Profession data analyst

Data analysts import, inspect, clean, transform, validate, model, or interpret collections of data with regard to the business goals of the company. They ensure that the data sources and repositories provide consistent and reliable data. Data analysts use different algorithms and IT tools as demanded by the situation and the current data. They might prepare reports in the form of visualisations such as graphs, charts, and dashboards.

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Personality Type

  • Investigative / Realistic
  • Social / Investigative

Knowledge

  • Query languages

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

  • 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.

  • 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.

  • 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.

  • Information confidentiality

    The mechanisms and regulations which allow for selective access control and guarantee that only authorised parties (people, processes, systems and devices) have access to data, the way to comply with confidential information and the risks of non-compliance.

  • Unstructured data

    The information that is not arranged in a pre-defined manner or does not have a pre-defined data model and is difficult to understand and find patterns in without using techniques such as data mining.

  • Data mining

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

  • Documentation types

    The characteristics of internal and external documentation types aligned with the product life cycle and their specific content types.

  • Business intelligence

    The tools used to transform large amounts of raw data into relevant and helpful business information.

  • Information categorisation

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

  • Information structure

    The type of infrastructure which defines the format of data: semi-structured, unstructured and structured.

  • Data quality assessment

    The process of revealing data issues using ​quality indicators, measures and metrics in order to plan data cleansing and data enrichment strategies according to data quality criteria.

Skills

  • Define data quality criteria

    Specify the criteria by which data quality is measured for business purposes, such as inconsistencies, incompleteness, usability for purpose and accuracy.

  • Implement data quality processes

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

  • Perform data mining

    Explore large datasets to reveal patterns using statistics, database systems or artificial intelligence and present the information in a comprehensible way.

  • Perform data cleansing

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

  • Apply statistical analysis techniques

    Use models (descriptive or inferential statistics) and techniques (data mining or machine learning) for statistical analysis and ICT tools to analyse data, uncover correlations and forecast trends.

  • 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.

  • Collect ICT data

    Gather data by designing and applying search and sampling methods.

  • Execute analytical mathematical calculations

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

  • Handle data samples

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

  • 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.

  • Integrate ICT data

    Combine data from sources to provide unified view of the set of these data.

  • Establish data processes

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

  • Manage data

    Administer all types of data resources through their lifecycle by performing data profiling, parsing, standardisation, identity resolution, cleansing, enhancement and auditing. Ensure the data is fit for purpose, using specialised ICT tools to fulfil the data quality criteria.

Optional knowledge and skills

create data models n1ql xquery online analytical processing deliver visual presentation of data information architecture ldap linq mdx web analytics sparql manage data collection systems cloud technologies gather data for forensic purposes data storage report analysis results database

Common job titles

  • Analyst/r programmer/data scientist
  • Business analyst, revenue reporting
  • Data analyst
  • Data scientist (fall internship)
  • Data scientist / entry level
  • It associate analyst
  • Data analyst with sas, r , phython
  • Entry level data analyst