Big Data Analyst with ITIL® Foundation (Version 5) and PRINCE2® Project Management Foundation (Version 7)
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Certificates: Certificate "Big Data Analyst"
Certificate "ITIL® Foundation (Version 5)"
Certificate "PRINCE2® Project Management Foundation (Version 7)" -
Additional Certificates: Data Engineer" certificate
Data Analytics" certificate
Certificate "Big Data Specialist" -
Examination: Praxisbezogene Projektarbeiten mit Abschlusspräsentationen
ITIL® Foundation (Version 5) (Prüfungsvoucher im Kurs enthalten)
PRINCE2® Project Management Foundation (Version 7) (Prüfungsvoucher im Kurs enthalten) -
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: 16 Weeks
Data Engineer
Fundamentals of Data Engineering and Data Architectures (approx. 2 days)
Tasks and Application Areas in Data Engineering
Fundamentals of Modern Data Architectures and Data Platforms
Fundamentals and Comparison of Cloud-Based Data Architectures
OLTP and OLAP
Processing Structured, Semi-Structured, and Unstructured Data
Requirements Management and Data Modeling (approx. 1 day)
Requirements for Data and Data Sources
Conceptual Data Modeling
Modeling Data Structures and Data Flows
Data Contracts, Data Governance, and Data Lineage
Databases and Data Processing (approx. 1 day)
Database System Architecture
SQL vs. NoSQL vs. NewSQL: A Comparison
Relational Data Modeling
SQL for Querying and Processing Data
Data Warehousing and Modern Data Architectures (approx. 2 days)
Fundamentals and Architecture of Data Warehouse Systems
Dimensional Data Modeling and Schema Design
Facts, Dimensions, and Historical Data Storage
Comparing Data Warehouses, Data Lakes, and Lakehouses
Introduction to dbt
Practical Implementation of Data Models
Data Integration and Data Pipelines (approx. 5 days)
Data Integration and Data Provisioning
ETL and ELT Processes
Design and Orchestration of Data Pipelines
Data Cleaning, Validation, and Data Quality
Batch and Streaming Processing
Practical Application of Airflow and Spark
Programming and Automated Data Processing (approx. 4 days)
Python Environments and Package Management
Data Processing and Transformation with Python
Automation of data processing workflows
Connecting to Different Data Sources and Interfaces
Development of data engineering workflows
Containerization with Docker
AI-powered support in the development and data processing process
Project work (approx. 5 days)
To consolidate the content learned
Presentation of the project results
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
Big Data Specialist
What is Big Data? (approx. 1 day)
Volume, Velocity, Variety, Value, Veracity
Opportunities and risks of large amounts of data
Differentiation: business intelligence, data analytics, data science
Introduction to data mining
Role of AI and data-driven systems in the big data environment
Introduction to big data frameworks (approx. 2 days)
Big data solutions in the cloud (overview of AWS, Azure, GCP)
Data access patterns
Data storage
Introduction to data lakes and data warehouses
Overview of Apache Hadoop and Spark
Distributed data processing with Spark (approx. 3 days)
Basics of distributed systems
Apache Spark (Core and SQL)
Comparison of different approaches to data processing
Processing large amounts of data
Introduction to simple ML workflows with Spark
Data pipelines and data integration (approx. 2 days)
ETL and ELT processes
Batch vs. streaming processing
Basics of data pipelines
Introduction to orchestration (e.g. Airflow overview)
Data quality and preparation
Components (approx. 2 days)
Brief presentation of various tools
Data transfer
Overview of resource management in big data systems
Hadoop ecosystem
Apache Spark deepening
Introduction to streaming technologies
NoSQL and data storage (approx. 2 days)
CAP theorem
ACID and BASE
Types of databases
HBase
Introduction to document-oriented databases
Introduction to storage formats
Overview of data lakehouse approaches
Big Data Visualization (approx. 2 days)
Theories of visualization
Diagram selection
New types of diagrams
Tools for data visualization
Introduction to BI tools (e.g. Power BI, Tableau)
Basics of data-driven decision making
Data governance and data protection (approx. 1 day)
Basics of the GDPR in the data context
Data ethics and responsible handling of data
Data quality and governance concepts
Access controls and security
Fundamentals of responsible AI use
Project work (approx. 5 days)
To consolidate the content learned
Presentation of the project results
ITIL® Foundation (Version 5)
Important ITIL terms and definitions (approx. 2 days)
Management of digital products and services
Products, services and service offerings
Value creation and service relationships
Service consumers, service providers, sponsors, customers and users
Service quality and service level agreements (SLA)
Utility, warranty, user experience and sustainability
ITIL product and service lifecycle
Continual improvement
The four dimensions of ITIL product and service management (approx. 1 day)
Organizations and people
Partners and suppliers
Information and technology
Value streams and processes
Holistic approach and external influencing factors
The ITIL product and service lifecycle (approx. 1 day)
Discover, Design, Acquire and Build
Transition, Operate, Deliver and Support
Value creation in the product and service lifecycle
Iterative and non-linear use of the lifecycle
The ITIL Value System (approx. 2 days)
Components of the ITIL Value System and ITIL basic principles
Governance, value chain and operating model
Management practices, practice guidelines and continuous improvement
Value orientation, collaboration and optimization
Service operations, releases and problem management
Continuous integration, continuous delivery and continuous deployment
Site reliability engineering (SRE) and observability
Metrics and Critical Success Factors (CSF)
Value stream identification, mapping and management (approx. 1 day)
Value streams and value stream management
Main value streams and supporting value streams
Complexity thinking and workflow optimization
Value stream mapping
ITIL and AI (approx. 0.5 days)
Artificial intelligence (AI) and AI maturity
Generative AI (GenAI) and Agentic AI
AI in the product and service lifecycle
AI governance
ITIL and other frameworks (approx. 0.5 days)
ITIL and DevOps
ITIL and PRINCE2
Project management in the product and service lifecycle
Project work, certification preparation and certification examination (approx. 3 days)
PRINCE2® Project Management Foundation (Version 7)
Introduction to project management based on PRINCE2® (approx. 1 day)
Definition and characteristics of a project
Project control cycle of project management and the six project dimensions
Challenges in project management - why do projects fail?
Advantages of the PRINCE2® project management method
Customer-supplier environments
Projects in a commercial environment
Structure of the PRINCE2® method and its five integrated building blocks
The management products of PRINCE2®
Digital tools and AI-supported analysis in modern project management
The PRINCE2® basic principles (approx. 1 day)
The seven basic principles of PRINCE2®
Statements and contents of the basic principles
Relationship between the basic principles and the PRINCE2® topics
Adaptation of PRINCE2® to the project environment, taking into account digital working methods
The importance of people for PRINCE2® projects (approx. 1 day)
Change management
Leadership and management
Communication in the project
Effects of digital and AI-supported systems on collaboration and change processes
The seven topics of PRINCE2® (approx. 3 days)
Business case (benefits management approach and sustainability management approach)
Organization (project structure, roles and responsibilities)
Creation of plans
Quality planning and quality control
Risk management using modern analysis methods and data-based evaluations
Issue management
Controlling the progress of the project
The seven PRINCE2® processes (approx. 2 days)
Interaction of the seven PRINCE2® processes in the project process
Activities in the respective PRINCE2® processes
Preparing, steering and initiating a project
Controlling a phase
Managing product delivery
Managing phase transitions
Closing a project
Project work, certification preparation and certification examination (approx. 2 days)
Changes are possible, the course content is updated regularly.
You are proficient in the processes involved in merging, preparing, enriching and forwarding data and understand big data analysis using basic Python programming, SQL and NoSQL database concepts. Knowledge of industry-specific software for processing and structuring large, unstructured data and visualizing it rounds off your knowledge.
You also understand the central concepts of managing digital products and services according to ITIL® Foundation (version 5). You are familiar with the ITIL product and service lifecycle, the ITIL Value System, value streams, value creation and service relationships as well as modern concepts such as AI, automation and continuous improvement and can classify these in an organizational context. You will also be able to work on PRINCE2® projects and be familiar with their processes and terminology. You will also be able to plan and implement IT projects and measure their success.
The course is aimed at people with a degree in computer science, business informatics, business administration, mathematics or comparable qualifications.
A systematic evaluation of data volumes is essential for companies in order to generate information about their own products and customer behavior. Against this backdrop, big data analysts are increasingly in demand across all industries.
With knowledge of IT service and project management with ITIL® and PRINCE2®, you have an additional qualification that is in high demand, especially in the IT sector.
Your meaningful certificate provides a detailed insight into the qualifications you have acquired and improves your career prospects.
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).