Session 4 means passing the course.

Course Content

• Production records: spreadsheet layout, consistent units, traceability, and correction of missing, duplicate or impossible values.
• Production and quality reports: defect summaries, trends, normal variation, and signals that warrant monitoring or escalation.
• Supervisor-ready summaries: checking findings against source data and explaining what happened, why it matters and what happens next.
• Secure data handling: Green, Yellow and Red classification, approved destinations, and appropriate boundaries for external AI tools.
• Workplace data scenarios: recognizing suspicious requests and choosing when to refuse, verify, report or escalate.
• AI-supported learning: Gemini Notebook is introduced in Session 1 and used throughout the course to support learning and review of course material.
• Integrated practice and assessment: Sessions 1–3 prepare learners to apply these skills in the written practical task and oral explanation in Session 4.

Learning Outcomes

Record and Organize Production Data

• Enter production data into a spreadsheet with sound layout and consistent units.
• Maintain clear attribution and traceability when entering or correcting production records.
• Find and fix duplicates, missing values, and impossible values.

Read and Act on Production and Quality Reports

• Explain in plain language what a defect summary, trend chart, or SPC (statistical process control) chart shows.
• Distinguish normal variation from a signal that needs attention.
• Decide whether to leave it, keep watching, or escalate — and to whom.

Turn Raw Data into Supervisor-Ready Summaries

• Consolidate a small raw dataset into a chart or one-page summary.
• Verify spreadsheet-suggested insights against the data before using them.
• State findings as decisions: what happened, why it matters, what next.
• Prepare a data summary that supports a Lean improvement decision.

Handle Production Data Securely

• Classify data before it moves into three sensitivity tiers — Green, Yellow, Red — using a simple traffic-light scheme built for the plant.
• Decide where data may travel and what never leaves the plant.
• Answer “can I just dump this into ChatGPT?” correctly for a given dataset.
• Know what data is safe to use with external AI (e.g., ChatGPT).

Respond to Workplace Data Scenarios

• Choose and justify a response: report, refuse, verify, or escalate.
• Recognize suspicious data requests or attachments dressed as routine work.
• Trace one piece of data end to end: recorded, read, summarized, secured.

Minimum requirements

• Complete the written practical component of the final assessment. Practical evidence must be attributable to the learner; any instructor-accepted alternative or directly observed work must be documented.
• Complete the oral component of the final assessment, explaining and justifying the record correction, report decision and safe workplace response.
• Meet the qualitative passing criteria through the combined written practical and oral evidence. A missing required component leaves the assessment incomplete.

Estimated effort time

12 hours

EMC updates learning content on a regular basis. Version: 2026-1