This course covers the role and responsibilities of the Scrum Product Owner, including working with the Product Backlog. It also covers the fundamentals of artificial intelligence, Python-based data analysis, big data, dashboards, and text mining. You’ll also learn the complete machine learning workflow—from data preparation and model development to evaluation and interpretation.
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Certificates: Certificate "Professional Scrum Product Owner (PSPO I) from Scrum.org"
Data Analytics" certificate
Machine Learning" certificate -
Examination: Praxisbezogene Projektarbeiten mit Abschlusspräsentationen
Scrum.org-Zertifizierung PSPO I - Professional Scrum Product Owner (in englischer Sprache) -
Teaching Times: Full-timeMonday to Friday from 8:30 a.m. to 3:35 p.m. (in weeks with public holidays from 8:30 a.m. to 5:10 p.m.)
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Language of Instruction: German
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Duration: 12 Weeks
Agile project management with Scrum: Product Owner (PSPO I)
Scrum Fundamentals (approx. 2 days)
Definition of Scrum
Empirical Process Control
Transparency
Review
Adaptation
Scrum Values (Commitment, Focus, Openness, Respect, Courage)
Scrum Team (approx. 1 day)
Product Owner
Developers
Scrum Master
Accountabilities (Product Owner, Scrum Master, Developers)
Self-managing Teams
Scrum Events (approx. 3.5 days)
Time Box
Sprint
Sprint Planning
Daily Scrum
Sprint Review
Sprint Retrospective
Artifacts (approx. 3 days)
Transparency and Verifiability
Product Backlog
Sprint Backlog
Increment
Definition of Done
Scrum Product Owner (approx. 3.5 days)
Responsibilities of the Scrum Product Owner
Product Vision and Product Strategy
Requirement Identification and Analysis
Prioritization and Value Maximization
Risk- and value-oriented product decisions
Product Metrics and Success Measurement
AI-powered analysis of user feedback for prioritization
Revision of the Product Backlog
Stakeholder management
Product Backlog Management (approx. 2 days)
Formulating Items
Sorting entries
Make goals and missions clear
Optimizing the development team’s work
Maintaining Backlog Transparency
Refinement techniques and splitting
Forecasting (velocity, burn-up/burn-down)
AI-powered data analysis for forecasts
Project work, certification preparation and Scrum.org Professional Scrum Product Owner certification (PSPO I) in English (approx. 5 days)
Data analytics
Introduction to Data Analytics (approx. 1 day)
Tasks, Areas of Application, and Typical Use Cases of Data Analytics
Data Analytics Processes and CRISP-DM
The Role of the Data Analyst and How It Differs from Data Science, Data Engineering, and Business Intelligence
Classification of Artificial Intelligence and Machine Learning
Python for Data Analytics (approx. 1 day)
Working with Jupyter Notebooks
NumPy and Pandas for Data Analysis
Importing and Exporting Data from Various Sources, e.g., Excel and CSV
Selecting and transforming data
Data Preparation and Exploratory Data Analysis (approx. 3 days)
Data cleaning and transformation with Pandas
Handling missing, incorrect, and outlier data
Merging, grouping, and aggregating data
Exploratory data analysis and deriving initial insights
Fundamentals of statistical analysis
SQL and Data Management (approx. 2 days)
Relational Databases and Data Models
Data Queries and Processing with SQL
Joins, aggregations, and analytical queries
Interaction Between SQL and Python in Data Analysis
Data Quality and Data Protection
Connecting to and Using Different Data Sources and Database Systems
Data Visualization and Data Storytelling (approx. 2 days)
Visualization with Matplotlib, Seaborn, and Plotly
Selecting Appropriate Charts and Visualization Methods
Preparing and interpreting analysis results
Data Storytelling and Communication Tailored to the Audience
Power BI and Interactive Dashboards (approx. 2 days)
Data Import and Preparation with Power Query
Data modeling and relationships
Basics of DAX and metrics
Creating interactive reports and dashboards
Visualization and Communication of Business KPIs
Overview of Alternative Business Intelligence and Visualization Solutions
AI-powered data analysis (approx. 2 days)
Use of generative AI in the data analytics workflow
AI-powered data preparation, analysis, and visualization
Support with Python and SQL queries
Prompting for data analysis and interpretation of results
Critical evaluation of data sources and detection of synthetic and implausible data
Quality assurance, data protection, and responsible use of AI
Business Analytics and Statistical Fundamentals (approx. 2 days)
Key Metrics and KPIs in a Business Context
Descriptive Statistics and Statistical Relationships
Correlation, Causality, and Simple Regression Analyses
Fundamentals of confidence intervals and hypothesis testing
Interpreting Analysis Results and Deriving Data-Driven Recommendations
Project work (approx. 5 days)
To consolidate the content learned
Presentation of the project results
Machine Learning
Introduction to Machine Learning and the ML Workflow (approx. 3 days)
Fundamentals, Applications, and Types of Machine Learning
Classification of Modern AI and Machine Learning Methods
Machine Learning Workflow with scikit-learn
Training, validation, and test data
Underfitting, overfitting, and model evaluation
Cross-Validation
Data Preparation and Feature Engineering (approx. 2 days)
Data Quality and Preprocessing
Scaling, Normalization, and Encoding
Feature Engineering and Feature Selection
Handling Missing Values, Outliers, and Imbalanced Datasets
Supervised Learning – Regression and Classification (approx. 5 days)
Regression and Classification Methods
Linear and Regularized Models
Distance-, probability-, and tree-based methods
Ensemble and Boosting Methods
Model Comparison and Hyperparameter Optimization
Evaluation Metrics and Probabilistic Predictions
Feature importance and explainable AI
Unsupervised Learning (approx. 2 days)
Dimension Reduction and Feature Extraction
Clustering Methods and Evaluation of Clustering Results
Anomaly Detection
Time Series Analysis and Forecasting (approx. 1 day)
Fundamentals and Analysis of Time Series Data
Forecasting Methods and Machine Learning for Time Series
Neural Networks and Modern AI (approx. 2 days)
Fundamentals of Neural Networks and Deep Learning
Machine Learning for Text Data and Natural Language Processing
Foundation Models, Large Language Models, and Generative AI
Deployment of ML models (approx. 1 day)
Storage and deployment of trained models
Development of simple ML applications
Interfaces and platforms for machine learning models
Project work (approx. 4 days)
To consolidate the content learned
Presentation of the project results
Changes are possible, the course content is updated regularly.
After this course, you will have a firm grasp of the Scrum framework and be able to manage product development as a product owner. You will be familiar with Scrum artifacts and be able to take over backlog management.
In addition, you can process and analyze data using Python and SQL, create meaningful visualizations and interactive dashboards with Power BI, and interpret and communicate analysis results in a way that is tailored to your audience. You can use generative AI specifically to support data analysis.
In addition, you can prepare data for machine learning applications, select appropriate supervised and unsupervised learning methods, and train, evaluate, and optimize models. You can interpret model results and implement basic machine learning applications. You are also familiar with current developments in the fields of deep learning and generative AI.
Computer science, mathematics, electrical engineering and people with a degree in (business) engineering
With Scrum, you will learn a project and product management process model that was originally used for agile software development in particular, but is now also used in many other specialist areas. As a scalable project management and development method, it is used successfully in numerous large-scale projects with several hundred team members. The official certificate from Scrum.org provides you with internationally recognized proof of your qualifications as a Scrum Product Owner.
Machine learning is used across various industries to analyze large amounts of data, identify patterns, optimize processes, and make predictions. Such skills are applied in fields such as IT, manufacturing, logistics, finance, and marketing. Proficiency in Python, as well as in the development, evaluation, and application of machine learning models, opens up career opportunities in data- and AI-driven fields.
In addition, data-driven decisions are playing an increasingly important role in companies and organizations. Accordingly, there is a need for professionals who can process, analyze, and clearly visualize data from various sources. Knowledge of Python, SQL, and business intelligence opens up career opportunities in the field of data analytics as well as in data-driven functional and business areas.
Didactic concept
Your lecturers are highly qualified both professionally and didactically and will teach you from the first to the last day (no self-study system).
You will learn in effective small groups. The courses usually consist of 6 to 25 participants. The general lessons are supplemented by numerous practical exercises in all course modules. The practice phase is an important part of the course, as it is during this time that you process what you have just learned and gain confidence and routine in its application. The final section of the course involves a project, a case study or a final exam.
Virtual classroom alfaview®
Lessons take place using modern alfaview® video technology - either from the comfort of your own home or at our premises at Bildungszentrum. The entire course can see each other face-to-face via alfaview®, communicate with each other in lip-sync voice quality and work on joint projects. Of course, you can also see and talk to your connected trainers live at any time and you will be taught by your lecturers in real time for the entire duration of the course. The lessons are not e-learning, but real live face-to-face lessons via video technology.
The courses at alfatraining are funded by Agentur für Arbeit and are certified in accordance with the AZAV approval regulation. When submitting a Bildungsgutscheinor Aktivierungs- und Vermittlungsgutschein, the entire course costs are usually covered by your funding body.
Funding is also possible via Europäischen Sozialfonds (ESF), Deutsche Rentenversicherung (DRV) or regional funding programs. As a regular soldier, you have the option of attending further training courses via Berufsförderungsdienst (BFD). Companies can also have their employees qualified via funding from Agentur für Arbeit (Qualifizierungschancengesetz).