Comparing A Data Analyst To A Data Scientist

There is some serious confusion about the functions of Data Analysts and Data Scientists. In the public mind, the two are often considered one and the same thing. To some extent, this misreading is being perpetuated by the companies themselves, by differently titling each role from one company to the next. But in reality, the roles of each discipline is distinctly different. To find out how different, a brief comparative study of data analyst vs data scientist is presented below.

Business Analytics

The title "Business Analytics" has been in use for over 30 years now, and was launched by the release of MS Excel in 1985. This marked the progress from manual execution of this discipline, using calculators only. This led to a lot of lengthy and tedious trial and error exercises. But the advent of the online, and computer software (like Excel), made a sea-change in the way Business was now approached. The Data Sciences created a worldwide demand for expertise, that has entered a boom period right now.

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Job Descriptions

There are now several types of practitioners of Data Science, but the two main categories are still Data Analyst and Data Scientist. A brief Job Description of the two categories are as follows:

Data Analyst: This expertise is applicable to a wide number of industries, with tasks such as:* Pattern Recognition.* Data Consolidation and infrastructure set up, developing automated routines and consolidating data.* Report creation * Collaboration with Company Stakeholders, other departments such as sales and marketing, other team members and peers in Data Science like database developers and data engineers.

Data Scientist: This is a big role, meant for theoreticians who are also experts in Big Data, statistics, programming (R, Python, SAS etc.) and several esoteric branches of Data Science. Some of the jobs handled are:* Pattern Search and Classification.* Pulling data, as well as merging and analyzing. * Simplifying data problems to develop predictive models.* Application of powerful data tools such as PySpark, Impala, Hive, Excel, Hadoop, to develop and test new algorithms.* Building data visualizations.* Combining all concepts and results to communicate the future actions to Stakeholders.

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Comparative Skills

The main difference between the two categories is in the leadership component, as the following demonstrates:

• The main job of the Data Scientist is to formulate the questions that will guide the business in the future, and then proceed to solve them. But the Data Analyst is given the questions by the management team, which he must now solve.
• While both are expected to query and work with the engineering teams, the Data Analyst does not build the statistical models or directly create the machine learning and advanced programs. This is the job of the Data Scientist.
• Powerful visualization skills are expected from the Data Scientist, while the Data Analyst does not have to convert data and create a business scenario.

And this basically is the comparison of Data analyst vs data scientist.

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