Аналитик данных — работа в Финляндии
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.
Аналитик данных - Какие навыки обязательны?
The type of infrastructure which defines the format of data: semi-structured, unstructured and structured.
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.
The tools used to transform large amounts of raw data into relevant and helpful business information.
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.
The methods of artificial intelligence, machine learning, statistics and databases used to extract content from a dataset.
Use ICT tools to apply mathematical, algorithmic or other data manipulation processes in order to create information.
Apply mathematical methods and make use of calculation technologies in order to perform analyses and devise solutions to specific problems.
The characteristics of internal and external documentation types aligned with the product life cycle and their specific content types.
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.
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.
Explore large datasets to reveal patterns using statistics, database systems or artificial intelligence and present the information in a comprehensible way.
Combine data from sources to provide unified view of the set of these data.
Collect and evaluate numerical data in large quantities, especially for the purpose of identifying patterns between the data.
Collect and select a set of data from a population by a statistical or other defined procedure.
Detect and correct corrupt records from data sets, ensure that the data become and remain structured according to guidelines.
Specify the criteria by which data quality is measured for business purposes, such as inconsistencies, incompleteness, usability for purpose and accuracy.
The techniques and methods used for eliciting and extracting information from unstructured or semi-structured digital documents and sources.
Apply quality analysis, validation and verification techniques on data to check data quality integrity.
Gather data by designing and applying search and sampling methods.
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.
The process of classifying the information into categories and showing relationships between the data for some clearly defined purposes.
The query languages such as SPARQL which are used to retrieve and manipulate data stored in Resource Description Framework format (RDF).
The field of standardised computer languages for retrieval of information from a database and of documents containing the needed information.
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.
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.
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.
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.
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.
Аналитик данных - Какие навыки полезны, но не обязательны?
Collect protected, fragmented or corrupted data and other online communication. Document and present findings from this process.
The characteristics, tools and techniques for measurement, collection, analysis and reporting of web data to get information on the users' behaviour and to improve the performance of a website.
The computer language MDX is a query language for retrieval of information from a database and of documents containing the needed information. It is developed by the software company Microsoft.
The methods through which information is generated, structured, stored, maintained, linked, exchanged and used.
The computer language XQuery is a query language for retrieval of information from a database and of documents containing the needed information. It is developed by the international standards organisation World Wide Web Consortium.
The classification of databases, that includes their purpose, characteristics, terminology, models and use such as XML databases, document-oriented databases and full text databases.
The computer language SPARQL is a query language for retrieval of information from a database and of documents containing the needed information. It is developed by the international standards organisation World Wide Web Consortium.
The computer language LDAP is a query language for retrieval of information from a database and of documents containing the needed information.
The physical and technical concepts of how digital data storage is organised in specific schemes both locally, such as hard-drives and random-access memories (RAM) and remotely, via network, internet or cloud.
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.
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.
The technologies which enable access to hardware, software, data and services through remote servers and software networks irrespective of their location and architecture.
Create visual representations of data such as charts or diagrams for easier understanding.
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.
The computer language LINQ is a query language for retrieval of information from a database and of documents containing the needed information. It is developed by the software company Microsoft.
The computer language N1QL is a query language for retrieval of information from a database and of documents containing the needed information. It is developed by the software company Couchbase.
Use specific techniques and methodologies to analyse the data requirements of an organisation's business processes in order to create models for these data, such as conceptual, logical and physical models. These models have a specific structure and format.
Другие профессии
- Специалист по науке о данных
- Директор по данным
- Специалист по качеству данных
- Архивист больших данных
- Менеджер по управлению информацией и знаниями в сфере ИТ
- Оператор ввода данных
- Руководитель группы ввода данных
- Специалист по интеграции баз данных
- Исследователь в области информатики
- Аналитик маркетинговых исследований
- Администратор баз данных
- Консультант по исследованиям в сфере ИТ