Data Analyst with ITIL® Foundation (Version 5) and PRINCE2® Project Management Foundation (Version 7)
Data analysts check and process data sets using relational databases and the Python programming language, evaluate them using statistical methods and visualize the results clearly. The course therefore begins by explaining statistical tools for analyzing various data groups, followed by specialist knowledge of programming with Python and developing and querying relational databases with SQL. Knowledge of data warehouse modelling and the ETL process as well as data analysis, visualization and data management round off the course. You will also expand your knowledge with ITIL®, a process optimization method, and PRINCE2®, an IT project management method.
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Certificates: Data Analyst" certificate
Certificate "ITIL® Foundation (Version 5)"
Certificate "PRINCE2® Project Management Foundation (Version 7)" -
Additional Certificates: Certificate "Statistics and data analysis"
Certificate "Relational Databases SQL"
Certificate "PCEP™ - Certified Entry-Level Python Programmer"
Data Engineer" certificate
Data Analytics" certificate -
Examination: Praxisbezogene Projektarbeiten mit Abschlusspräsentationen
ITIL® Foundation (Version 5) (Prüfungsvoucher im Kurs enthalten)
PRINCE2® Project Management Foundation (Version 7) (Prüfungsvoucher im Kurs enthalten)
Certified Entry-Level Python Programmer (PCEP™) (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: 24 Weeks
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
Relational databases with SQL
Basics of database systems and SQL (approx. 3 days)
Overview of database systems and models
Redundant data and data integrity
Normalization
Database design and entity relationship model (ERM)
Primary and foreign keys
Relationships between relations
Data types in SQL
Indexes and performance
Constraints and validation
Queries in SQL
Structured data as the basis for AI-supported analysis methods
Introduction to SQL Server Management Studio (SSMS) (approx. 2 days)
Overview of SQL Server and SSMS
Physical database design
Creating tables and defining data types
Constraints, default values and relationships
Database diagrams (ERM) and relationships
Backup and restore
Introduction to performance monitoring
Overview of AI-supported query optimization and query analysis
Introduction to DDL (Data Definition Language) and DML (Data Manipulation Language) (approx. 8 days)
SQL basics and extended syntax
Operators and integrated functions
Queries and manipulation of data
Error handling and transaction management
Creation and administration of database objects
Basics of performance optimization
Working with modern data types
Data modeling and structured preparation for AI and analysis applications
DCL - Data Control Language and Security (approx. 1 day)
User administration and authorizations
Roles and security concepts
Auditing
Introduction to Row Level Security
Data security in the context of AI-supported evaluations
Data types, data import and export in modern systems (approx. 1 day)
Data import and export
Modern data types
Import, transformation and provision of data for analysis processes
Project work (approx. 5 days)
To consolidate the content learned
Presentation of the project results
Programming with Python
Python basics (approx. 1 day)
History, concepts
Usage and areas of application
syntax
Lexis, semantics
PEP-8 conventions
Interpreter vs. compiler
Numeral systems: binary, octal, hexadecimal
Scientific Notation
First steps with Python (approx. 5 days)
Numbers
Strings
Date and time
Standard input and output
Numeric operators
Comparison, logical and bitwise operators
Data type conversion
list, tuple, dict, set
List functions and methods
Branching and loops (if, for, while)
Member operators
String basics: escaping, multiline strings
Prioritizing and binding operators
Artificial intelligence (AI) in the work process
Presentation of specific AI technologies
and possible applications in the professional environment
Functions (approx. 5 days)
Define your own functions
Variables
Parameters and arguments
Return values
Recursion
Namespaces
Functional programming
Parameter types: positional, keyword, mixed
Default values
Shadowing and global keyword
None and return without value
Troubleshooting (approx. 0.5 days)
Basics of error handling with try and except
Typical error types and exception hierarchy
Error propagation and program interruptions
Structuring the except blocks
Object-oriented programming (approx. 4.5 days)
Python classes
Methods
Immutable objects
Data class
Inheritance
Project work, certification preparation and certification exam "PCEP™ - Certified Entry-Level Python Programmer" in English (approx. 4 days)
Data Engineer
Basics of Business Intelligence (approx. 2 days)
Fields of application, dimensions of a BI architecture
Basics of business intelligence, OLAP, OLTP, tasks of data engineers
Data Warehousing (DWH): handling and processing of structured, semi-structured and unstructured data
Requirements management (approx. 2 days)
Tasks, objectives and procedures in requirements analysis
Data modeling, introduction/modeling with ERM
Introduction/modeling in UML
- Class diagrams
- Use case analysis
- Activity diagrams
Artificial intelligence (AI) in the work process
Presentation of specific AI technologies
and possible applications in the professional environment
Databases (approx. 3 days)
Basics of database systems
Architecture of database management systems
Application of RDBMS
Implementation of data model in RDBMS, normal forms
Practical and theoretical introduction to SQL
Limits of relational databases, csv, json
Data Warehouse (approx. 4 days)
Star Schema
Data modeling
Creation of Star Schema in RDBMS
Snowflake Schema, basics, data modeling
Creation of Snowflake Schema in RDBMS
Galaxy Schema: Basics, data modeling
Slowly Changing Dimension Tables Type 1 to 5 - Restating, Stacking, Reorganizing, mini Dimension and Type 5
Introduction to normal, causal, mini and monster, heterogeneous and sub dimensions
Comparison of state and transaction oriented
Fact tables, density and storage from DWH
ETL (approx. 4 days)
Data Cleansing
- Null Values
- Preparation of data
- Harmonization of data
- Application of regular expressions
Data Understanding
- Data validation
- Statistical data analysis
Data protection, data security
Practical structure of ETL routes
Data Vault 2.0, basics, hubs, links, satellites, hash key, hash diff.
Data Vault data modeling
Practical structure of a Data Vault model - Raw Vault, practical implementation of hash procedures
Project work (approx. 5 days)
To consolidate the content learned
Presentation of the project results
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 and customizing of 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
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.
After this course, you will have essential knowledge of statistics, be able to carry out complex queries using relational databases with SQL and be proficient in the Python programming language. Combined with the specialist knowledge of data engineering and data analysis taught in the course, you will be able to manage extensive data sets, evaluate them statistically efficiently and summarize the results in a clear and easy-to-understand way.
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 business administration, mathematics or (business) informatics and comparable qualifications.
As companies have to manage and structure ever-increasing volumes of data to evaluate and set objectives for their business processes, data analysis skills are in demand in all sectors.
With knowledge of ITIL® and PRINCE2®, you will be able to work as a project manager or project team member. In the IT sector in particular, your career prospects will increase, for example in IT management or as a project manager.
The original "ITIL® Foundation" and "PRINCE2® Foundation Level" certificates provide meaningful proof of your newly acquired knowledge.
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).