In this workshop, your employees will be introduced to the basics of data science and machine learning — without the need for programming knowledge. You will learn how these technologies can be used to make data-based decisions, recognize patterns in large amounts of data, and optimize business processes. The participants gain a better understanding of the application of machine learning in practice, which helps them to use the potential of data in the company and actively shape the digital transformation.

Data science and machine learning offer valuable insights and opportunities to optimize business processes. Even without programming knowledge, employees can understand basic concepts and use these technologies in a targeted manner to make data-based decisions.

Benefits for you as a company:

• Unlocking data-driven optimization potential without technical hurdles
• Fostering innovative strength and increasing efficiency through data-based decisions
• Expanding entrepreneurial expertise in the area of data science
• Improving analytical ability and identifying trends and patterns
• Enabling wider use of machine learning across business areas

1. Basics of data science and machine learning (ML) and their significance for companies

• What is data science and how does it help with decision-making? :
  An introduction to data science and its role in data-driven decision making.
• Overview of machine learning: What is it, how does it work and why is it relevant for the future of companies? :
  The basics of machine learning and its importance for companies.
• Difference between traditional methods and data-driven approaches:
  Why data-driven approaches are becoming increasingly important.
• Practical application examples: How data science and ML are transforming companies:
  Examples of the application of data science and ML in various industries.

2. Understanding and preparing data

• The importance of high-quality data: Why data cleansing and preparation are crucial:
  Why clean and well-prepared data is the key to meaningful results.
• Introduction to different types of data (e.g. structured, unstructured data) and how they are processed:
  An overview of the different types of data and how they are processed.
• Tools and methods for data preparation (e.g. Excel, Google Sheets, simple data cleansing tools):
  Tools and methods for effective data preparation.
• Data visualization: How to recognize and visually represent data to understand patterns and trends:
  The importance of data visualization for analysis

3. Basics of Machine Learning

• Supervised vs. unsupervised learning: What are the differences and examples of use? :
  An introduction to the two main types of machine learning.
• Introduction to common algorithms: linear regression, decision trees, K-means clustering:
  The most important algorithms in machine learning and their applications.
• Create models without programming: Using easy-to-use modeling tools (e.g. Google AutoML, Microsoft Azure ML Studio):
  How to build machine learning models without programming knowledge.
• How machine learning is used in a corporate context: Examples from marketing, customer analysis and process optimization:
  Practical use cases for ML in companies.

4. Data analysis and predictive models

• Analyzing data sets: How to recognize patterns and draw conclusions from the data:
  Tips for effectively analyzing data sets.
• Introduction to predictive models: How ML is used to predict future trends and behaviors:
  The role of forecasting models in decision making.
• Application examples: How companies use predictive models to better understand sales forecasts and customer needs:
  Examples of using predictive models.
• Tools to perform simple data analyses without programming knowledge (e.g. Tableau, Power BI):
  Tools for data analysis without programming knowledge.

5. Understanding and interpreting machine learning results

• What to do with the results? How do you interpret the predictions of ML models? :
  Tips for interpreting ML results.
• Understanding metrics: What are accuracy, precision, and recall, and how do they affect model performance? :
  The most important metrics for evaluating ML models.
• Introduction to model evaluation: How to test a model for effectiveness (e.g. through cross-validation):
  Methods for evaluating model performance.
• Decision-making: How to make well-founded business decisions based on the results:
  How to translate ML results into business decisions

6. Use of data science and machine learning without programming

• Presentation of user-friendly data science and ML platforms (e.g. Google AutoML, KNIME, RapidMiner):
  Tools to get started with data science and ML without programming knowledge.
• Step-by-step instructions for using these tools for modeling and analysis:
  Practical instructions for using ML tools.
• Integration of ML tools into business processes and strategies without deep technical knowledge:
  How to integrate ML tools into existing processes.
• Automating workflows: How ML-based tools can make business processes more efficient:
  The role of ML in process automation

7. Practical exercises and examples

• Practical example: Create a simple predictive model using a drag-and-drop tool:
  A hands-on exercise to build an ML model.
• Analyzing a data set and applying ML models to predict results:
  An exercise in data analysis and forecasting.
• Working with a practical example from your own company: How can data science be specifically applied in your organization? :
  Applying data science to real business data.
• Final project: Implementation of a simple project using the techniques and tools learned:
  A practical project to apply the skills you have learned.

8. Ethics and Responsibility in Data Science and Machine Learning

• What does ethics mean in data science and machine learning? :
  The importance of ethics in data processing and ML.
• Risks and challenges in handling data: Data protection, distortions and fairness:
  The most important ethical challenges in data science and ML.
• Best practices for responsible use of machine learning:
  Tips for using ML ethically.
• How companies can handle their data transparently and responsibly:
  Strategies for responsible use of data.

9. The future of data science and machine learning

• Trends and innovations in data science and machine learning:
  The latest developments in data science and ML.
• How companies can increase their competitiveness through the use of AI and machine learning:
  The role of AI and ML in the competitiveness of companies.
• How data science and machine learning are changing the job market and which new professional fields are emerging:
  The impact of data science and ML on the job market.
• Further steps: How to develop in the area of data science and machine learning in the long term:
  Tips for long-term development in data science and ML.

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