Fundamentals for data-driven products
From data understanding to PoC decision
Did you know?
This course is part of the certified Data Expert Master Class. By booking the entire Master Class,you save 26 percent compared to booking the individual courses.
- PoC as a basis: What is a proof of concept?
- The importance of hypotheses: How hypothesis-driven work creates structure and focus.
- Data exploration: Identifying opportunities, risks, and potential at an early stage.
- Role of the data structure: The significance of the existing data structure for the subsequent data product.
Statistics as a starting point: Using the basics correctly
- Data at a glance: Initial orientation and key questions
- Basic concepts of mathematics: measures of location, measures of dispersion, distributions
- Normal distribution: significance and practical classification
- Visualization: Making data visible and comparable
- Systematic overview of the most important characteristics of the data
Data exploration: Recognizing the structure behind the data
- Missing values: Types and technical significance
- Outliers & data problems: Distinguishing between outliers and measurement errors; role of subject matter expertise
- Simple data transformations: fundamentals and relevance
- Documentation: Systematic recording of findings for the data project
Hypothesis work: Structure, logic, and preparation for hypothesis formation.
Hypothesis testing: basic concept, use of test cards and learning cards.
Reflection: Deriving new questions based on results.
Fundamentals of time series: significance in AI and overview of time series types.
Time series: Discovering and interpreting patterns
- Time series in practice: typical forms and patterns
- Exploration: Basic analysis methods and tests
- Practical examination: Recognizing structure, behavior, and special features
- Interpretation: Derive initial conclusions from time series.
Data maturity, documentation — significance for the data product
- Maturity model: Five stages of a data-driven company; determining the status of the data set
- Documentation: Exploration logbook and documentation according to CRISP-DM Phase 3
- Preparation for modeling: clarification of data status, open issues, risks, and assumptions
- Analysis and consolidation: clarification of the state of knowledge, identification of risks and relevant patterns.
- Derivation for data preparation: Necessary cleanups, transformations, and feature requirements.
- Transition to PoC: Criteria for "Fit for PoC" and go/no-go fundamentals.
Contents and course schedule
1. Understand data before making decisions
- What a PoC really is – and what role data understanding plays
- How hypotheses bring structure, focus, and speed to data projects
- The importance of data structures for subsequent modeling
2. Apply statistical principles confidently
- Location measures, dispersion, distributions, and normal distribution
- Visual exploration as the starting point for every analysis
3. Explore data and identify typical challenges
- Understanding missing values and classifying them correctly
- Distinguishing between outliers and measurement errors
- Relevant transformations and documentation of findings
4. From assumptions to real knowledge
- Develop and test hypotheses logically
- Using Test Cards & Learning Cards
- Reflect critically on results
5. Analyzing time series in a practical manner
- Recognizing patterns, understanding trends and seasonality
- Use simple tests and visual exploration
- Derive initial interpretations for PoCs
6. Document findings professionally
- Determining the maturity level of a data set
- Set up an exploration logbook
- Clearly identify risks, assumptions, and open issues
7. Create clarity for the PoC
- Consolidation of all findings
- Identification of opportunities, risks, and hypotheses
- Prepare go/no-go decisions carefully
This is how you learn in this course
This course offers you a digital blended concept that has been developed for part-time learning. Thanks to a flexible mix of online seminars and self-study phases, you are sure to reach your goal. This is how you learn in this course:
Learning environment: In your online learning environment, you will find useful information, downloads and extra services for this training course after you have registered.
Self-study phases: Learn independently, at your own pace and whenever you want. Our courses offer you didactically high-quality learning material.
Live webinars: In regular online seminars, you will meet your trainers in person. You will receive answers to your questions, specific assistance and instructions on how to deepen your knowledge and apply the skills you have acquired in practical exercises.
Learning community: A digital learning community is available to you throughout the course. Exchange ideas with other participants and the trainers and ask questions.
Future Jobs Club: Get exclusive access to a business network, news, and future work hacks.
Certificate of completion and Open Badge: As a graduate of the course, you will receive a certificate of completion and an Open Badge, which you can easily share on professional networks (including LinkedIn).
Your benefit
- You understand how a PoC is structured and what really matters in the early stages.
- You will learn how to explore data in a structured way and confidently recognize initial patterns.
- You understand how hypotheses create structure, focus, and clarity in data projects.
- You recognize which data structures are relevant—and when a data set is unsuitable for a PoC .
- You understand how to classify missing values, outliers, or measurement errors.
- You are able to apply statistical principles in a targeted manner to better understand correlations.
- You know how to classify time seriesand can recognize and interpret important patterns.
- You document findings in such a way that they can be directly reused in the project.
- You are able toderive meaningful adjustments, transformations, and next steps.
- Based on your analyses, you make an informed go/no-go decision for the PoC.
Methods
A well-thought-out mix of content, methods and support is essential for learning success, especially in blended online learning. Our course concept is precisely tailored to this: structured self-study phases, in-depth trainers, best-practice examples, practical exercises, discussions and sharing experiences in the learning community.
Tools
Recommended for
This training course is suitable for anyone who wants to develop a future-oriented data mindset and learn how to create real added value through data. You will learn how to design PoCs for data and AI projects, evaluate their feasibility, and confidently act as an interface between business and IT.
- Project managers in data projects
- process managers
- Specialists from controlling, HR, finance, and other departments who finally want to move beyond Excel spreadsheets
- IT specialists and individuals who are proficient in a scripting language
- Individuals with little prior experience with data but who are highly motivated to work in a data-driven manner
Mastering the future
Recognize potential, manage data projects, and increase added value—with the "Data Expert" master class.
Start dates and details
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