ITW – Internationale Woche

In this course, students will learn how to transform and visualize business data using Power BI, integrating advanced features such as AI-driven insights. Students will gain skills in data cleaning and modeling, designing interactive dashboards, and deriving actionable business insights. Through hands-on exercises, students will develop practical expertise in the following areas:

• Introduction to Business Data Visualization: Power BI overview and setup, Navigating Power Query Editor.
• Data Transformation and Modeling for Analytics: Using Power Query for Data transformation, Relationships between Data models.
• Building Interactive Dashboards: Data preparation with Power Query, Basic visuals, usage of interactive elements: Filters, slicers, Drill-throughs, visualising data in business dashboards using Power BI.
• Incorporating AI Insights: Using Power BI’s built-in AI insights: Influencers, Decomposition Tree and Q&A features.
• Case Study: Perform end-to-end analysis and create interactive dashboard on Stock Market Dataset to identify key trends, patterns, and actionable insights. 

This module is designed to provide students with the knowledge and skills necessary to design, implement, and manage secure computer networks.

In this module, students will gain a solid foundation in establishing and maintaining robust network infrastructures. Simultaneously, the module addresses the critical aspect of securing these networks against potential threats, ranging from cyberattacks to data breaches. Through a combination of theoretical concepts and practical exercises, students will develop the expertise needed to identify vulnerabilities, implement security measures, and formulate strategies to safeguard information assets in the interconnected world of computer networks.

In addition to providing a broad range of fundamental computer networking and security knowledge for all IT careers, this module will also provide students with an opportunity to further self-study and gain conceptual knowledge and practical skills required for 200-301 Cisco® Certified Network Associate (CCNA®) exam.

The course aims to guide students in designing and building an E-Commerce website over the course of a week, applying principles of good user experience with a particular focus on theories and strategies related to emotional and persuasive design. During the week, students test each other’s projects using various methods, such as interviews and observational studies, in addition to employing analytics tools to enhance their understanding. 

Course Outline

• Monday: Introduction to basic operations and User Experience (UX) principles. Task: Set up the E-Commerce website for specific target groups and create user personas.
• Tuesday: Emotional Design. Task: Design and implement the E-Commerce website, incorporating Emotional Design strategies tailored to the selected target groups and personas.
• Wednesday: Persuasive Design. Task: Conduct user tests, apply Persuasive Design strategies, and develop the next iteration of the E-Commerce website.
• Thursday: Growth Hacking planning for the E-Commerce website Task: Conduct user tests and finalize the E-Commerce website with its final iteration. Develop a plan for improving the website, including strategies for growth and optimization.
• Friday: Presentation and Reflection. Task: Present the completed websites and key findings. Summary: Recap and reflect on the process.

The course concludes with an exam. 

Applying theoretical concepts in practice during the course, combined with students testing each other's E-Commerce websites, provides valuable insights. This process helps students recognize and understand different user groups, learn how to emotionally engage them, and effectively use emotions and persuasion in the E-Commerce context.

1. Introduction to Ethical Hacking and Vulnerability Analysis
   Fundamentals of key issues in the information security world, including the basics of ethical hacking. Different vulnerability assessment and Penetration testing tools: Shodan, Nmap, Nexpose, Netcraft, privateeye, Google advanced search operators, Harvester, Burpsuite
   
   
2. Social Engineering and Session Hijacking
   Social engineering concepts and techniques, including how to identify theft attempts. Case Study: Phishing attack MiM attack: Kali Linux, BettrCap, SetTool Kit, GoFish
   
   
3. Hacking Web Servers and Hacking Web Applications
   Web server attacks, including a comprehensive attack methodology used to audit vulnerabilities in web server and web applications. web application hacking methodology, SQL Injection attack, HTTrack
   
   
4. IoT and Cloud Hacking
   IoT and Cloud attacks, hacking methodology, hacking tools, IoT and cloud security techniques and tool

This course provides a comprehensive understanding of artificial intelligence (AI) security, with a focus on ethical hacking principles, attacks on AI systems, and defence strategies. Students will gain practical knowledge of key threats such as data poisoning, model inversion and backdoor attacks. They will understand how to design secure and ethically responsible AI solutions. Using advanced tools such as the Adversarial Robustness Toolbox, CleverHans and others, students will learn to identify vulnerabilities and implement protection methods.
 
Learning Objectives:

1. Understand the key security challenges and threats in AI systems.
2. Learn to perform ethical hacking attacks such as data poisoning, model inversion and backdoor attacks.
3. Analyse and apply defence strategies such as differential privacy, anomaly detection and adversarial model training.
4. Promote an ethical approach to AI security, guided by legal regulations and moral principles.

 
Recommended prerequisites for students: Python, ML/AI basics
 
Practical Assessments (Hands-On Labs) formats:

• Red Team vs. Blue Team.
• AI Model Building & Defense using tools like Adversarial Robustness Toolbox or CleverHans to secure or attack AI systems.

This module introduces the core ideas and the basis techniques of machine learning. It covers theory, algorithms and applications, focusing on real understanding of the principles of inductive learning theory and of several machine learning techniques.

• Concept Learning
• Decision Tree Learning
• Bayesian Learning
• Artificial Neural Networks
• Support Vector Machines

Phyton is the programming language used in this module but prior knowledge of Python programming is not required. Students will gain all required knowledge in a step-by-step fashion, through examples.

Contents:

• Data driven International Digital Marketing Strategies for best ROI in eCommerce
• Digital marketing channels (owned, earned, paid) optimization
• Search Engine Optimization as part of the digital marketing
• Digital marketing target group segmentation
• Optimizing ROI in Social Media Marketing (including TikTok)
• Digital marketing targets, analytics and metrics

After the module/course students will 

• Be able to manage data driven digital marketing campaigns for business in an international level.
• Be able to analyse what digital marketing channels companies should use (owned, earned, paid)
• Understand basics of Search Engine Optimization
• Can apply target groups segmentation especially with META and Google Ads tools
• Understand how Social Media Marketing works
• Understand and can apply digital marketing analytics and metrics settings (especially Google Tag Manger and Analytics 4)