Data Analyst versus Data Scientist
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Data Analyst versus Data Scientist

Is Data Scientist a Data Analyst OR vice-versa? I googled this and got more than a 1000 websites that provides this differentiation. So my thought was to consolidate everything I learnt for people who choose their career path in one or the other. If you check Gartner’s Hype Cycle of Emerging Technology, the peak of inflated expectations belongs to “Internet of Things” and more data collection activities such as Machine Learning and Wearable. In addition, the big data paradigm contributes more through social media including Facebook, Twitter, YouTube and many more.

Gartner Hype Cycle for Emerging Technologies 2015

So what is the difference between the data scientist and data analyst? A data scientist converts volume into value and data analyst checks for the viability and visualization of the value by breaking them down into smaller topics. That doesn’t mean that you need 2 different resources to be budgeted for. These are shift in behaviors of the 2 roles. Hence a data scientist is focused on researching data anomalies and used cases from complex, unstructured data elements. However, a data analyst has a time and scope bound topic that they break down into components with meaningful objective.

I saw a nice diagram that explains the differences and overlaps between data science and data analytics here.

Here are some of the differences between the two roles:

  • Data Analyst provides a representation of what happened versus a Data Scientist provides a prediction of what is going to happen
  • Data Analyst has an objective of analyzing data which is typically time and scope driven versus a Data Scientist has an objective of predicting incidents that may not be limited to a certain scope
  • Data Analysts are more business focused and Data Scientists are more statistics focused
  • Data Analysts may use SQL programming versus a Data Scientists may use complex statistical tools such as R programming and Predictive modeling
  • Data Analysts are typically BI Developers, SQL Analysts, BI Analysts, and Operations teams versus Data Scientists are typically Data mining SME’s, Statisticians, and R&D Experts.

In summary, Data Analysts are responsible for summarizing current state using past and present data versus Data Scientists are responsible for forecasting insights using past and current data. However, the commonalities between both involve data aspects such as data governance, data quality, data preparation, data modeling, and analytics skills. Both roles are very critical for information based decision-making process.

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