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Data Science Vs. Big Data Vs. Data Analytics

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Data is growing at an exponential rate, there is more than 2.7 zettabytes of data in today’s digital world and an expected growth of approximately 180 zettabytes in 2025. All the data has been analyzed to tease out insights which helps organizations to make sense of all the data. This article discusses the recommended skills for data science, big data and data analytics.

Data Science

Data scientists collaborate Statistics, Mathematics, Programming, Problem-solving and capturing data in shrewd wats and find out the different patterns, along with the activities such as cleaning, preparing and standardizing the data. Data science deals with cleaning, preparing and analyzing data. Structured and unstructured data can be dealt efficiently. Data Science is used to extract insights and information based on the data by organizations. Skills required:

  • Knowledge of SAS or R in-depth. R is usually preferred for Data Scientists.
  • Coding in Python, followed by Java, Perl, C/C++.
  • Knowledge of Hadoop. Experience in Hive or Pig is considered as cherry on the cake.
  • Complex queries in SQL. NoSQL and Hadoop are major focus
  • Able to work on unstructured data is crucial, the source may be from social media, video feeds or any other source.

Big Data

Gartner defines Big Data as, “Big Data is high-volume and high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable insight, decision-making, and process automation”. Big data helps to find insights for making better strategic business decisions. Big data is used to describe unstructured and structured data that has immense volumes to be effectively processed. Unorganized and unaggregated raw data is processed initially, data which cannot be stored in single computer memory. Skills required:

  • Analytical Skills: The ability to develop sense from the data that is generated. Coupled with problem-solving abilities, the ability to determine which data is required for a solution.
  • Creativity: Able to develop new methods to gather, interpret and analyze the data.
  • Mathematical and statistical skills are needed
  • Use of highly efficient algorithms to find out insights from data is required.
  • Business Skills: Business objectives should be understood, along with underlying processes that help in the growth of the business and profit.

Data Analytics The ability to examine raw data with the goal to find hidden patterns and to apply algorithms or mechanical process to figure out insights and draw conclusions based on it is the science of Data Analytics. A data analyst work is to infer conclusions based on what the researcher knows. Data analysts help organizations to make strategic decisions and to verify or reject existing theories or models. Skills required:

  • Data Visualization and Communication skills.
  • Machine Learning skills
  • Programming knowledge in R and Python
  • Descriptive and inferential statistics & experimental designs are crucial.
  • Ability to convert all the data into standardized format for better understanding.

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GradesFixer. (2019). Data Science vs. Big Data vs. Data Analytics. Retrived from https://gradesfixer.com/free-essay-examples/data-science-vs-big-data-vs-data-analytics/
GradesFixer. "Data Science vs. Big Data vs. Data Analytics." GradesFixer, 11 Feb. 2019, https://gradesfixer.com/free-essay-examples/data-science-vs-big-data-vs-data-analytics/
GradesFixer, 2019. Data Science vs. Big Data vs. Data Analytics. [online] Available at: <https://gradesfixer.com/free-essay-examples/data-science-vs-big-data-vs-data-analytics/> [Accessed 19 September 2020].
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