AI and Machine Learning for BI, Automation, and Data Analytics

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.

This program equips participants with the skills to design, automate, and deploy data-driven workflows across industrial and business environments, using Python and AI to support decision-making, compliance, and optimization strategies.

Participants learn to handle the full data lifecycle - from structured acquisition and cleaning to machine learning model deployment - with a focus on industrial systems, privacy compliance, and real-time analysis using practical scenario-driven code.

Chapter I

Getting Started with Python - Core Python Concepts including Syntax, Architecture, and AI-Assistance

I.1 Training Presentation 1
I.2 Python Environments 2
I.3 Python Hierarchy and Python Code Structure 15
I.3.1 Body Mass Index and Linear Regression 15
I.3.2 Python Building Blocks and Code Structure 17
I.3.2.1 Python’s Components 17
I.3.2.2 Python Code Architecture 24
I.3.3 The Good News: Learning Python with ChatGPT 32
I.3.4 Self-Paced Activity: Descriptive Statistics, Non-Linear Regression, and Google Drive Integration 35
I.4 Learning Outcomes 42

Chapter II

Data Collection in an Industrial Setting - SQL and API Pipelines for Automated Access to Databases and CRM Systems

II.1 Python in the Industrial Data Ecosystem 43
II.2 Structured Query Language 48
II.2.1 Relational Databases 48
II.2.2 SQL Databases and SQL Database Management Systems 49
II.2.3 SQL Editors and Programmatic Access 49
II.2.4 Exercise: Exploring the Chinook Sample Database 51
II.3 Automated Data Collection and Analysis 55
II.3.1 Step 1: Integration of production data from an SQL DB 55
II.3.1.1 Workflow with an SQL DBMS 55
II.3.1.2 Python and SQL code 57
II.3.1.3 Code Output 61
II.3.2 Step 2: Customer Data Retrieval from a CRM system 62
II.3.2.1 Workflow with an API 62
II.3.2.2 Flask Web Framework 63
II.3.2.3 Step 2a: Setting Up a Local Flask API with Authentication 64
II.3.2.4 Step 2b: Requesting Data from the Flask API 70
II.3.3 Step 3: Correlation Analysis 74
II.4 Self-paced Activities 79
II.4.1 Characteristic Python Code Patterns 79
II.4.2 Get Survivors of the Titanic Sinking 84
II.5 Learning Outcomes 88

Chapter III

Data Cleaning and Preparation with Industrial Applications - ETL, Deduplication, Imputation, Scaling, and Workflow Automation

III.1 ETL, Warehousing, and Data Wrangling 93
III.2 Deduplication Methods Including Fuzzy and Phonetic Matching 96
III.3 Imputation Techniques with KNN and Regression 102
III.4 Scaling Featuring Z-Score and Median Deviation 108
III.5 Outlier Detection Using Visual and Statistical Approaches 112
III.6 Additional Steps in Data Preprocessing 123
III.7 Automated Data Cleaning and Preparation 125
III.7.1 December Production Data 125
III.7.2 File Watcher and Automatic Data Preprocessor 132
III.7.3 January Production Data 141
III.7.4 Sensor Data Stream with Duplicates, Outliers, and Missing Values 145
III.7.5 Cleaned Data Output in Operations Hub by Watcher and Preprocessing Pipeline 146
III.8 Self-Paced Challenge: Manufacturing Data Analysis 156
III.9 Learning Outcomes 160

Chapter IV

Exploratory Data Analysis including ML Techniques - Correlation Measures, Association Rules, and Tree-Based Machine Learning

IV.1 Correlation and Association 165
IV.1.1 Automated Correlation Analysis with SMS Notification and Email Report 165
IV.1.2 Automated Association Analysis for Each Batch of 100 Observations 174
IV.2 Decision Trees and Random Forests 184
IV.2.1 Visualizing Spam Filters 184
IV.2.2 Predicting Customer Loyalty 193
IV.3 Learning Outcomes 208

Chapter V

Data Protection and Privacy in AI-Driven Manufacturing - Regulatory Compliance, Consent Systems, and Encrypted IoT Flows

V.1 Data Protection 213
V.1.1 General Data Protection Regulation 213
V.1.2 California Customer Privacy Act 214
V.1.3 Recommended Measures for Data Privacy Compliance 214
V.2 Consent Management: Frontend-Backend Deployment with Real-Time Analytics 216
V.2.1 Consent Flow and HTML Implementation 216
V.2.2 Backend API Programming and Automated Summary Integration 221
V.2.3 Frontend Consent Submission and Backend Update 227
V.2.4 AI-Generated Comprehensive Insights Report 234
V.3 Consent Compliance Audit: Self-Paced Activity 238
V.4 Encryption, Anonymization, and Pseudonymization 239
V.4.1 Positional Number Systems and Encoding Systems 239
V.4.2 Hashing, Masking, Tokenization, and Encryption 243
V.4.3 Encryption in Warehousing 245
V.4.4 Secure Broker Communication 250
V.4.4.1 HTTPS over TCP 251
V.4.4.2 MQTT over TCP 252
V.4.4.3 Encrypted MQTT 253
V.4.4.4 ML-Based Anomaly Detection in Process Surveillance 254
V.4.4.5 IoT Messaging with Credentialed Access and Attack Modeling 254
V.4.4.6 Implementation Process 268
V.5 Learning Outcomes 276

Chapter VI

Machine Learning for Supply Chain and Marketing Optimization - From ML Techniques to AI-Guided Research: Gradient Boosting, LSTMs, Smart Clustering, and Dimensionality Reduction

VI.1 Artificial Intelligence and Machine Learning 281
VI.2 Unsupervised ML – Marketing Strategies 288
VI.2.1 K-Means – Customer Segmentation 288
VI.2.2 Self-Paced Study: Unsupervised ML – Interactive Visualizations 307
VI.2.2.1 Principal Component Analysis 308
VI.2.2.2 t-Distributed Stochastic Neighbor Embedding 309
VI.2.2.3 Static 2D and Interactive 3D t-SNE Cluster Scatter Plots 311
VI.2.3 Self-Paced Exercise: Unsupervised ML –Purchase Behavior 315
VI.2.4 High-Profile Customers 321
VI.3 Supervised ML – Automated Supply Chain Management 324
VI.3.1 Demand Forecasting 325
VI.3.1.1 Real-World Data Collection 326
VI.3.1.2 Supervised AI for Fixed and Flowing Data 326
VI.3.1.3 Stabilizing Data for GBM – Moving Window Average 327
VI.3.1.4 Sequential Data Preparation for LSTM – Lagging 328
VI.3.1.5 Hybrid Model 329
VI.3.1.6 Neural Network Fundamentals 337
VI.3.1.7 Core Activation Functions 338
VI.3.2 Automated Inventory Management 339
VI.3.3 Route Optimization 340
VI.3.4 Risk Mitigation and Dynamic Pricing 343
VI.4 Learning Outcomes 344

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.

• Certificate of Attendance for all participants.
• Certificate of Completion with submitted work or brief quiz / oral exchange.
• Distinction awarded for strong practical work or deeper discussion - ensuring 'Your Science' stands for real value.

Certificate of Attendance for webinar participation, Certificate of Successful Completion for submission of defined work, "With Distinction” tag for advanced submissions.

This training is delivered via Microsoft Teams Webinars, with no installation required for participants. Sessions combine clear, expert-led instruction with applied demonstrations. Each webinar includes a dedicated 10-15 minute Q&A segment for live interaction. To ensure continued support, participants may submit follow-up questions by email, with responses provided via email or phone - approximately 10 minutes per session per participant. This personal guidance promotes real understanding without overwhelming schedules.

The course is designed to fit the schedules of busy professionals. It typically runs over three weeks, with six sessions of two hours each, held on weekday late morning (10-12). The schedule may be modified by unanimous agreement of the participants to ensure maximum accessibility.

Introductory Rate

This training, successfully delivered in previous sessions, is now available for the first time on the Lifelong Learning platform. It is offered at a special introductory rate of EUR 645, providing full access to all content, high-quality resources, and expert support.

Training Outcomes

This program is designed around structured, chapter-specific learning outcomes that go far beyond generic skills. Each chapter culminates in applied competencies directly tied to industrial workflows - from integrating CRM and SQL systems to deploying AI models for supply chain optimization.

Participants progressively build fluency in Python programming, statistical reasoning, data privacy compliance, and ML-based decision support. By combining regulatory insight, domain-specific automation, and self-paced exploration, the training ensures not only technical proficiency but also real-world readiness for AI-driven production environments.

Unlike traditional programs, our learning outcomes are not abstract goals - they are measurable capabilities, anchored in authentic use cases and reinforced by guided exercises and system-level simulations.

Presentation Language

The default presentation language will be English, but it may be changed by unanimous agreement of the participants. However, all participants are welcome to interact in the language of their choice - English, French, German, or Luxembourgish. Experience shows that language is rarely a barrier, as scientific terminology is largely shared across English and French.