By which disciplines is Industrial Data Science characterized?
How are the intersections of Industrial Data Science shaped?
Schnittstellen zu:
Computer Science
Statistics
Engineering Science
In which areas of everyday life is Data Science involved?
Sensor and camera-based tracking of position and performance data for obective game analysis and development of tactics methods
Real time based analysis and evaluation of body and fitness values for training control and interpretation of competition strategies
Recognition and processing of naturally spoken language as a digital assistant to enable searching information or performing simple tasks
How is data mining different from maschine learning?
Data mining is defined as the process of finding patterns in data. This process runs automatically.
Maschine learning is artificial generation of knowledge from experience by learning algorithms developing a model from examples.
Which fields of application for Data Science exist in the industrial enviroment?
Manufacturing
Customer Analyses
Sales Forecast
Supply Management
Product Development
What are the objectives?
What primary methods does Data Science distinguish?
Supervised Learning
Classification
Logistic Regression
Decision Trees
Random Forest
Regression
Linear Regression
Regression Tree
Nonlinear Regression
Unsupervised
Cluster Analysis
Fuzzy Clustering
Self Organizing Maps
Association Analysis
Correlation Analysis
Association Rules/ Shopping Cart Analysis
Which phases are distinguished within the product life cycle and how are they defined?
What is the influence of Industry 4.0 on Industrial Data Science?
What examples can you give of the digization of production?
Digital assistance systems
Simulation and facotry digitization
Planning and control systems
Smart Logistics
Over which areas does the scope of the digital factory extend?
Digital Factory
The Digital Factory is the generic term for a comprehensive network of digital models, methods and tools - including simulation and 3D / VR visualization - which are integrated through end-to-end data management.
What are the different data types of Data Science?
Alpha numeric data
Time data/ timestamp
Enviromental data
Tet data
Maschine documentation
social media
Image data
Material defects
Surface conditions
Audio signal data
Process signals
Process sounds
Which characteristics of Big Data can you name?
Volume
amount of data generated
Velocity
speed of data generation
Variety
type of data generation
According to which principles can sensors be classified?
What are the main tasks involved in the preparation of raw data?
Creation of the data sets
Annotation of the data sets
Data cleansing
Data transformation
How can data, information and knowledge be distinguished?
What forms of data analysis can you name?
Descriptive
Inferential
Explorative
Confimatory
What are the significant use cases of Data Science in manufacturing?
What fields of application can you point out for supervised and unsupervised learning methods?
What process models exist for carrying out data mining projects?
What is meant by business understanding in relation to the CRISP-DM?
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