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Certificates: Data Manager" certificate
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Additional Certificates: Certificate "AWS Certified Cloud Practitioner"
Statistics" certificate
Certificate "Relational Databases SQL"
Python" certificate -
Examination: Practical project work with final presentations
AWS certification exam CLF-C02 -
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
AWS Cloud Administrator
Cloud concepts (approx. 3.5 days)
Advantages of the AWS Cloud
Principles of the AWS Cloud design
Migration to the AWS Cloud
Concepts of cloud economics
Artificial intelligence (AI) in the work process
Presentation of specific AI technologies
and possible applications in the professional environment
Security and compliance (approx. 4.5 days)
AWS model as shared responsibility
AWS cloud security, governance and compliance concepts
AWS access management functions
Components and resources for security support
Cloud technology and services (approx. 5 days)
Methods for deployment and operation in the AWS Cloud
Global AWS infrastructure
AWS computing services, database services, network services, and storage services
AWS service for artificial intelligence and machine learning as well as analytics services
Services from other covered AWS services categories
Invoicing, pricing and support (approx. 2 days)
Comparison of AWS pricing models
Resources for billing, budget and cost management
AWS technical resources and support options
Project work, certification preparation and certification exam (approx. 5 days)
AWS Certified Cloud Practitioner CLF-C02
Statistics
Statistical basics (approx. 6 days)
Measurement theory basics (population and sample, sample types, measurement and scale levels)
Univariate descriptive statistics (frequency distributions, central measures, measures of dispersion, standard value, histograms, bar charts, pie charts, line charts and box plots)
Bivariate descriptive statistics (measures of correlation, correlation coefficients, crosstabs, scatter plots and grouped bar charts)
Basics of inductive inferential statistics (probability distribution, normal distribution, mean value distribution, significance test, Fisher's null hypothesis test, effect size, parameter estimation, confidence intervals, error bar charts, power analyses and determining the optimum sample size)
Artificial intelligence (AI) in the work process
Presentation of specific AI technologies
and possible applications in the professional environment
Methods for comparing two groups (approx. 5 days)
z- and t-test for a sample (deviation from a specified value)
t-test for the mean difference between two independent/connected samples
Testing the effectiveness of actions, measures, interventions and other changes with t-tests (pretest-posttest designs with two groups)
Supporting significance tests (Anderson-Darling test, Ryan-Joiner test, Levene test, Bonnet 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 with measures of association)
Methods for comparing the means of several groups (approx. 5 days)
One- and two-factorial analysis of variance (simple and balanced ANOVA)
Multi-factorial analysis of variance (general linear model)
Fixed, random, crossed and nested factors
Multiple comparison methods (Tukey-HSD, Dunnett, Hsu-MCB, Games-Howell)
Interaction analysis (analysis of interaction effects)
Selectivity and power analysis for variance analyses
Introduction to Design of Experiments (DoE) (approx. 1 day)
Full 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 with Access (approx. 3 days)
Redundant data
Data integrity
Normalization
BCNF
DB design
Relationship 1:n, m:n
data types
tables
Primary and foreign keys
Referential integrity
Relationships between relations
Entity relationship model
Index, default value
Restrictions (check)
Queries
Forms, reports
Circular reference
Introduction to SQL Server Management Studio (SSMS) (approx. 2 days)
Overview of
Physical DB design
Creating tables
Data types in MS SQL
Primary Key
Restrictions, default values, diagram, 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 (approx. 8 days)
SQL basics
syntax
Commands
Multiple tables
Operators
Flow control
Scalar value functions
Table value functions
System functions
Procedures with and without parameters
Error types
Transactions, locks, DeadLock
DCL - Data Control Language (approx. 1 day)
Logins
User learning
roles
Authorizations
Data types, data import and export (approx. 1 day)
Data type geography
Data export, data import
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
First steps with Python (approx. 5 days)
Numbers
Strings
Date and time
Standard input and output
list, tuple dict, set
Branches and loops (if, for, while)
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, recursion
Functional programming
Troubleshooting (approx. 0.5 days)
try, except
Intercept program interruptions
Object-oriented programming (approx. 4.5 days)
Python classes
Methods
Immutable objects
Data class
Inheritance
Graphical user interface (approx. 1 day)
Buttons and text fields
Grid layout
File selection
Project work (approx. 3 days)
To consolidate the content learned
Presentation of the project results
Changes are possible. The course content is updated regularly.
After this course, you will have the specialist knowledge required to operate in the cloud. You will also have the technical experience required to provision, secure and automate environments regardless of the provider's solution. With statistics and SQL, you have mastered two essential tools for processing, presenting and analyzing data. Knowledge of Python completes your profile in the field of data management.
The course is aimed at IT specialists, programmers, engineering and business administration graduates as well as business people and specialists with relevant professional experience.
The field of data management is becoming increasingly important across all industries. Data management skills are not only needed in IT and data analysis, but are also in demand in industry, trade, services and finance.
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).