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Top critical capabilities for a data scientist

dasca DATA SCIENCE COUNCIL OF AMERICA TOP CRITICAL CAPABILITIES FOR A DATA SCIENTIST The world is driven by big data , the fuel that powers business and organizations to make data - driven decisions They can manipulate large , unstructured data sources 1 Data scientists are the most critical people for effectively using big data 28 Coding knowledge : Strong coding skills are essential to pre - process data from different sources , build a model , and analyze its performance 01 J To do this , they require the following skills © 2021. All Rights Reserved . Data Science Council of America . Creativity : Alternative , creative thinking is often the key to tackling a challenge successfully by observing new patterns in good and failed examples 02 A data scientist must combine creative , investigative , and scientific thinking to extract insights from datasets and address the underlying client challenges Business knowledge Be aware and look beyond the obvious to spot interesting trends for the business Remember that nobody has all the skills needed , and a flexible , systematic approach will help to develop the required familiarity By appreciating different approaches , a data scientist can more effectively model data in the business world and communicate the results to the decision - makers The right approach is critical 01 $ 32 Curiosity : Be excited by asking " why " and extract meaning from datasets by zooming in on challenges and picking clues in data 03 © 2021. All Rights Reserved . Data Science Council of America . 03 Scientific approach : Use different tools to manage annotations , code , data , and workflows 04 Here are the different thinking paradigms useful for data scientists 05 They can extract valuable insights Agent - based thinking Look at simpler entities and how relatively simple interactions between these entities can result in emergent system behaviors Computational thinking The emphasis is on structured problem solving , problem decomposition , pattern recognition , generalization , and abstraction that can be coded and executed by computers Model thinking Think in terms of models i.e. logical , mathematical , or physical abstraction of reality i.e. representation of a process , property , or an object Behavioral thinking Remember that human behavior is not ideal and rational , but is based on thinking capacity , available information , and time Right metrics Optimize for the right metrics as per client requirements , not mere vanity discrepancies metrics Systems thinking Look for the structures underlying complex situations , and discern high- and low - leverage change Clearly , a data scientist requires more than just technical skills to generate value for business partners Clean data 02 Do not assume data is clean , and look for critical How can you create more impact as a data scientist ? 04 Statistics lie Evaluate charts and statistics carefully , and see if they were correctly sampled and ethically used Fallacies Avoid misguided judgments and actions based on probabilities and Visit to know more ! © 2021. All Rights Reserved . Data Science Council of America . misinterpreted correlation The way ahead A successful data scientist must combine critical skills with the right thinking paradigms , steering clear of common fallacies for the best results ! Boost your data science prospects with the right certification from DASCA .

Top critical capabilities for a data scientist

shared by datasciencecouncil on Jun 22
Given the significance of data scientists as users of ballooning big data volumes, certain critical capabilities serve them well in achieving their purpose.


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