Understanding digitization: Data processing with Excel, AI basics and ChatGPT.

Chapter 1: Data Literacy

You will learn how data supports decision-making - whether in strategic planning or in operational processes. You will recognize which characteristics distinguish data and what is important in terms of its structure and quality.

You will familiarize yourself with the individual steps of data analysis: from preparation to evaluation and interpretation. You will apply suitable methods for cleansing, selecting and aggregating data.

You use best practices to present your results in vivid visualizations - clear, understandable and convincing.

You will use specific problems to recognize which AI method makes sense - for example classification, regression or clustering. You will also expand your technical vocabulary around analysis and artificial intelligence and use the terms confidently in the right context.

Chapter 2: Excel Basics

You will learn the basics of using Excel, which you need to structure and prepare data in tables.

Using a practical example on the topic of financial planning, you will learn what you need to consider when creating an Excel file - for example, how to correctly format columns and cell values and why it is worth formatting important cell ranges as table objects. You will apply formulas and functions for simple calculations and be able to differentiate between relative, absolute and mixed cell references.

You will learn how to use structured references to make your formulas even easier to understand and how to filter and sort tables to get a better overview of your data.

Chapter 3: Data analysis with Excel

In this chapter, you will learn how to summarize, analyse and visualize data in order to gain insights. And in the most practical way possible!

We look at the data from an online course provider to answer questions about customer satisfaction, processing times and the background of participants. You will learn how to use and interpret basic statistical key figures, pivot tables and data slices for simple analyses. You will learn to use named ranges to make formulas easier to understand and conditional formatting to properly highlight important data points.

With functions such as XVERWEIS() you will get to know Excel as an effective tool for data organization. And of course, in addition to key figures and aggregated tables, you will also learn how to use visualizations effectively to understand your data.

At the end, an exciting interim project awaits you, where you can put all your data and Excel skills to the test once again.

Chapter 4: Ensuring data quality

This chapter is about ensuring data quality standards - at every stage of the value chain.

You are facing a new challenge: the data you worked with in chapter 1 was clean and tidy. Unfortunately, this is often not the case in reality. There are many reasons for this, which you should understand better.

In this chapter, you will therefore first learn about the dimensions of data quality. But not just in theory, but in practice using real data with the Excel tool. You will learn how to deal with typical errors when importing different data sources, how to clean data using the PowerQuery Editor and how to ensure certain aspects of data quality using formulas and data checks, for example. The analysis skills from chapter 1 are also used here. The limitations of Excel are also discussed in more detail in this chapter.</p

At the end of the chapter, another exciting final project awaits you, in which you can consolidate all your newly acquired skills.

Chapter 5: Data-driven management

You will learn how cognitive biases influence our perception and what effect they can have on data-based decisions. You interpret analysis results confidently and can assess their significance in the respective context.

Using concrete examples, you will recognize how data products contribute to the achievement of company goals - whether in process optimization, marketing or product development. You will develop strategies and measures to effectively promote a data-driven corporate culture.</p

You will use proven data storytelling methods to communicate insights in an understandable and convincing way. You will also describe the innovation cycle of data products and understand which phases are passed through from idea to implementation.

Use the Priority Matrix Canvas to prioritize data projects in a structured and goal-oriented way. You know the basics of effective data governance and can explain its importance and application in the corporate context.
Finally, you will define what constitutes data quality, which characteristics are crucial - and why it plays a central role in every data-based decision.

Chapter 6: Data Storytelling

You will learn about the different types of data visualizations - from simple bar charts to complex interactive dashboards. You will understand which form of presentation is suitable for which type of data and what is important when making a selection.</p

You will direct the attention of your audience by clearly structuring visualizations, highlighting core messages and consciously using design principles.

In addition, you present data and analyses embedded in a story - with a common thread that creates context, explains connections and conveys your message convincingly.

Chapter 7: ChatGPT - the basics of generative AI

You will learn about the areas in which generative AI is already being used today - from text creation and image generation to the automation of creative processes. You will be able to name typical use cases and assess their benefits in a company context.

You will also deal with the ethical and legal aspects of AI use. You will understand which issues relating to data protection, transparency and responsibility need to be clarified before AI solutions are used.

In addition, you work with structured prompts to control AI models in a targeted manner. You formulate inputs in such a way that they lead to precise, relevant results - efficient, comprehensible and application-oriented.