Data Engineer vs Data Analyst vs Data Scientist vs Machine Learning Engineer vs Machine Learning Scientist vs BI Developer
The 21st century is all about changing trends and revving to more techno-based advancements. No doubt, the title of this blog seems to be a little intimidating, but trust the author for it is going to interest your concerns. Data Professionalism is a burgeoning field that has surfaced over recent years. It has gained enough momentum to be recognized as the topmost emerging job. We are going to dive into this complex world of data engineering and analysts etc. to get a hint of how these experts work and what they are supposed to do.
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Data Engineer
So the first term that is going to get our attention is, data engineer. A data engineer is someone who has gained enough expertise in the field of data analysis and algorithms. Using complicated algorithms, data engineers create the data or the ‘data infrastructure’ for the client to work on his project. A data engineer gathers all the data for analytical and operational purposes. However, data collection varies from organization to organization but the basics remain the same. The task mainly involves the creation of a ‘data pipeline’ from different sources through integration, consolidation, and cleansing. Data engineers are required in any organization in the data analytics team to help the enterprise with the technicalities of the latest business trends.
Data Analyst
Data collected through data engineers are now transferred to data analysts for further scrutiny. To uncover business trends and to gain more insights, a data analyst works on the collected data set and further processes it so that the stakeholders can get an insight into the future business patterns accordingly. Businesses or stakeholders largely depend on these statistical analyses and their decisions are data-oriented as proposed by the data analysts. In order to determine the organizational goals, data analysts are supposed to work with IT teams and management of the enterprise to come up with logical and methodical approaches. It is to be noted here that the data collected through the pipeline is filtered here and then interpreted with the help of standardized statistical tools.
Data Scientist
Data, after being gathered and scrutinized by the respective experts is now transferred to data scientists. We are all aware that businesses need some technical strategies to align their prospects in concordance with the changing trends. This intricate and technical analysis needs to be searched, dissected, integrated and analyzed again and again to avert any financial mishap. So, a data scientist sifts through the data and analyses the weaknesses and opportunities for the respective organization. They are supposed to extrapolate on all the findings using their mathematical calculations, analysis, and other statistical tools to propose solutions to the vexing problems. One of the key skills of a data scientist is to communicate in order to answer business questions. So an aspiring data scientist would use his critical thinking and domain-specific skills to explain the answers to non-technical clients or stakeholders.
Machine Learning Engineer
While data professionals provide insights to the human audience, machine learning engineers source their information into the software. Machine learning engineers employ their machine learning programs or artificial intelligence to make self-running software. These professionals generate algorithms and programs enabling computers to take actions without being programmed. So, whenever a program is run in that software, it automatically creates a collection of the results to generate more precision and accuracy upon future operations. One such example of machine learning is Netflix. You may have observed that the suggestions that pop up on your computer screens are much similar to the ones you watch. This personalized recommendation is machine learning in its action.
Machine Learning Scientist
A machine learning scientist is a researcher who creates those programs and algorithms used in the action of artificial intelligence. The job of a research scientist is to make research on the available data to build machine learning models to extract patterns and predict product suggestions for companies. They clean the data pipeline and then interpret it to develop a combination of algorithm and data for further programming. These researchers come from academic research fields with their research backgrounds usually relating to ML (machine learning). Machine learning scientists efficiently put their research into action owing to the ongoing breakthroughs in research and data accumulation.
BI Developer
Last but not the least, BI developer is the one who constructs and manages BI infrastructure, tools, and reports. These tools are then used by the analysts and business management teams for further decisions. A BI developer transcribes the computational data to make it understandable for businesses. They present the data in the form of charts, presentations, summaries, and graphs to provide their clients or management teams with comprehensive intelligence about the state of the business. The most productive feature of BI developer is his ability to recognize business’ growth opportunities, and threats. This will in turn enable the business to raise its profit share and employee productivity while reducing costs etc.
