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  2. A software engineer shares the résumé that got him on-site ...

    www.aol.com/software-engineer-shares-r-sum...

    To show the full scope of his project areas, Phadké ensured that his résumé emphasized projects in different areas of software engineering, spanning systems engineering, back-end web ...

  3. Data engineering - Wikipedia

    en.wikipedia.org/wiki/Data_engineering

    Around the 1970s/1980s the term information engineering methodology (IEM) was created to describe database design and the use of software for data analysis and processing. [3] [4] These techniques were intended to be used by database administrators (DBAs) and by systems analysts based upon an understanding of the operational processing needs of organizations for the 1980s.

  4. Liang Wenfeng - Wikipedia

    en.wikipedia.org/wiki/Liang_Wenfeng

    In 2008, Liang formed a team with his classmates to accumulate data related to financial markets. He also led the team to explore quantitative trading using machine learning and other technologies. This was during the peak of the 2007–2008 financial crisis. [2]

  5. Category:Data engineers - Wikipedia

    en.wikipedia.org/wiki/Category:Data_engineers

    In other projects Wikidata item; Appearance. move to sidebar hide. ... Pages in category "Data engineers" The following 4 pages are in this category, out of 4 total.

  6. Data science - Wikipedia

    en.wikipedia.org/wiki/Data_science

    Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession. [4] Data science is "a concept to unify statistics, data analysis, informatics, and their related methods" to "understand and analyze actual phenomena" with data. [5]

  7. DataOps - Wikipedia

    en.wikipedia.org/wiki/Dataops

    DataOps is a set of practices, processes and technologies that combines an integrated and process-oriented perspective on data with automation and methods from agile software engineering to improve quality, speed, and collaboration and promote a culture of continuous improvement in the area of data analytics. [1]