Customer Data Analyst

As a Customer Data Analyst, you will carry out data-based and systematic studies on the topics of customer data and behavior, on the basis of which customer-related processes are generated with regard to profitability and a more effective customer approach. The course provides the relevant specialist knowledge for this: Customer relationship management using CRM software, statistical methods and experimental design are covered, as well as the Python programming language, which is particularly suitable for data evaluation and visualization, and knowledge of data warehouse modelling and the ETL process, data analysis, visualization and data management. You will also be introduced to the use of artificial intelligence in this area.
  • Certificates: Customer Data Analyst" certificate
  • Additional Certificates: Certificate "Customer service with CRM"
    Statistics" certificate
    Certificate "Relational Databases SQL"
    Certificate "PCEP™ - Certified Entry-Level Python Programmer"
    Data Engineer" certificate
    Data Analytics" certificate
  • Examination: Praxisbezogene Projektarbeiten mit Abschlusspräsentationen
    Certified Entry-Level Python Programmer (PCEP™) (in englischer Sprache)
  • Teaching Times: Full-time
    Monday 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.)
  • Language of Instruction: German
  • Duration: 24 Weeks

Customer service with CRM

Basics & Strategic CRM (approx. 2 days)

CRM as a strategic corporate function

Companyblueprint: company processes

CRM vs. pure software solution


Business models & target group analysis (approx. 1 day)

Business models in the CRM environment

Market and target group definition

Personas for a differentiated customer approach

Sales channels in strategic CRM


Customer worlds & individual relationships (approx. 1 day)

Definition of the customer world

Customer journey, customer experience

Customer needs-demands-motives


ERP & operational CRM (approx. 1 day)

Resource planning and business contexts

CRM in the value chain

Synergy between sales, marketing & service


Data protection & GDPR (approx. 1 day)

Data protection

GDPR in marketing

DSGVO practical cases


CRM software systems (approx. 2 days)

Introduction to CRM systems

Implementation of software

Mapping processes, automating workflows


Artificial intelligence (AI) in CRM (approx. 1 day)

Presentation of specific AI technologies

Predictive analytics

Sentiment analytics

AI humanizers for customer loyalty


Analytical CRM (approx. 2 days)

KPIs for measuring success

Data mining, OLAP

SWOT analysis in CRM

Drill-down analysis


Customer Relationship Cycle & Customer Satisfaction (approx. 2 days)

Customer Relationship Cycle

Satisfaction management: NPS, CSAT, KANO & parameters for optimization


Customer acquisition, customer loyalty & increasing profitability (approx. 2 days)

Account-Based Marketing (ABM)

Strategic acquisition processes

Loyalty programs

Increase profitability


Customer communication as a relationship guarantor (approx. 2 days)

Conversational skills and empathy in customer contact

Psychology of customer relationships

De-escalation techniques for critical customer moments

How authentic communication creates long-term relationships


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 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

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)

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.

English language skills for the Python certification exam are required.

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).

We will gladly advise you free of charge. 0800 3456-500 Mon. - Fri. from 8 am to 5 pm
free of charge from all German networks.
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We will gladly advise you free of charge. 0800 3456-500 Mon. - Fri. from 8 am to 5 pm free of charge from all German networks.