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Certificates: Certified Business Manager" certificate
Certificate "Big Data Analyst" -
Additional Certificates: Data Engineer" certificate
Data Analytics" certificate
Certificate "Big Data Specialist" -
Examination: Praxisbezogene Projektarbeiten mit Abschlusspräsentationen
Certified Business Manager:in -
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
Management and corporate planning
Management (approx. 1 day)
Hierarchy levels
Management methods
Planning horizons
Management functions
Management control loop
Management instruments
Objectives, methods and instruments of business plan preparation (approx. 3 days)
Basics of business plan preparation
Analysis and planning tools (SWOT analysis, scenario analysis, portfolio analysis,
creativity techniques)
Use of AI for brainstorming and structuring business models
AI-supported creation and revision of business plan components
Contract law (approx. 1 day)
Declarations of intent
Warranty and guarantee
Types of contract and their disruptions
Law on contractual defects (subsequent performance, withdrawal, purchase price reduction, compensation)
General terms and conditions (AGB)
Commercial and company law (approx. 1 day)
Types of merchants
company forms
Commercial practices
Special features of commercial law
Commercial register and publicity
Introduction to insolvency law
Project planning (approx. 0.5 days)
basics
Resource planning
Planning steps
Errors in project planning
Environment analysis (approx. 0.5 days)
Company analysis
Industry analysis
Location analysis
Competition analysis
Use of AI to support market and competition analyses
Marketing (approx. 3 days)
Market analysis and market segmentation
Basics of market research
Instruments of the marketing mix
Advertising and supporting instruments
Use of AI to develop marketing ideas and address target groups
AI-supported creation of marketing content
Financial planning (approx. 1 day)
Overview of
Instruments
Planning investment calculation (approx. 2 days)
Basics
Investment calculation methods
Limits and problems of capital budgeting methods
Key figures
Financial planning (approx. 1 day)
Basics
Equity and debt financing
External and internal financing
Key figures
Controlling (approx. 1 day)
Tasks and objectives of controlling
Areas of controlling
Controlling instruments
Project work, certification preparation and certification examination (approx. 5 days)
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
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 will also know how different business plans for projects and companies are created from an economic perspective and apply the most important economic planning and marketing tools. You use artificial intelligence specifically to support analysis and planning processes and critically evaluate their results. You also have knowledge of contract law and are familiar with the content of commercial and company law as well as the basics of financial planning.
The course is aimed at people with a degree in computer science, business informatics, business administration, mathematics or comparable qualifications.
The course is also aimed at anyone who would like to acquire basic business management and planning knowledge or deepen their knowledge.
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.
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).