Course Guide DPV

• Introduction
  • Global overview of the course
  • What is optional and obligatory?
  • Grading and resits
    • Grading
    • Repairs
    • Supervision and guidance
  • Project report template
    • Structure
• I Technical topics
  • Data Preparation and Visualization [DPV]
    • Introduction
      • Global description of the practicum and project
      • Study material and tools
      • Deliverables and obligatory items
    • Data set and database
    • Description of the practical assignments - Tool-based
      • Tools
      • Assignment 1: Facts and dimensions
      • Assignment 2: Re-create the demo from the lecture
      • Assignment 3: Do it yourself
      • Assignment 4: multidimensional modeling Case ``Mobile app beta tester service''
      • Trouble shooting
    • Description of the practical assignments - R-based
      • Tools
      • Assignment 1: Facts and dimensions
      • Assignment 2: Re-create the demo from the lecture
      • Assignment 3: Do it yourself
      • Assignment 4: multidimensional modeling Case ``Mobile app beta tester service''
  • Data Mining [DM]
    • Introduction
      • Global description of the practicum and project
      • Study material and tools
      • Deliverables and obligatory items
    • Description of the practical assignments
  • Information Extraction Using Natural Language Processing [IENLP]
    • Introduction
      • Global description of the practicum and project
      • Study material and tools
      • Deliverables and obligatory items
    • Description of the practical assignments
      • Datasets
      • Prerequisites
      • Assignment 1: Regular Expressions
      • Assignment 2: Named Entity Recognition
      • Assignment 2: Text Classification
      • Some Hints:
  • Feature extraction from Time Series data [TS]
    • Introduction
      • Global description of the practicum and project
      • Study material and tools
      • Deliverables and obligatory items
    • Description of the practical assignments
      • Datasets
      • Understanding the dataset
      • Exploring the Raw Data
      • The Time Domain and the Frequency Domain
      • Filtering
      • Dynamic Time Warping and Classification with K-nearest Neighbours
      • Time Series Comparison and Prediction
      • Basic tutorial on Python and common modules
      • Additional tutorials and references
  • Semi-structured data [SEMI]
    • Introduction
      • Global description of the practicum and project
      • Study material and tools
      • Deliverables and obligatory items
    • Description of the practical assignments
      • Assignment 1: SQL/XML
      • Assignment 2: XPath and XQuery
      • Assignment 3: Handling JSON data
      • Assignment 4: RDF data and SPARQL querying
  • Probabilistic Databases and Data Quality [PDBDQ]
    • Introduction
      • Global description of the practicum and project
      • Study material and tools
      • Deliverables and obligatory items
    • Description of the practical assignments
      • Installation
      • Crash course DataLog
      • Querying a probabilistic database
      • Creating probabilistic data
      • Representing data quality problems as probabilistic data
      • Probabilistic Data Integration
  • Process Mining [PM ]
    • Introduction
      • Global description of the practicum and project
      • Study material and tools
      • Deliverables and obligatory items
    • Description of the practical assignments
      • Installation
      • Data set(s)
      • Assignment 1: Process discovery from event logs (on paper)
      • Assignment 2:The PROM Tool
      • Assignment 3: Petri Nets
      • Assignment 4: Process Discovery by using Alpha Algorithm (on-paper)
      • Assignment 5:Process Discovery and Enhancement in PROM
• II Projects
  • Project 1: Decision support for University timetables [TIMETABLES]
    • Introduction
    • Description of data set
    • Description of challenge
      • Strategy 1: Compliance
      • Strategy 2: Exploration
      • Strategy 3: Trend analysis
    • Tips and suggestions
      • Creating new geographic maps for use in Tableau
  • Project 2: Business Intelligence [BI]
    • Introduction
    • Description of data set
    • Description of challenge
      • Task 1: Business Questions and Multidimensional Modeling
      • Task 2: ETL
      • Task 3: Visualisation
    • Tips and suggestions
      • Hint 1
      • Hint 2
  • Project 3: Transport domain [TRANSPORT]
    • Introduction
    • Description of data set
      • Description NDW data speed, flow and traveltime
      • Description Status information
      • Description of BE-mobile data
      • Description of CDR data
    • Description of challenge
    • Tips and suggestions
      • Creating new geographic maps for use in Tableau
  • Project 4: Predicting neurological outcome in patients with a severe postanoxic encephalopathy [EEG]
    • Introduction
    • Description of data set
    • Description of challenge
    • Tips and suggestions
  • Project 5: Text classification or Named Entity Recognition [TCNER]
    • Introduction
    • Description of data set
    • Description of challenge
      • Text Classification
      • Named Entity Recognition
    • Tips and suggestions
  • Project 6: Automatic detection of Atrial fibrillation (AF) episodes [AF]
    • Introduction
      • Background
    • Description of data set
    • Description of challenge
      • Requirements report
    • Tips and suggestions
  • Project 7: Linked Open Data [LOD]
    • Introduction
    • Description of data set
    • Description of challenge
    • Tips and suggestions
  • Project 8: Probabilistic Data Integration [PDI]
    • Introduction
    • Description of data set
    • Description of challenge
    • Tips and suggestions
  • Project 9: Referral Advice [RA]
    • Introduction
    • Description of data set
    • Description of challenge
    • Tips and suggestions
  • Project 10: Displacement prediction in South Sudan [DISPRED]
    • Introduction
    • Description of data set
    • Description of challenge
    • Tips and suggestions
  • Project 11: Predicting surgical case durations for a Thorax centre [PSCD]
    • Introduction
    • Description of data set
    • Description of challenge
    • Tips and suggestions
  • Project 12: Web Harvesting for Smart Applications [SDSI]
    • Introduction
    • Description of data set
    • Description of challenge
    • Tips and suggestions
  • Project 13: Web Harvesting of Online Healthcare Communities [WHCC]
    • Introduction
    • Description of data set
    • Description of challenge
    • Tips and suggestions
    • References
  • Project 14: Mining Crowd-based Inventions [CBIP]
    • Introduction
    • Description of data set
    • Description of challenge
    • Tips and suggestions
    • References
  • Project 15: Process discovery and analysis [PDA]
    • Introduction
      • Deliverables
    • Description of data set
    • Description of challenge
    • Tips and suggestions