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)

Fundamentals of Data Engineering and Data Architectures (approx. 2 days)

Tasks and Application Areas in Data Engineering

Fundamentals of Modern Data Architectures and Data Platforms

Fundamentals and Comparison of Cloud-Based Data Architectures

OLTP and OLAP

Processing Structured, Semi-Structured, and Unstructured Data

Requirements Management and Data Modeling (approx. 1 day)

Requirements for Data and Data Sources

Conceptual Data Modeling

Modeling Data Structures and Data Flows

Data Contracts, Data Governance, and Data Lineage

Databases and Data Processing (approx. 1 day)

Database System Architecture

SQL vs. NoSQL vs. NewSQL: A Comparison

Relational Data Modeling

SQL for Querying and Processing Data

Data Warehousing and Modern Data Architectures (approx. 2 days)

Fundamentals and Architecture of Data Warehouse Systems

Dimensional Data Modeling and Schema Design

Facts, Dimensions, and Historical Data Storage

Comparing Data Warehouses, Data Lakes, and Lakehouses

Introduction to dbt

Practical Implementation of Data Models

Data Integration and Data Pipelines (approx. 5 days)

Data Integration and Data Provisioning

ETL and ELT Processes

Design and Orchestration of Data Pipelines

Data Cleaning, Validation, and Data Quality

Batch and Streaming Processing

Practical Application of Airflow and Spark

Programming and Automated Data Processing (approx. 4 days)

Python Environments and Package Management

Data Processing and Transformation with Python

Automation of data processing workflows

Connecting to Different Data Sources and Interfaces

Development of data engineering workflows

Containerization with Docker

AI-powered support in the development and data processing process

Project work (approx. 5 days)

To consolidate the content learned

Presentation of the project results

Introduction to Data Analytics (approx. 1 day)

Tasks, Areas of Application, and Typical Use Cases of Data Analytics

Data Analytics Processes and CRISP-DM

The Role of the Data Analyst and How It Differs from Data Science, Data Engineering, and Business Intelligence

Classification of Artificial Intelligence and Machine Learning

Python for Data Analytics (approx. 1 day)

Working with Jupyter Notebooks

NumPy and Pandas for Data Analysis

Importing and Exporting Data from Various Sources, e.g., Excel and CSV

Selecting and transforming data

Data Preparation and Exploratory Data Analysis (approx. 3 days)

Data cleaning and transformation with Pandas

Handling missing, incorrect, and outlier data

Merging, grouping, and aggregating data

Exploratory data analysis and deriving initial insights

Fundamentals of statistical analysis

SQL and Data Management (approx. 2 days)

Relational Databases and Data Models

Data Queries and Processing with SQL

Joins, aggregations, and analytical queries

Interaction Between SQL and Python in Data Analysis

Data Quality and Data Protection

Connecting to and Using Different Data Sources and Database Systems

Data Visualization and Data Storytelling (approx. 2 days)

Visualization with Matplotlib, Seaborn, and Plotly

Selecting Appropriate Charts and Visualization Methods

Preparing and interpreting analysis results

Data Storytelling and Communication Tailored to the Audience

Power BI and Interactive Dashboards (approx. 2 days)

Data Import and Preparation with Power Query

Data modeling and relationships

Basics of DAX and metrics

Creating interactive reports and dashboards

Visualization and Communication of Business KPIs

Overview of Alternative Business Intelligence and Visualization Solutions

AI-powered data analysis (approx. 2 days)

Use of generative AI in the data analytics workflow

AI-powered data preparation, analysis, and visualization

Support with Python and SQL queries

Prompting for data analysis and interpretation of results

Critical evaluation of data sources and detection of synthetic and implausible data

Quality assurance, data protection, and responsible use of AI

Business Analytics and Statistical Fundamentals (approx. 2 days)

Key Metrics and KPIs in a Business Context

Descriptive Statistics and Statistical Relationships

Correlation, Causality, and Simple Regression Analyses

Fundamentals of confidence intervals and hypothesis testing

Interpreting Analysis Results and Deriving Data-Driven Recommendations

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)