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Certificates: Certificate "Big Data Engineer"
Certificate "Statistics and data analysis" -
Additional Certificates: Data Engineer" certificate
Certificate "Big Data Specialist" -
Examination: Praxisbezogene Projektarbeiten mit Abschlusspräsentationen
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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
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
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
Statistics and data analysis
Statistical basics (approx. 6 days)
Measurement theory basics (population, sample, sample types, measurement, scale levels)
Univariate descriptive statistics (frequency distributions, central measures, measures of dispersion, standardization, histograms, bar charts, pie charts, line charts, box plots)
Bivariate descriptive statistics (measures of correlation, correlation coefficients, crosstabs, scatter plots, grouped bar charts)
Basics of inductive inferential statistics (probability distributions, normal distribution, sampling distribution of the mean, significance test, null hypothesis test, significance level, effect size, parameter estimation, confidence intervals, error bar charts, power analysis, sample size)
Data preparation and data cleansing with suitable software
Descriptive analysis
Visualization of statistical results
AI-supported analysis and interpretation of statistical results
Methods for comparing two groups (approx. 5 days)
z-test, t-test for one sample
t-test for independent and related samples
Pretest-posttest designs with two groups
Supporting significance tests (Anderson-Darling test, Ryan-Joiner test, Levene test, Bonett test, significance test for correlations)
Nonparametric methods (Wilcoxon test, sign test, Mann-Whitney test)
Contingency analyses (binomial test, Fisher's exact test, chi-square test, cross-tabulations, measures of association)
Interpretation of test results
AI-supported interpretation of results
Basics of regression analysis (approx. 2 days)
Linear regression
Model interpretation
AI-supported model interpretation
Correlation analysis
Methods for comparing the means of several groups (approx. 3 days)
One-factorial and two-factorial analysis of variance (ANOVA)
Post-hoc analyses
Interpretation of group differences
Multi-factorial analysis of variance (general linear model)
Fixed, random, crossed and nested factors
Multiple comparison methods (Tukey-HSD, Dunnett, Games-Howell)
Interaction analysis
Power analysis for variance analyses
Introduction to Design of Experiments (DoE) (approx. 1 day)
Full factorial and partial factorial experimental designs
Project work (approx. 3 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. You can also process large, unstructured data volumes with the help of industry-specific software. You have knowledge of the Apache framework and know how to visualize data in an appealing way.
You will also understand the basics of statistics, be able to process and evaluate data and present, explain and interpret statistical data analyses and results using graphics.
The course is aimed at people with a degree in computer science, business informatics, business administration, mathematics or comparable qualifications.
Big data is used in companies for the interdisciplinary analysis and design of IT solutions in collaboration with development and operations teams. Big Data Engineers are in demand from both large and medium-sized companies in industry, trade, services and finance.
A sound knowledge of statistics is a valuable additional qualification that is in great demand in industrial research and development, in drug development, in the supervision of medical studies, in finance and insurance, in information technology or in public administration.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).