Data analyst and AI manager

Statistical basics (approx. 6 days)

Measurement theory basics (population, sample, sample types, measurement, scale levels)

Univariate descriptive statistics (frequency distributions, central measures, measures of dispersion, standardization, histograms, bar charts, pie charts, line charts, box plots)

Bivariate descriptive statistics (measures of correlation, correlation coefficients, crosstabs, scatter plots, grouped bar charts)

Basics of inductive inferential statistics (probability distributions, normal distribution, sampling distribution of the mean, significance test, null hypothesis test, significance level, effect size, parameter estimation, confidence intervals, error bar charts, power analysis, sample size)

Data preparation and data cleansing with suitable software

Descriptive analysis

Visualization of statistical results

AI-supported analysis and interpretation of statistical results

Methods for comparing two groups (approx. 5 days)

z-test, t-test for one sample

t-test for independent and related samples

Pretest-posttest designs with two groups

Supporting significance tests (Anderson-Darling test, Ryan-Joiner test, Levene test, Bonett 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, measures of association)

Interpretation of test results

AI-supported interpretation of results

Basics of regression analysis (approx. 2 days)

Linear regression

Model interpretation

AI-supported model interpretation

Correlation analysis

Methods for comparing the means of several groups (approx. 3 days)

One-factorial and two-factorial analysis of variance (ANOVA)

Post-hoc analyses

Interpretation of group differences

Multi-factorial analysis of variance (general linear model)

Fixed, random, crossed and nested factors

Multiple comparison methods (Tukey-HSD, Dunnett, Games-Howell)

Interaction analysis

Power analysis for variance analyses

Introduction to Design of Experiments (DoE) (approx. 1 day)

Full factorial 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

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 in SQL

Structured data as the basis for AI-supported analysis methods

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 (ERM) and relationships

Backup and restore

Introduction to performance monitoring

Overview of AI-supported query optimization and query analysis

Introduction to DDL (Data Definition Language) and DML (Data Manipulation Language) (approx. 8 days)

SQL basics and extended syntax

Operators and integrated functions

Queries and manipulation of data

Error handling and transaction management

Creation and administration of database objects

Basics of performance optimization

Working with modern data types

Data modeling and structured preparation for AI and analysis applications

DCL - Data Control Language and Security (approx. 1 day)

User administration and authorizations

Roles and security concepts

Auditing

Introduction to Row Level Security

Data security in the context of AI-supported evaluations

Data types, data import and export in modern systems (approx. 1 day)

Data import and export

Modern data types

Import, transformation and provision of data for analysis processes

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

Lexis, semantics

PEP-8 conventions

Interpreter vs. compiler

Numeral systems: binary, octal, hexadecimal

Scientific Notation

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

String basics: escaping, multiline strings

Prioritizing and binding 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

Parameter types: positional, keyword, mixed

Default values

Shadowing and global keyword

None and return without value

Troubleshooting (approx. 0.5 days)

Basics of error handling with try and except

Typical error types and exception hierarchy

Error propagation and program interruptions

Structuring the except blocks

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)

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 and customizing of 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

Fundamentals of operational AI projects (approx. 5 days)

Introduction to AI, ML, DL, NLP and computer vision (operational focus)

Roles and tasks: Setting up, operating and reviewing the effectiveness of the management system in accordance with ISO 42001

Role delineation and collaboration: AI officer, AI manager and AI auditor

Identification and evaluation of operational use cases in the company

Project initiation: target definition, scope, feasibility analysis

Stakeholder management

Value creation and ROI through AI

Successful AI initiatives in management

Data management and use of tools (approx. 3 days)

Data preparation, quality and integration

Selection and implementation of AI tools and platforms

Practical prompting for text, image and video applications

Building simple data pipelines

Introduction to MLOps concepts

AI automation options in operation

Model training, validation and use (approx. 2 days)

Training and validation of models

Test procedures: Black box, white box, unit tests

Use of models

Monitoring and iterative optimization

Integration of AI agents in projects

Risk management and quality assurance (approx. 2 days)

Technical risk analysis: bias metrics, fairness tests, model error analysis

Quality assurance: KPIs, monitoring, acceptance processes

Management system according to ISO 42001

Security and explainability of AI systems

Operational project management and agile methods (approx. 2 days)

Agile methods: Scrum, Kanban, iterative deployment cycles

Resource and budget planning

Team and stakeholder communication

Ongoing optimization and problem-solving strategies (CIP)

Cooperation with external partners

Organizational development, governance and change management (approx. 3 days)

Analysis of business processes

Maturity level analysis, GAP analysis

Creation of an AI roadmap

AI governance and strategy development

Development of a sustainable organizational structure

Responsibilities

Practical handling of resistance in AI operations

Sustainability and corporate digital responsibility (CDR)

Project work, certification preparation and certification exam "AI Manager with TÜV Rheinland certified qualification" (approx. 3 days)