The Key Skills To Build A Data Science

 

Data Science refers to an emerging area of work concerned with the collection, preparation, analysis, visualization, management, and preservation of large collections of information. Although the name Data Science seems to connect most strongly with areas such as databases and computer science, many different kinds of skills – including non-mathematical skills – are needed.

While it is certainly true that companies, schools, and governments use plenty of numeric information – sales of products, grade point averages and tax assessments are a few examples- there is lots of other information in the world that mathematicians and statisticians look at and cringe. So, while it is always useful to have great math skills, there is much to be accomplished in the world of Data science for those who are presently more comfortable working with words, lists, photographs, sounds, and other kinds of information.The Key Skills To Build A Data Science

In addition, Data science is much more than simply analysing data. There are many people who enjoy analysing data and who could happily spend all day looking at histograms and averages, but for those who prefer other activities, Data science offers a range of roles and requires a range of skills. Let’s consider this idea by thinking about some of the data involved in buying a box of cereal.

The data scientist must become involved in the archiving of the data. Preservation of collected data in a form that makes it highly reusable – what you might think of as “Data curation” – is a difficult challenge because it is so hard to anticipate all of the future uses of the data.

Learning the application domain – The data scientist must quickly learn how the data will be used in a particular context.

  • Communicating with data users – A Data scientist must possess strong skills for learning the needs and preferences of users. Translating back and forth between the technical terms of computing and statistics and the vocabulary of the application domain is a critical skill.
  • Seeing the big picture of a complex system – After developing an understanding of the application domain, the Data scientist must imagine how data will move around among all of the relevant systems and people.
  • Knowing how data can be represented – Data scientists must have a clear understanding about how data can be stored and linked, as well as about “metadata” (data that describes how other data are arranged).
  • Data transformation and analysis – When data become available for the use of decision makers, Data scientists must know how to transform, summarize, and make inferences from the data.
  • Visualization and presentation – Although numbers often have the edge in precision and detail, a good data display (e.g., a bar chart) can often be a more effective means of communicating results to data users.
  • Attention to quality – No matter how good a set of data may be, there is no such thing as perfect data. Data scientists must know the limitations of the data they work with, know how to quantify its accuracy, and be able to make suggestions for improving the quality of the data in the future
  •  Ethical reasoning – If data are important enough to collect, they are often important enough to affect people’s  lives. Data scientists must understand important ethical issues such as privacy, and must be able to communicate the limitations of data to try to prevent misuse of data or analytical. results.

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