Customer Data Analyst

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

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

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

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

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

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