Data Scientist with ITIL® Foundation (Version 5) and PRINCE2® Project Management Foundation (Version 7)

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

Introduction to Machine Learning (approx. 5 days)

Why machine learning?

Application examples

Supervised learning, unsupervised learning, partially supervised learning, reinforcement learning

Examples of data sets

Getting to know data

Training, validation and test data

Viewing data

Making predictions

Supervised learning (approx. 5 days)

Classification and regression

Generalization, overfitting and underfitting

Size of the data set

Algorithms for supervised learning

Linear models

Bayes classifiers

Decision trees

Random Forest

Gradient Boosting

k-nearest neighbors

Support Vector Machines

Conditional Random Field

Neural Networks and Deep Learning

Probabilities

Unsupervised learning (approx. 5 days)

Types of unsupervised learning

Preprocessing and scaling

Data transformations

Scaling training and test data

Dimension reduction

Feature engineering

Manifold learning

Principal component decomposition (PCA)

Non-negative matrix factorization (NMF)

Manifold learning with t-SNE

Cluster analysis

k-Means clustering

Agglomerative clustering

Hierarchical cluster analysis

DBSCAN

Cluster algorithms

Evaluation and improvement (approx. 2 days)

Model selection and model evaluation

Tuning the hyperparameters of an estimator

Cross-validation

Grid search

Evaluation metrics

Classification

Project work (approx. 3 days)

To consolidate the content learned

Presentation of the project results

Introduction to Deep Learning (approx. 1 day)

Deep learning as a type of machine learning

Fundamentals of neural networks (approx. 4 days)

Multilayer perceptrons

Calculation of neural networks

Optimization of model parameters, backpropagation

Deep learning libraries

Regression vs. classification

Typical loss and activation functions

Evaluating the model prediction with metrics

Regression and classification metrics

Learning curves, overfitting and regularization

Hyperparameter optimization

L1/12 regularization

Dropout

Early stopping

Stochastic gradient descent (SGD)

Momentum, Adam Optimizer

Optimization of the learning rate

Dynamic learning rate adjustment

Reduce Learning Rate on Plateau

Learning rate optimization with the TensorBoard

Controlling the fit process with callbacks

Saving and loading models

Convolutional Neural Network (CNN) (approx. 2 days)

Image classification

Convolutional layers, pooling layers

Reshaping layers, flattening, global-average pooling

CNN architectures ImageNet-Competition

Deep neural networks, vanishing gradients, skip connections, batch normalization

Transfer learning (approx. 1 day)

Adapting and combining models

Unsupervised pre-training

Image data augmentation, explainable AI

Data loader

Regional CNN (approx. 1 day)

Object localization

Semantic segmentation

Regression problems

Branched neural networks

YOLO architecture

U-Net models

Methods of creative image generation (approx. 1 day)

Generative Adversarial Networks (GAN)

Deepfakes

Diffusion models

Superresolution

Supplementing image areas

Apply foundation models from Hugging Face

Multimodal models

LoRA-Fine-Tuning

Application areas of generative models

Legal restrictions

Recurrent neural networks (approx. 2 days)

Sequence analysis

Recurrent layers

Backpropagation through time (BPTT)

Analysis of time series

Exploding and vanishing gradient problems

LSTM (Long Short-Term Memory)

GRU (Gated Recurrent Unit)

Deep RNN

Deep LSTM

Text processing using neural networks (approx. 2 days)

Text preprocessing

Embedding layers

Text classification

Sentiment analysis

Natural Language Processing (NLP)

Translations

Text generation

Sequence-to-sequence method, encoder-decoder architecture

Encoder-only and decoder-only models

Local application of large language models

Language models (approx. 1 day)

Transfomer architecture

Attention and Multihead-Attention

Positional Encodings

Fine-tuning large language models

Prompting

Text generation pipelines

Summarization

Chatbots

Retrieval Augmented Generation

AI agents

Deep reinforcement learning (approx. 1 day)

Control of dynamic systems

Agent systems

Training through rewards

Policy Gradients

Deep Q-learning

Bayesian neural networks (approx. 1 day)

Uncertainties in neural networks

Statistical evaluation of forecasts

Confidence, standard deviation

Unbalanced data

Sampling methods

Project work (approx. 3 days)

To consolidate the content learned

Presentation of the project results

Important ITIL terms and definitions (approx. 2 days)

Management of digital products and services

Products, services and service offerings

Value creation and service relationships

Service consumers, service providers, sponsors, customers and users

Service quality and service level agreements (SLA)

Utility, warranty, user experience and sustainability

ITIL product and service lifecycle

Continual improvement

The four dimensions of ITIL product and service management (approx. 1 day)

Organizations and people

Partners and suppliers

Information and technology

Value streams and processes

Holistic approach and external influencing factors

The ITIL product and service lifecycle (approx. 1 day)

Discover, Design, Acquire and Build

Transition, Operate, Deliver and Support

Value creation in the product and service lifecycle

Iterative and non-linear use of the lifecycle

The ITIL Value System (approx. 2 days)

Components of the ITIL Value System and ITIL basic principles

Governance, value chain and operating model

Management practices, practice guidelines and continuous improvement

Value orientation, collaboration and optimization

Service operations, releases and problem management

Continuous integration, continuous delivery and continuous deployment

Site reliability engineering (SRE) and observability

Metrics and Critical Success Factors (CSF)

Value stream identification, mapping and management (approx. 1 day)

Value streams and value stream management

Main value streams and supporting value streams

Complexity thinking and workflow optimization

Value stream mapping

ITIL and AI (approx. 0.5 days)

Artificial intelligence (AI) and AI maturity

Generative AI (GenAI) and Agentic AI

AI in the product and service lifecycle

AI governance

ITIL and other frameworks (approx. 0.5 days)

ITIL and DevOps

ITIL and PRINCE2

Project management in the product and service lifecycle

Project work, certification preparation and certification examination (approx. 3 days)

Introduction to project management based on PRINCE2® (approx. 1 day)

Definition and characteristics of a project

Project control cycle of project management and the six project dimensions

Challenges in project management - why do projects fail?

Advantages of the PRINCE2® project management method

Customer-supplier environments

Projects in a commercial environment

Structure of the PRINCE2® method and its five integrated building blocks

The management products of PRINCE2®

Digital tools and AI-supported analysis in modern project management

The PRINCE2® basic principles (approx. 1 day)

The seven basic principles of PRINCE2®

Statements and contents of the basic principles

Relationship between the basic principles and the PRINCE2® topics

Adaptation of PRINCE2® to the project environment, taking into account digital working methods

The importance of people for PRINCE2® projects (approx. 1 day)

Change management

Leadership and management

Communication in the project

Effects of digital and AI-supported systems on collaboration and change processes

The seven topics of PRINCE2® (approx. 3 days)

Business case (benefits management approach and sustainability management approach)

Organization (project structure, roles and responsibilities)

Creation of plans

Quality planning and quality control

Risk management using modern analysis methods and data-based evaluations

Issue management

Controlling the progress of the project

The seven PRINCE2® processes (approx. 2 days)

Interaction of the seven PRINCE2® processes in the project process

Activities in the respective PRINCE2® processes

Preparing, steering and initiating a project

Controlling a phase

Managing product delivery

Managing phase transitions

Closing a project

Project work, certification preparation and certification examination (approx. 2 days)