LUNARA LEARNING HUB

Season’s greetings! 😊

As the year comes to an end, thank you for being part of my community—whether you joined via LinkedIn, a webinar, or YouTube. I truly appreciate your support.

Wishing you a joyful Christmas, a peaceful holiday, and time to rest, reflect, and enjoy moments with loved ones. ✨

Please feel free to reach out to me anytime if you need support or just a conversation—I’m always happy to help.

If you need personalized guidance or want to deepen your skills, feel free to reach out for a one-on-one session. I’m available to support you.

Connect with me directly : calendar.app.google/r4mQ73DkxQ8W9PkR7

Looking forward to sharing more resources and insights with you in 2026.

Thank you for being here!

1 week ago (edited) | [YT] | 4

LUNARA LEARNING HUB

Bias in AI HR Tools: What Managers Must Know


For any hiring manager who has ever stared down a barrel of 500+ applications for a single open role, AI feels like magic. It promises to sort, rank, and surface the "perfect" candidates in seconds, saving thousands of hours of human labour.


We must understand that AI does not eliminate human bias; it often automates and amplifies it.


1. Most AI recruiting tools are built on machine learning models trained on historical data. If your company’s last ten years of hiring shows a pattern of promoting men from certain backgrounds into leadership, the AI will learn that pattern as "success." It will then scour resumes for proxies of that pattern on keywords, universities, career paths and systematically filter out divergent profiles.

If your company has historically favored candidates from specific universities, or subconsciously penalized gaps in employment (which disproportionately affects women), the AI will identify those patterns and codify them as "rules for success." The tool doesn't know why you hired those people; it just knows you did, and it wants to give you more of the same.


2. When a qualified candidate is mysteriously scored poorly, or when a certain demographic group consistently fails a video interview analysis, you may have no way to audit the decision. Delegating people’s decisions to an inscrutable algorithm is an acute ethical and legal liability. Would you accept a hiring manager who refused to explain their choices? This is a neck-to-break situation in many organizations now.


What Must Managers Do?

Become an Informed Skeptic.

• Ask Vendors the Hard Questions: "What specific steps did you take to identify and mitigate bias in your training data?" "Can you provide a bias audit from a third party, if possible?" "What is the demographic breakdown of the data this model was trained on?"


• Implement a "Human-in-the-Loop" (HITL) system. You can use AI for shortlisting, not final selection. occasionally analyze outcomes: Are certain groups being disproportionately filtered out at any stage? The future will be the ones smart enough to know when to stop the machine and ask: "Is this fair?


The future of work won't be built by AI. It will be built by leaders who know how to manage AI tools to improve systems. A tool is only as objective as the data it’s fed and the humans who built its parameters


#AIbais

1 week ago (edited) | [YT] | 4

LUNARA LEARNING HUB

In 2026, successful analysts won’t just “work with data” — they’ll collaborate with AI.


Data analysts were valued for their ability to extract, clean, and model data. In 2026, the real differentiator will be how well analysts think alongside AI.


AI is rapidly absorbing the mechanical layers of analytics: data preparation, pattern detection, anomaly surfacing, and even first-draft insights.


The analyst of the future is less a report generator and more a strategic thinker—someone who frames the right questions, validates AI outputs, and translates insights into decisions that leaders can trust.


The most valuable analysts in 2026 will be those who understand AI’s strengths and its blind spots in analytics—bias, overconfidence, and context blindness—and can responsibly guide organizations through them.


Decision literacy will be paramount—the ability to connect data, AI, and human judgment—will define impact.


It’s human with AI to build smarter systems.



#AIinAnalytics

2 weeks ago (edited) | [YT] | 6

LUNARA LEARNING HUB

Discover the top 10 AI tools every data analyst should master in 2026 — from ChatGPT and Power BI Copilot to Tableau GPT and DataRobot — to work smarter, automate faster, and deliver powerful insights.

1. ChatGPT (GPT-5.2)
Your all-purpose data assistant. Perfect for cleaning data, generating SQL queries, writing Python code, summarizing reports, and explaining complex concepts.

2. Claude AI
Built for deep reasoning and long documents — great for analyzing data reports, extracting insights, and writing executive summaries.

3. Microsoft Power BI (with Copilot)
The go-to business intelligence tool now powered by AI. Ask natural-language questions, automate reports, and generate predictive visuals in seconds.

4. Tableau GPT
Let Tableau’s AI handle visualization. You describe what you want; it designs dashboards and identifies trends automatically.

5. DataRobot
A leader in AutoML. Build and deploy machine learning models fast — no heavy coding needed.

6. IBM Watson Studio
For enterprise analysts working with large, complex data. Automates prep, model training, and deployment all in one workspace.

7. Polymer
A no-code AI platform that turns spreadsheets into interactive dashboards and insights in minutes. Perfect for business analysts.

8. Julius AI
An emerging AI assistant that interprets data patterns, finds correlations, and summarizes datasets instantly.

9. Powerdrill AI / Bloom
Simplifies exploratory data analysis — just upload your spreadsheet and ask questions in plain English.

10. KNIME Analytics Platform
An open-source favorite. Create visual data workflows with AI nodes for predictive analytics and automation.

2 weeks ago | [YT] | 6

LUNARA LEARNING HUB

Why Learning Analytics Should Guide Every Decision You Make

In training, education, and even personal skill growth — guessing is expensive. Learning analytics takes the emotion, assumptions, and "I think" moments off the table and replaces them with real evidence.

When we track learner progress, engagement patterns, assessment outcomes, and behavior inside a learning environment, we gain the power to:

🔍 Identify what’s working — and what’s not
⚙️ Improve content and delivery for better results
🌱 Personalize learning to each user’s pace and needs
📈 Predict performance and intervene early
💡 Make smarter, data-driven decisions instead of operating on instinct

Analytics leads to insight, clarity, and control.

If you want impact, growth, and measurable success…
start tracking, start analyzing, start deciding with data.

Let’s learn smarter. 🚀

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4 weeks ago | [YT] | 4

LUNARA LEARNING HUB

Which Excel formula do you use the MOST in your work?

1 month ago | [YT] | 4

LUNARA LEARNING HUB

Skill Check| Which Excel formula do you find trickiest?

1 month ago | [YT] | 2

LUNARA LEARNING HUB

✅ 9 Unique Excel Formulas

XLOOKUP – Modern lookup for exact/approximate matches, vertical or horizontal.

FILTER – Returns rows that meet specific conditions.

UNIQUE – Extracts distinct values from a range (great for HR lists).

TEXTSPLIT – Splits text into columns/rows using delimiters (super useful for emails → names).

LET – Assigns names to calculations to simplify long formulas.

SEQUENCE – Generates lists of numbers automatically.

TEXTJOIN – Combines text from multiple cells with a delimiter.

IFS – Multi-condition IF without nested chaos.

XMATCH – A modern alternative to MATCH (supports wildcard, exact/next match).

Watch full video here : www.youtube.com/playlist?list...

1 month ago | [YT] | 6

LUNARA LEARNING HUB

📌 Welcome to the Lunara Learning Hub

Hello everyone, and thank you for joining this community.
I’m pleased to create a dedicated space where we can learn, grow, and stay informed about the future of work.

Here, you’ll find clear, practical content focused on AI, analytics, and career development. My goal is to provide you with insights, tools, and guidance that support your professional growth in an ever-evolving landscape.

Expect structured tutorials, thoughtful explanations, and resources designed to help you make informed decisions and stay competitive in your field.

Thank you for being part of this community.
I look forward to supporting your journey and building a space where learning truly leads to leadership.

1 month ago | [YT] | 4

LUNARA LEARNING HUB

📊 TOP 10 HR METRICS EVERY HR SHOULD TRACK 🚀

Want to make smarter HR decisions and boost organizational performance?
Here are 10 essential HR metrics that every HR professional should monitor consistently 👇

1️⃣ Time to Fill – How long it takes to hire new talent
2️⃣ Turnover Rate – % of employees leaving the company
3️⃣ Cost per Hire – Total expense of bringing in a new employee
4️⃣ Absenteeism Rate – % of workdays employees are absent
5️⃣ Employee Engagement – Level of commitment and enthusiasm
6️⃣ Training ROI – Return on investment for learning programs
7️⃣ Diversity Ratio – Representation across gender, age, etc.
8️⃣ Performance Ratings – Evaluation of job performance
9️⃣ Promotion Rate – % of employees advancing internally
🔟 Retention Rate – How well you keep top talent

✅ Tracking these metrics helps HR teams drive productivity, engagement, and growth across the organization.

🎯 #HRAnalytics #PeopleAnalytics #HumanResources #DataDrivenHR #HRMetrics #BusinessGrowth #WorkforcePlanning

2 months ago | [YT] | 2