Differentiating Between Data Analyst and Data Scientist

Differentiating Between Data Analyst and Data Scientist
18th Jun, 2019
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Data Science has grown tremendously in this century that one has to search in which field it is not being used. Healthcare, cyber security, banking, online retail, finance, SEO, digital marketing, and many other fields use Data Science in their businesses. Do you know that Bank of America finds out which loan borrowers would most likely default on their payments with a very high accuracy rate? In the organizational setup, there are many roles in Data Science like Data Scientist and Data Analyst. Both of the roles may seem the same to you but keep reading as we clearly differentiate between the two.

Check this Data Scientist vs Data Analyst vs Data Engineer video :

Comparison of skills required for Data Scientist and Data Analyst

CriteriaData ScientistData Analyst
Languages requiredLanguages like Pig, Hive, Matlab, Scala, SAS, SQL, Python, R need to be learntLanguages like Python, HTML, Javascript, R, SQL need to be learnt
SpecializationData visualization and expounding business stories to other teams in the organizationSpecialization in data visualization tools like Qlikview, Tableau, MSBI
Hadoop expertiseStrong knowledge to work around distributed storage and computing frameworks like HadoopIt’s not really necessary
AI skillsMachine learning conceptsNot required

 

How are Data Analyst and Data Scientist different from each other?

    • To be a Data Scientist one needs to have robust business acumen and visualization skills to process insights into a business story whereas a Data Analyst needn’t have specialized business skills and basic visualization skills would suffice in his case.
    • A Data Scientist should be very proficient in machine learning and in building statistical models. Such models find huge applications in spatial models, recommendation systems, predictive modeling, supervised classification, clustering. In the case of Data Analyst however he’s not required to be proficient in these processes.
    • Predictive analytics is a process which the Data Scientist needs to excel in. Deriving highly accurate future predictions from past datasets is one of his primary responsibilities. A Data Analyst on the other hand derives valuable insights from huge data.
    • A Data Scientist’s job requires him to make sense of the unknown aspects of the business while a Data Analyst works on the known business aspects from fresh perspectives. This is one of the reasons why being a Data Scientist is twice the hard work than being a Data Analyst. It also answers why Data Scientists are paid almost twice than Data Analysts.
    • A Data Scientist approaches business issues and moreover picks up those issues which have greater business value while a Data Analyst just approaches business issues.

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  • A Data Scientist should be well grounded in statistics, mathematics, data mining, correlation. A Data Analyst needs to excel in data architecture’s tools and components.
  • Applying rank, median like analytical functions on data sets is one of Data Scientist’s many jobs. A Data Analyst needs only excel in data storing and retrieving tools.
  • Expertise on database systems especially on NoSQL systems is required by the Data Scientist. A Data Analyst needs to know business intelligence and data warehousing concepts.

Conclusion

Well, don’t think you know about all the roles in Data Science industry. There are various other job roles like data architect, data engineer, statistician, database administrator, business analyst, data and analytics manager. They are also very crucial in getting Data Science applications up and running but we have dedicated this post only to make you familiar with analyst and scientist.

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