What does a data engineer do? How does that differ from what a data analyst’s role is? Who is a data scientist then?! Confused? Here’s an attempt by us to put these Data Analytics roles into different buckets. It’ll answer all these questions for you.
If you are looking to transition from IT, or any other field, to Data Analytics, then you may want to go through this carefully to understand your options. Moreover, if you are already in this field, and are considering a shift or upgrade, then here are all your options mapped out for you!
Data Analytics is an evolving field and although these roles are defined loosely. There are 4 buckets within Data Analytics roles.
Let’s take the example of an e-commerce company. Let’s try and understand all these different types of Data Analytics roles. Check out the video or infographic or just continue reading below!
As shown in the infographic here’re the top Data Analytics Roles:
A data engineer creates the platform and the data structure within which all the data from the users is captured. For example, the items they buy, what is in their cart currently as well as on their wish-list. Data engineers should make sure that the captured data is stored in such a fashion that it is not only efficient but also easily retrievable.
They are comfortable in working with varied data sources, write ETL queries to collate data from all of them. Then they organize all this data in data warehouses or databases. So that, others in the company can make the best use of it.
To become a data engineer you need to acquire knowledge of languages such as Python, Java, SQL, Hadoop, Spark, Ruby, and C++. You should note, however, that knowledge of all of these is not mandatory but varies from company to company.
As a data engineer, you would be sitting at the rare intersection of a software engineering professional and a data analyst.
Data analysts are expected to draw insights from the data, which directly impacts business decisions. They are directly involved in day-to-day business activities. There are a lot of ad hoc analyses that a data analyst or a business analyst is expected to do.
For example, a data analyst in an e-commerce company helps the marketing team identify the customer segments that require marketing, or the best time to market a certain product, or why the last marketing campaign failed and what to do in the future to prevent such mistakes. Hence, for a data analyst, a good understanding of business, data, and statistics is essential.
The tools and languages that would be most commonly used by a data analyst would be Excel, SQL, and R, and in some cases Tableau as well.
Data Visualiser/Business Intelligence Professional
There might be a data visualizer or a business intelligence professional at this e-commerce company. They are responsible for creating weekly dashboards to inform the management about various metrics. These metrics include weekly sales of different products, the average delivery time, or the number of daily cancellations of orders.
A data scientist uses the data that the organization holds, 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 identify similar users. Then, they will create algorithms around the same so that the website can automatically recommend products to the users based on their navigation histories, purchase histories, and other such metrics. This solution must be effective enough that it can predict the future purchases, in real-time, for website visitors.
The way this is different from a data analysts’ role is that data analysts are expected to perform a lot of ad hoc analyses which can facilitate decision making within an organisation. Data scientists, on the other hand, not only perform ad hoc analyses and create prototypes, but they also create data products that make intelligent decisions by themselves. This is where machine learning becomes extremely critical.
The requisite tools and concepts for a data scientist is knowledge of algorithms, statistics, mathematics, machine learning, and programming languages such as R, Python, SQL, and Hive. A data scientist should have a business understanding and the aptitude for framing the right questions to ask. They should find the answers in the available data. Then communicate the results effectively to the team members, and all the stakeholders.
We hope that this helps you segment all the different Data Analytics roles and decide where you fit in best. Good luck with your career!
In case you have any questions, please write to us at firstname.lastname@example.org or comment below.