Who is a Data Scientist, a Data Analyst and a Data Engineer?
Updated on Nov 25, 2022 | 8 min read | 6.3k views
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Updated on Nov 25, 2022 | 8 min read | 6.3k views
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If you’re wondering when is the right time to chase a career in data – look no further because the time is now. Data chomping requires an army of specialists. There’s an acute shortage of two lakh professionals in data analytics. Professionals, who can derive insights and make informed decisions from data are in high demand. These roles broadly include Data Engineers, Data Analysts, and Data Scientists. As a result, data analytics courses are in high demand in India.
Data Engineers are responsible for organizing the data in a structured and easily accessible format for the organization they work for. Data Analysts and Data Scientists use this data to come up with insights, which inform the business or develop data products that improve the customer journey.
Let’s consider the example of an e-commerce Company. A Data Engineer would create the platform and a data structure to capture their users’ data – the items they buy, store in their cart and list on their wishlist. They have to make sure that the respective data is stored efficiently and that it is easily retrievable.
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The roles and responsibilities of a Data Analysts includes analysing data and deriving insights from it. They use statistics, Exploratory Data Analysis and Machine Learning to evaluate the data at hand. They help organizations to understand:
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Going back to the example of an e-commerce company. The skills of Data Analyst should include helping the marketing team identify the customer segments. The segments need to be marketed to, or the peak time to market a certain product and how, or why the last marketing campaign failed and what should be done in the future to prevent it. There may also be a Data Visualiser who is responsible for creating weekly dashboards to inform the management about sales of different products, the average delivery time or the number of daily cancellations of orders.
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A Data Scientist embodies the perfect combination of business knowledge, technical expertise and statistics. As a Data Scientist your job is not to simply draw insights and trends from the data collected over a period of time, but to also create intelligent systems which companies can deploy to automate decision making.
Like other data professionals, as a Data Scientist, you would be expected to know how to retrieve data from varied sources. Additionally, you should also know how to create algorithms to find hidden trends and patterns, and how to develop appropriate solutions. Therefore, you will need to know concepts like data preparation and exploration- to gather and understand data. Machine learning- to create predictive systems. And, a bit of software engineering too- in order to create a product, which companies can directly obtain.
For example, a data scientist would create a recommendation system at an e-commerce company, or the system that connects you with the nearest cab in your Uber app based on the geographical distance.
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Let us take the example of recommendation systems. Firstly, Data Scientists will go through the available data of an e-commerce website to look at the buying patterns and identify similar items and users on the website. They will then create algorithms in order for the website to be able to recommend products to users, based on their navigation and purchase history. This solution must be effective enough to predict the website’s visitors’ future purchases.
This is different from a Data Analyst’s role since Data Analysts are expected to perform a lot of ad-hoc analysis. It can facilitate decision making within an organisation. Whereas, Data Scientists also create prototypes and develop data products, which make independently make intelligent decisions. This is where machine learning becomes extremely critical.
However, the day-to-day job of a Data Scientist varies a lot. One day she might be performing text mining to make sense of unstructured linguistic data. Next day, she might be cleaning data to feed into a learning algorithm. Thus, a Data Scientist must be extremely agile too.
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To explain in further detail, they both deal with data, but the key difference between Data Scientist and Data analyst is what they use the data for? Data Scientist roles and responsibilities include interpreting data. They also have to code and prepare models for the better future of the company. A Data Scientist already has an advanced degree which in some cases, they might have already performed the roles and responsibilities of a Data Analyst. If it still confuses you that exactly what does a Data Scientist do, then understand the fact that they are skilled and advanced in programming and the current processes of data modeling. But they also possess the skills of Data Analyst.
All we can say about what is the role of Data Analyst is juggling between data and identifying new trends. To make decisions based on the insights and to create graphical representations, and to showcase to the company what the data reveals is precisely what is the work of a Data Analyst.
The Data Engineer manages the data needs of the organization. Data Analysts provide insights. Data Scientists create data products which can make the user experience seamless. It is important to keep in mind that these definitions and roles may vary in different organizations.
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