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Certificates: Customer Data Analyst" certificate
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Additional Certificates: Certificate "Customer service with CRM"
Statistics" certificate
Certificate "Relational Databases SQL"
Python" certificate
Data Engineer" certificate
Data Analytics" certificate -
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: 24 Weeks
Customer service with CRM
Basics of Customer Relationship Management (approx. 3 days)
Introduction to Customer Relationship Management
Strategic, analytical, operational CRM
Integrated CRM solutions: ERP system, data warehouse, data mining and OLAP
Data protection basics (approx. 1 day)
Dealing with customer data
Storage and forwarding of customer data
Data protection in the area of marketing/advertising
Artificial intelligence (AI) in the work process
Presentation of specific AI technologies
and possible applications in the professional environment
Acquiring and retaining customers (approx. 4 days)
Analysis of customer needs
Customer satisfaction management
Customer communication
Customer experience (CX)
Psychology of customer relationships
Development and maintenance of customer databases
360 degree customer view
Holistic case management
Dealing with customer data (approx. 4 days)
Managing appointments, contracts and budgets
Customer administration
Workflows between teams
Cleaning up the database
Analytical CRM (target group analysis, customer value analysis, forecasts)
Real-time dashboards
Overview of key performance indicators
Drill-down analysis
Inline data visualization
Evaluation of sales opportunities
Increasing customer profitability (approx. 3 days)
marketing
Targeted feedback
Segmentation tools
Campaign management
Workflows
Lead-to-cash transparency
Real-time sales forecasting
Pipeline reports
Introduction to CRM software (approx. 2 days)
Overview of the CRM system landscape
Presentation and positioning of various CRM systems
Mapping process flows
Project work (approx. 3 days)
To consolidate the content learned
Presentation of the project results
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
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 be able to analyze and optimize customer relationships. You will also have a compact, basic knowledge of programming with Python. With statistics and SQL, you will have mastered two essential tools for processing, displaying and analyzing data. 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.
The course is aimed at people with a degree in business administration, mathematics or (business) informatics or people with comparable qualifications who deal with data analysis in the customer segment.
As a Customer Data Analyst, you will work in a wide range of industries and companies, such as marketing and telecommunications, e-commerce companies, retail, financial services and technology companies.
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).