Profession data scientist

Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.

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Personality Type

• Investigative / Realistic

Knowledge

• Information categorisation

  The process of classifying the information into categories and showing relationships between the data for some clearly defined purposes.

• Visual presentation techniques

  The visual representation and interaction techniques, such as histograms, scatter plots, surface plots, tree maps and parallel coordinate plots, that can be used to present abstract numerical and non-numerical data, in order to reinforce the human understanding of this information.

• Resource description framework query language

  The query languages such as SPARQL which are used to retrieve and manipulate data stored in Resource Description Framework format (RDF).

• Information extraction

  The techniques and methods used for eliciting and extracting information from unstructured or semi-structured digital documents and sources.

• Statistics

  The study of statistical theory, methods and practices such as collection, organisation, analysis, interpretation and presentation of data. It deals with all aspects of data including the planning of data collection in terms of the design of surveys and experiments in order to forecast and plan work-related activities.

• Data models

  The techniques and existing systems used for structuring data elements and showing relationships between them, as well as methods for interpreting the data structures and relationships.

• Data mining

  The methods of artificial intelligence, machine learning, statistics and databases used to extract content from a dataset.

• Online analytical processing

  The online tools which analyse, aggregate and present multi-dimensional data enabling users to interactively and selectively extract and view data from specific points of view.

• Query languages

  The field of standardised computer languages for retrieval of information from a database and of documents containing the needed information.

Skills

• Develop data processing applications

  Create a customised software for processing data by selecting and using the appropriate computer programming language in order for an ICT system to produce demanded output based on expected input.

• Handle data samples

  Collect and select a set of data from a population by a statistical or other defined procedure.

• Execute analytical mathematical calculations

  Apply mathematical methods and make use of calculation technologies in order to perform analyses and devise solutions to specific problems.

• Collect ICT data

  Gather data by designing and applying search and sampling methods.

• Deliver visual presentation of data

  Create visual representations of data such as charts or diagrams for easier understanding.

• Design database scheme

  Draft a database scheme by following the Relational Database Management System (RDBMS) rules in order to create a logically arranged group of objects such as tables, columns and processes.

• Report analysis results

  Produce research documents or give presentations to report the results of a conducted research and analysis project, indicating the analysis procedures and methods which led to the results, as well as potential interpretations of the results.

• Normalise data

  Reduce data to their accurate core form (normal forms) in order to achieve such results as minimisation of dependency, elimination of redundancy, increase of consistency.

• Build recommender systems

  Construct recommendation systems based on large data sets using programming languages or computer tools to create a subclass of information filtering system that seeks to predict the rating or preference a user gives to an item.

• Interpret current data

  Analyse data gathered from sources such as market data, scientific papers, customer requirements and questionnaires which are current and up-to-date in order to assess development and innovation in areas of expertise.

• Perform data cleansing

  Detect and correct corrupt records from data sets, ensure that the data become and remain structured according to guidelines.

• Establish data processes

  Use ICT tools to apply mathematical, algorithmic or other data manipulation processes in order to create information.

• Implement data quality processes

  Apply quality analysis, validation and verification techniques on data to check data quality integrity.

• Manage data collection systems

  Develop and manage methods and strategies used to maximise data quality and statistical efficiency in the collection of data, in order to ensure the gathered data are optimised for further processing.

Optional knowledge and skills

Source: Sisyphus ODB