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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
Scrum basics (approx. 2 days)
Definition of Scrum
Empirical process control
transparency
Review
Adaptation
Scrum Team (approx. 1 day)
product owner
Developer
Scrum Master
Responsibilities for results
Scrum events (approx. 4 days)
Time Box
Sprint
Done
Sprint Planning
Daily Scrum
Development Work
Sprint Review
Sprint Retrospective
Artificial intelligence (AI) in the work process
Presentation of specific AI technologies
and possible applications in the professional environment
Artifacts (approx. 3 days)
Transparency and verifiability
Product backlog
Sprint backlog
Increment
Definition of Done
Scrum Product Owner (approx. 3 days)
Tasks of the Scrum Product Owner
Requirements identification and analysis
Prioritization and value maximization
Revision of the product backlog
Product Backlog Management (approx. 2 days)
Formulate entries
Sorting entries
Making goals and missions recognizable
Optimize the work of the development team
Keeping the backlog transparent
Project work, certification preparation and Scrum.org Professional Scrum Product Owner certification (PSPO I) in English (approx. 5 days)
Data analytics
Introduction to data analysis (approx. 1 day)
CRISP-DM reference model
Data analytics workflows
Definition of artificial intelligence, machine learning, deep learning
Requirements and role in the company of data engineers, data scientists and data analysts
Review of Python basics (approx. 1 day)
data types
Functions
Data analysis (approx. 3 days)
Central Python modules in the context of data analytics (NumPy, Pandas)
Process of data preparation
Data mining algorithms in Python
Artificial intelligence (AI) in the work process
Presentation of specific AI technologies
and possible applications in the professional environment
Data visualization (approx. 3 days)
Explorative data analysis
insights
Data quality
Benefit analysis
Visualization with Python: Matplotlib, Seaborn, Plotly Express
Data storytelling
Data management (approx. 2 days)
Big data architectures
Relational databases with SQL
Comparison of SQL and NoSQL databases
Business Intelligence
Data protection in the context of data analysis
Data analysis in a big data context (approx. 1 day)
MapReduce approach
Spark
NoSQL
Dashboards (approx. 3 days)
Library: Dash
Structure of dashboards - Dash components
Customizing dashboards
Callbacks
Text Mining (approx. 1 day)
Data preprocessing
Visualization
Library: SpaCy
Project work (approx. 5 days)
To consolidate the content learned
Presentation of the project results
Machine Learning
Introduction to Machine Learning (approx. 5 days)
Why machine learning?
Application examples
Supervised learning, unsupervised learning, partially supervised learning, reinforcement learning
Examples of data sets
Getting to know data
Training, validation and test data
Viewing data
Making predictions
Supervised learning (approx. 5 days)
Classification and regression
Generalization, overfitting and underfitting
Size of the data set
Algorithms for supervised learning
Linear models
Bayes classifiers
Decision trees
Random Forest
Gradient Boosting
k-nearest neighbors
Support Vector Machines
Conditional Random Field
Neural Networks and Deep Learning
Probabilities
Unsupervised learning (approx. 5 days)
Types of unsupervised learning
Preprocessing and scaling
Data transformations
Scaling training and test data
Dimension reduction
Feature engineering
Manifold learning
Principal component decomposition (PCA)
Non-negative matrix factorization (NMF)
Manifold learning with t-SNE
Cluster analysis
k-Means clustering
Agglomerative clustering
Hierarchical cluster analysis
DBSCAN
Cluster algorithms
Evaluation and improvement (approx. 2 days)
Model selection and model evaluation
Tuning the hyperparameters of an estimator
Cross-validation
Grid search
Evaluation metrics
Classification
Project work (approx. 3 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.
Furthermore, you can analyse, visualize and manage data. You also understand the use of dashboards and text mining.
You also have relevant knowledge of machine learning. You know the most important reasons for using machine learning, areas of application and the various categories and concepts of machine learning. You will round off your knowledge with skills in evaluation and improvement.
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 in numerous areas of application: the independent development of suitable spam filters for the internet, the creation of precise forecasts of stock levels in supply chain management or the development of purchase forecasts for individual customers or customer segments in marketing. Employees who are qualified in the field of machine learning can be deployed across all industries and are therefore in high demand on the job market.
As companies also have to manage and structure ever-increasing volumes of data in order to evaluate and target their business processes, data analysis skills are in demand in all sectors.
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).