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Certificates: Certificate "PCEP™ - Certified Entry-Level Python Programmer"
Certificate "Relational Databases SQL"
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
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
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
Troubleshooting (approx. 0.5 days)
try, except
Error types
Intercepting program interruptions
Error forwarding between functions
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 and BCNF
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 (SQL)
Forms and reports in modern DBMS
Circular reference and dependency management
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 and relationships
Backup and restore
Artificial intelligence (AI) in the work process
Presentation of specific AI technologies
and possible applications in the professional environment
Introduction to DDL (Data Definition Language) (approx. 8 days)
SQL basics and advanced syntax
Creating tables and defining constraints
Operators and function definitions
Queries and manipulation of data
Error handling and transaction management
DCL - Data Control Language and Security (approx. 1 day)
User administration and authorizations
Roles, authorizations and auditing
Data types, data import and export in modern systems (approx. 1 day)
Data import and export
Modern data types
Project work (approx. 5 days)
To consolidate the content learned
Presentation of the project results
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 of dashboards - Dash components
Customizing 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
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 able to use the programming language with its classes, libraries and functions with confidence.
You will also be able to build and manage relational databases with SQL, create views and execute complex queries, including using SQL functions. The course is taught on the Microsoft SQL server using the Microsoft SQL Server Management Studio.
You are also proficient in processes relating to the consolidation, preparation, enrichment and forwarding of data.
Furthermore, you can analyse, visualize and manage data. You also understand the use of dashboards and text mining.
The course is aimed at people with a degree in computer science, business informatics, mathematics, business administration or comparable qualifications.
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 are the interface between the specialist and IT departments. As more and more large and medium-sized companies are using data analysis, they are in demand in industry and commerce as well as in the service and finance sectors.
As companies also have to manage and structure ever-increasing volumes of data in order to evaluate and target their business processes, data analysis skills are in demand in all sectors.
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).