Designed for Leaders, Teams and Individual Professionals
Fractional CDO transforms your systems and strategy. Training and Enablement transforms your people. We help turn data into everyday decision-making habits for Leaders, Teams & contributors, and Analysts & data scientists.
Book a Discovery CallPerfect for:
Owners, directors, senior managers, and emerging leaders who review reports, sit in KPI meetings, or are being asked to “use AI more” in their teams.
What you'll learn:
- Data-literate leadership: how to speak the language of data and ask sharper questions.
- How to define clear KPIs that reflect strategy (not vanity metrics).
- How to read dashboards with confidence and spot red flags or noise quickly.
- Simple decision frameworks that combine data + judgment in real-world scenarios.
- Where AI can realistically support leadership (summaries, scenarios, decision support) and where human judgment must stay in charge.
- How to set basic guardrails for AI use in your team: transparency, privacy, and accountability.
By the end, you'll be able to:
- Align your team around a small, disciplined KPI set tied to outcomes.
- Run meetings where dashboards, reports, and AI summaries clarify decisions instead of confusing them.
- Push back on unclear metrics or overconfident AI outputs—and demand clarity, not just more charts or hype.
Perfect for:
Operations teams, Analysts, PMs, finance/HR teams, and cross-functional staff.
What you'll learn:
- How to design a simple, repeatable data workflow from raw data to report.
- How to use tools like Excel, Power BI, and Python to cut manual work.
- How to design clear, trustworthy dashboards people use.
- Where AI tools can realistically help (and where they shouldn’t be used).
- Basic AI guardrails: privacy, bias, security, and accountability.
By the end, you'll be able to:
- Turn recurring reports into semi-automated or fully automated workflows.
- Build dashboards that answer “So what?” for your stakeholders.
- Use AI as a reliable assistant, not a risky shortcut.
Perfect for:
Analysts-in-training, independent consultants, MBAs, and career shifters.
What you'll learn:
- The core analytics mindset: from business question → data → insight.
- Essential tools: spreadsheets, basic SQL, BI tools, and AI assistants.
- How to turn analysis into simple, visual stories for clients or managers.
- How to use AI responsibly to draft, summarize, and explore scenarios.
- How to structure a portfolio project that showcases your skills.
By the end, you'll be able to:
- Complete a portfolio-ready analytics project using your own or sample data.
- Talk through your process in a way that builds credibility with employers.
- Show how you use AI transparently and ethically in your workflow.
Perfect for:
Junior analysts, BI/reporting roles, and professionals transitioning into analytics.
What you'll learn:
- How to frame business questions so your analysis matters.
- Data foundations: cleaning and transforming data in Excel/Sheets & SQL.
- Exploratory analysis and visualization in Power BI or Tableau.
- Core statistics every analyst needs (trends, variability, correlations, A/B basics).
- How to design reusable dashboards and reports for stakeholders.
- How to present findings with clear recommendations, not just charts.
By the end, you'll be able to:
- Take a messy dataset and turn it into a clean, structured model.
- Build 2–3 dashboards or reports that answer real business questions.
- Confidently walk stakeholders through your analysis and “so what”.
Perfect for:
Aspiring data scientists, strong analysts moving into DS, and technical professionals adding ML to their toolkit.
What you'll learn:
- How to turn a business challenge into a data science problem.
- Foundations of supervised & unsupervised learning (regression, classification, clustering).
- Feature engineering, model evaluation, and validation in practice.
- Basics of experimentation and A/B testing for product and process changes.
- How models get deployed: APIs, monitoring, and feedback loops (at a practical level).
- Responsible AI: bias, fairness, explainability, and documentation.
- How to explain models to executives and non-technical partners.
By the end, you'll be able to:
- Deliver an end-to-end data science case study or prototype.
- Evaluate models using appropriate metrics and explain why they matter.
- Communicate trade-offs (accuracy vs. simplicity, speed vs. robustness) in plain language.
Perfect for:
Everyboby
What you'll learn:
- What “data literacy” really means and how data flows through your work.
- The data-to-decision journey (from question → data → insight → action).
- How to spot data quality issues and avoid “Garbage In, Garbage Out.”
- Basics of working with data in everyday tools (spreadsheets & dashboards).
- Simple data storytelling & visualization principles.
- Foundations of data ethics, privacy, and responsible AI use.
By the end, you'll be able to:
- Read and challenge reports and charts with confidence.
- Ask sharper questions that lead to better analysis and clearer answers.
- Recognize misleading visuals and weak data before they drive decisions.
- Understand where AI and algorithms show up in your work—and when to be cautious.
- Contribute to a stronger data culture, where decisions are clearer, faster, and more transparent.