Statistical Thinking for Business – with JASP and Python

No formal prerequisites. The training is self-contained and progressively structured to support learners from diverse backgrounds. No prior coding experience required - Python is acquired naturally, much like one’s mother tongue, through guided exposure, repetition, use, and AI support.

• Apply Your Science’s internship-style, AI-assisted learning model to solve industrial challenges using JASP, Python, and ChatGPT.
• Perform core statistical tasks — data cleaning, variability analysis, boxplot interpretation, and confidence interval estimation for means, standard deviations, and proportions — on both no-code and code-based platforms.
• Interpret sampling uncertainty and statistical output in high-stakes manufacturing contexts, including aerospace and pharmaceutical quality control.
• Design structured data analysis workflows that integrate AI-assisted reasoning, automated procedures, and domain-specific context to support industrial decision-making.
• Generate professional, AI-supported reports that synthesize statistical findings with process insights and professional interpretation.
• Detect process instabilities and root causes using outlier typologies, fault tree analysis, and capability indices.
• Integrate statistical inference with modern AI tools, adopting structured learning and problem-solving strategies suited to the data-driven, AI-enabled workplace.

This course takes learners on a structured journey from the foundations of data-driven decision-making to advanced applications in industrial contexts. It begins with an overview of Your Science | Scientific Consulting’s services and our internship-style, AI-enhanced approach to education. Participants are then introduced to the most effective tools — from ChatGPT and JASP to R and Python — and guided step by step through tutorials, case studies, and practical analyses.

Core modules cover statistical fundamentals such as variability, boxplots, and confidence intervals, demonstrated in both JASP and Python. Hands-on lab activities apply these methods to real-world problems, including pharmaceutical quality assurance and aerospace manufacturing. Along the way, learners benefit from auto-graded quizzes, structured reporting exercises, and AI-supported insights that mirror professional practice.

The course concludes with advanced industrial framing techniques — from outlier typologies and root cause analysis to process monitoring and benchmarking — before culminating in a final assessment and a clear statement of learning outcomes.

• Your Science | Scientific Consulting: Our Services at a Glance
• Robust, Industry-Relevant, AI-Enhanced Education
• Optimal Tools: AI Assistants, No-Code Solutions, and Code Platforms
  • ChatGPT and Siblings
  • JASP and jamovi
  • R and Python
• JASP
  • Installing JASP
  • Sampling Uncertainty in Practice: A JASP Tutorial
    • Scenario: Defect Rates in Shift-Based Manufacturing
    • Generating the Dataset for JASP Analysis Using Python
    • Analyzing the Generated Dataset using JASP
  • Distribution, Central Tendency, and Variability
  • Boxplots
  • Interpreting JASP Output and Introducing Confidence Intervals
• Python
  • Installing and Running Python
    • Running Python in the Cloud via Google Colab
    • Running Python Locally from Command Prompt
    • Running Python Locally from JupyterLab
  • The Good News: Learning Python with ChatGPT
  • Sampling Uncertainty in Practice: A Python Tutorial
• Rethinking Education in the Era of AI and Chatbots
• Auto-Graded Quiz in Your Private Google Colab Environment
• Self-Paced Lab Activity in Secured Colab Workspace: Ensuring Drug Quality – Estimating Active Ingredient Variability
• Lab: Bootstrap Proportion CIs in Aerospace
  • Bootstrap CI for the Mean Cycle Time – JASP & Python
  • Prompting Your AI Assistant: Best Practices Checklist
  • CI for the Proportion of Efficient Cycles – JASP
    • Approximate Confidence Interval for a Proportion
    • Exact Confidence Interval for a Proportion
• Summary: CIs, Conditions & Distributions
• Automated Reporting
  • Analyst’s Observations and Interpretation
    • Data Cleaning
    • Key Findings
  • AI’s Observations and Report
• Industrial Framing: Connecting Data to Process Reality
  • Outlier Typology in Manufacturing Contexts
  • Root Cause Techniques: 5 Whys, Ishikawa, and Fault Tree Analysis
  • Monitoring Process Stability: Insights from EWMA Charts
  • Benchmark Comparison: Predecessor Model & Manufacturer Specs
• Final Auto-Graded Quiz
• Learning Outcomes

We get you job-ready with factory-floor training - more internship than lecture hall. We teach you to ask the RIGHT questions to your 24/7 AI assistant, support you with book-grade notes, and help you upgrade how and what you learn for the AI era.

Assessment will be carried out through quizzes administered in Google Colab.

Participants receive a Certificate of Completion. Those who pass all quizzes earn a Certificate of Achievement. Each certificate is digitally signed and features a QR code for verification as a document issued by Your Science | Training Services.

The training is delivered fully online in a self-paced format, accessible Europe-wide. All activities run securely in Google Cloud and Google Colab, with no local installation required. Token-protected access ensures GDPR compliance, while quizzes and certification are automated to provide a seamless learning experience.

Learners enjoy continuous access to all course materials, including 80 pages of book-grade lecture notes, an interactive lab, and quizzes. Certificates are digitally signed and QR-verifiable for authenticity. The program typically requires 2–3 days (around 24 hours) to complete, depending on individual pace and depth of engagement. Designed for professionals and SMEs, it offers scalable, industry-relevant training aligned with modern AI-enabled workflows.