Customer Data Analyst

Free of cost

by funding

The course explains the analysis and optimization of customer relationships, programming with Python, statistics, relational databases with SQL as well as specific expertise in data engineering and data analysis. You will also learn how artificial intelligence is used in your profession.
  • Certificates: Customer Data Analyst" certificate
  • 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
  • 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 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).

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.
Contact
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.