Data Engineers are the less famous cousins of data scientists, but no less important. Here's everything you need to know about Data Scientists and Data Engineers.

Workplace job titles are often far from accurate or precise. It might seem that anyone who works in technology is a programmer, or at least has some programming skills, but with big data on the rise, two jobs are in high demand: data engineers and data scientists. The positions may sound the same but they are very different, with less overlap than the names may imply.

Data Engineer and Data Scientist - Two peas in a pod
Imagine a NASCAR car racing team. There is a "Pit Crew" which is responsible for making sure the "race vehicle" is in peak form by ensuring all the different parts of the vehicle are working correctly so that it can perform under heavy stress that will be put on the vehicle during the race.

In addition, another very important role is the "racing driver" who is responsible for making sure that the vehicle is used in an optimized way by using different strategies such as when to speed, what type of "banking" should be done when turning and other techniques during the race. Both the driver and the pit crew had to work very closely for a successful outcome of the race.

In a similar manner, Data Engineers and Data Scientists whose functions were very blurry earlier are becoming essential for a successful outcome of a data science implementation.

"Data engineers" transform data into a format that is ready for analysis. These professionals are usually software engineers by trade. Their job involves cleaning the data, compilation and installation of database systems, scaling to multiple machines, writing complex queries, and strategizing disaster recovery systems.

"Data scientists" usually start with data preprocessing, which is cleaning, understanding, and trying to fill gaps in the data with the help of domain experts. Once this is done, they will build models which are truly valuable in extrapolating, analysing, and finding patterns in existing data.

We can see from the above responsibilities that both Data Scientists and Data Engineer responsibilities are very critical for a favorable outcome of any Data Science implementation.

Data Engineers - Less known cousin whose rise is coming

Data Engineers are the less famous cousins of data scientists, but no less important. Data engineers focus on collecting the data and validating the information that data scientists use to answer questions.

Data Engineers need to have a solid knowledge of the Hadoop ecosystem, streaming, and computation at scale. In addition, they should be very familiar with common scripting languages and tools, such as PostgreSQL, MySQL, MapReduce, Hive and Pig.

Nowadays, since very large data-intensive projects such as autonomous cars, e-commerce shopping, large financial networks, etc., use Artificial Intelligence, the role of data engineers has been deemed very critical and on the rise.

Data Scientists - The Omnipresent role

The role of Data Scientist has been projected as a must-have entity for all disruptive technology projects. The Data Scientist mainly focuses on understanding core human abilities such as vision, speech, language, decision making, and other complex tasks, and designing machines and software to emulate these processes.

Data Scientist responsibilities are focused on finding the right model to solve tasks such as "to augment or replace complex time-consuming decision-making processes" or "to automate customer interactions to be more natural and human-like" or "to uncover subtle patterns and make decisions that involve complicated new types of streaming data."

Data scientists should have a very good understanding of statistics, Machine Learning, Artificial Intelligence concepts and model building techniques. Knowledge of Data Visualization and Design thinking approaches to problem solving is very critical. Without these, a Data Scientist would be unable to add value to organisations. From a tools knowledge, typically having a good working knowledge of the R and python Data Science stack (e.g., NumPy, SciPy, pandas, scikit-learn, etc.), one or more deep learning frameworks (e.g., TensorFlow Torch, etc.), and distributed data tools (e.g., Hadoop, Spark, etc.). is required.

Data Engineer Vs Data Scientist - "Which will get me my Ferrari quicker and How to Start"

Both Data Engineers and Data Scientists are in very high demand. According to a recent survey by INDEED, in INDIA there is a need for 200,000 Data Scientists and Data Engineers in the next 5 years. From a salary perspective, both positions are equally paid. A recent poll conducted by LinkedIn suggests that the average salary for either a Data Scientist or Data Engineer is around 18 lakhs per annum in India and around USD 100,000 per year in the USA.

Since there is so much demand for both Data Science and Data Engineering skills, a new field called "Computational Data Science" where data engineering concepts and AI concepts are being equally emphasised, is one of the most sought-after degree programmes in the Ivy League and other top universities across the world.

Conclusion - To Be or Not to Be

In conclusion, we can say that data scientists dig into the research and visualization of data, whereas data engineers ensure data flows correctly through the pipeline. Both are very essential and have a tremendous demand with limited supply. It all depends on individual interests and strength. You will not go wrong choosing either one of these professions.

Article source: https://www.indiatoday.in/education-today/jobs-and-careers/story/data-scientist-vs-data-engineers-all-you-need-to-know-before-choosing-the-right-career-path-1854754-2021-09-20

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