what are the characteristics of data scientist?
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['Statistical thinking. Data scientists are professionals who turn data into information, so statistical know-how is at the forefront of our toolkit. ... ', 'Technical acumen. ... ', 'Multi-modal communication skills. ... ', 'Curiosity. ... ', 'Creativity. ... ', 'Grit.']
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You'll need to have: excellent analytical and problem-solving skills. experience in database interrogation and analysis tools, such as Hadoop, SQL and SAS. exceptional communication and presentation skills in order to explain your work to people who don't understand the mechanics behind data analysis.
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The basic qualities of a researcher are intelligence, honesty, curiosity and initiative, enough knowledge, and good in oral and written communication. It cannot be denied that a researcher must be intelligent enough.
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['Expectation vs. Reality – Here Lies A Wide, Wide Gap! ... ', "Mapping a Data Scientists' Role to Business Goals. Here's another (un)popular expectation problem. ... ", 'Lack of Upskilling for Data Science Professionals. ... ', 'No Clear Benchmarking in Salary Pay-Outs. ... ', 'Great Exposure to Different Data Science Projects on Different Platforms.']
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Characteristics of scientific research. The nine main characteristics of science are the following: Objectivity, verifiable, ethical neutrality, systematic exploration, reliability, precision, abstraction and predictability.
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The most important things to learn in Data Science are: Mathematical concepts such as linear algebra, probabilities, and distributions. Statistical concepts such as descriptive and inferential statistics. Programming languages such as python, R, and SAS.
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Curiosity. In many ways, curiosity – that insatiable hunger for knowledge and understanding – is the first and foremost trait of a data scientist. Our job is to ask questions of data and of people.
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The demand for big data professionals has never been higher. "Machine Learning Engineers, Data Scientists, and Big Data Engineers rank among the top emerging jobs on LinkedIn," Forbes proclaims. ... Data science is heavily math-oriented. By contrast, data engineers work primarily on the tech side, building data pipelines.
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Being a data scientist isn't everything it's cracked up to be. It has its share of boring, repetitive tasks. According to a new survey, on average data scientists spend more than half their time (53 percent) doing stuff they don't dig -- such as cleaning and organizing data for analysis.
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['Expertise In Core Python.', 'Good grasp of Web Frameworks.', 'Object Relational Mappers.', 'Road to Data Science.', 'Machine Learning and AI.', 'Deep Learning.', 'Understanding of Multi-Process Architecture.', 'Analytical skills.']
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['Data Analyst. A data scientist is very similar to a data analyst and depending on the company, these two are very interchangeable. ... ', 'Data Engineer. ... ', 'Machine Data Engineer. ... ', 'Data Scientist.']
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According to the current popular view, data science is exclusively a tech enterprise; data scientists are professionals who work at tech firms like Google or Apple. Data crunchers who work at finance companies, in contrast, are thought of as quantitative analysts.
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['Volume. Massive volumes of data, challenges in cost-effective storage and analysis.', 'Velocity. The rate at which data is produced and changes, and also how fast the data must be processed to meet business requirements.', 'Variety. The diversity in the formats and types of data. ... ', 'Variability. ... ', 'Veracity. ... ', 'Value.']
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You'll typically need a degree in a computer science, mathematical or science-based subject to work as a data scientist. The following degree subjects may be particularly useful: computer science. data science/computer and data science.
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While data scientists focus on comparing a number of different methods to create the best machine learning model, statisticians rather work to improve a single, simple model to best fit the data. Statisticians tend to focus more on quantifying uncertainty than data scientists.
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Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data. ... A Data Scientist will look at the data from many angles, sometimes angles not known earlier.
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['A high level of mathematical ability.', 'Programming languages, such as SQL, Oracle and Python.', 'The ability to analyse, model and interpret data.', 'Problem-solving skills.', 'A methodical and logical approach.', 'The ability to plan work and meet deadlines.', 'Accuracy and attention to detail.']