To have a more holistic view at all data professionals such as Data Analysts, Data engineers, Business Intelligence Professionals, and Data Analytics Specialist etc how one role differs from the other a word of caution here by the way is that data analytics is still an evolving field and hence these roles are slightly usually defined and can vary from one organisation to the other but still to put them into buckets to have better clarity, let’s take the example of an e-commerce company and in context of this example let us try and understand all these different types of job rules.
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Let’s start with the job role of a DATA ENGINEER, would create the platform and the data structure within which all the figures from the users would be captured for example what items they buy, what is in their cart currently and what is in their wish list daytime juniors have to make sure that the captured data is stored in such a fashion that is not only efficient but it’s also easily retrievable they are comfortable in working with very data sources right Extract Transform Load queries to collate data from all of them and then organize all of this data in data warehouse or databases so that others in the company can make best use of it to become a data engineer you need to acquire knowledge of languages such as Python, SQL, Hadoop, Java, C++ and Spark etc. Now all of these not mandatory but they vary from company to company as a data engineer you would be stating at the rare combination of a software engineering professional and data analyst now that we have talked about data engineers.
Data Analysts come into picture as expected to draw insights from the data which would directly impact business decisions, Directly involved in day-to-day business activities and there are lot of ad hoc analyses that a Figure analyst or a business analyst is expected to do for example let’s continue with an e-commerce company example, a data analyst would help the marketing team identify the customer segments that require marketing or the best time to market the product or why the last marketing campaign failed and what to do in future to prevent such mistakes hence for a data analyst a good understanding of business data and statistics is essential tools and languages that would be most commonly used as a data analyst would be Excel, SQL and in some cases Tableau also. There might also be a BUSINESS INTELLIGENCE PROFESSIONAL who is responsible for creating weekly dashboards to inform the management about weekly sales of different products the average delivery time or the number of daily cancellations of order.
Then let’s understand role about a DATA SCIENTIST, the rare gem would then use the data that has been existing in the organization to design business oriented machine learning models as a starting point data scientists can go through the available data of the company to look at various buying patterns identify similar items on the website and also identify similar users then he will create algorithms around the same so that the website can automatically recommend products to the users based on the navigation histories, purchase histories, now this solution has to be effective enough that it can predict the future purchases in real time for visitors of the website now the way this is different from data analyst role is that data analysts are expected to perform a lot of ad hoc analysis which can facilitate decision making within an organization data scientist on the other hand not only perform ad hoc analysis and create prototypes but they also create data products that make intelligent decisions by themselves and this is where machine learning becomes extremely critical for instance, the suggestion you get after you buy a particular item or on the basis of the items that you have on your wish list are because of machine learning models built by data scientists the requisite skill for a data scientist is knowledge of algorithms, statistics, mathematics, machine learning and programming languages such as Python, C equal as and height they should also have some business understanding and the aptitude to frame the right questions to ask and find the answers in the available data finally a data scientist should be able to communicate the results effectively to the team members and all the involved stakeholders.
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