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Certificates: Statistics" certificate
Certificate "Big Data Analyst" -
Additional Certificates: Data Engineer" certificate
Data Analytics" certificate
Certificate "Big Data Specialist" -
Examination: Practical project work with final presentations
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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: 16 Weeks
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
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
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
What is data mining?
Introduction to Apache Frameworks (approx. 2 days)
Big data solutions in the cloud
Data access patterns
Data storage
MapReduce (approx. 3 days)
MapReduce philosophy
Hadoop Cluster
Chaining of MapReduce jobs
Artificial intelligence (AI) in the work process
Presentation of specific AI technologies
and possible applications in the professional environment
Components (approx. 3 days)
Brief presentation of various tools
Data transfer
YARN applications
Hadoop JAVA-API
Apache Spark
NoSQL and HBase (approx. 3 days)
CAP theorem
ACID and BASE
Types of databases
HBase
Big Data Visualization (approx. 3 days)
Theories of visualization
Diagram selection
New types of diagrams
Tools for data visualization
Project work (approx. 5 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 and understand big data analysis using basic Python programming, SQL and NoSQL database concepts. Knowledge of industry-specific software for processing and structuring large, unstructured data and visualizing it rounds off your knowledge.
After this course, you will 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.
A systematic evaluation of data volumes is essential for companies in order to generate information about their own products and customer behavior. Against this backdrop, big data analysts are increasingly in demand across all industries.
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).