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Certificates: Certificate "PCEP™ - Certified Entry-Level Python Programmer"
"Relational Databases – SQL" Certificate
Data Engineer" certificate
Data Analytics" certificate -
Examination: Praxisbezogene Projektarbeiten mit Abschlusspräsentationen
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: 16 Weeks
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)
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
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
Changes are possible, the course content is updated regularly.
After the course, you will have a compact, basic knowledge of programming with Python. You will be confident in using the programming language with its classes, libraries and functions.
You can also develop and manage relational databases with SQL. You will create tables and views, formulate queries and process data with suitable SQL commands. You will pay attention to data integrity, use transactions and assign user rights in SQL Server. You will also prepare structured data for analysis and AI-supported evaluations.
In addition, you have knowledge of data modeling, integration, preparation, and automated processing, and you can design and implement modern data processes.
In addition, you can process and analyze data using Python and SQL, create meaningful visualizations and interactive dashboards with Power BI, and interpret and communicate analysis results in a way that is tailored to your audience. You can use generative AI specifically to support data analysis.
This course is designed for individuals with a business, technical, or IT background who want to analyze and visualize data and use it to make data-driven decisions.
The versatility of Python makes employees with the relevant skills attractive in numerous industries and companies. People with programming skills in Python are particularly sought after in web development, machine learning and data analysis.
By creating and developing databases, companies ensure efficient sorting, logical structuring and permanent documentation of important data. Additional knowledge as an SQL database specialist or administrator will complement your knowledge accordingly and round off your profile.
Data engineers develop and maintain data structures and processes that integrate and process data from various sources and make it available for analysis and other applications. As data-driven business processes become increasingly important, these skills are in demand across all industries.
In addition, data-driven decisions are playing an increasingly important role in companies and organizations. Accordingly, there is a need for professionals who can process, analyze, and clearly visualize data from various sources. Knowledge of Python, SQL, and business intelligence opens up career opportunities in the field of data analytics as well as in data-driven functional and business areas.
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).