Data Scientist vs Data Engineers: Everything You Need to Know Before Choosing the Right Career Path


Job titles in the workplace are often far from exact or precise. It may seem like anyone who works in tech is a programmer, or at least has programming skills, but with the rise of big data, two jobs are in high demand: data engineers and data scientists. The positions may look the same, but they are very different, with less overlap than the names suggest.

Data Engineer and Data Scientist – Two peas in a pod

Imagine a NASCAR car racing team. There is a “Pit Crew” who is responsible for ensuring that the “race vehicle” is in top shape by ensuring that all the different parts of the vehicle are functioning properly so that it can operate under high stress which will be put on the vehicle during the race.

In addition, another very important role is the “racing driver” who is responsible for ensuring that the vehicle is used in an optimized way using different strategies such as when to accelerate, what type of “turn” should be done during driving. turns and other techniques during the race. The driver and the pit crew had to work closely together to make the race a success.

Likewise, Data Engineers and Data Scientists whose functions were previously very fuzzy are becoming essential to a successful data science implementation.

“Data engineers” transform the data into a format ready for analysis. These professionals are generally professional software engineers. Their job includes cleaning data, compiling and installing database systems, scaling across multiple machines, writing complex queries, and strategizing for disaster recovery systems. .

“Data scientists” Usually starts with data preprocessing, which involves cleaning, understanding, and trying to fill data gaps with the help of experts in the field. Once done, they will build models that are really useful for extrapolating, analyzing, and finding patterns in existing data.

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

Data Engineers – Lesser 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 data and validating the information that data scientists use to answer questions.

Data engineers should have a solid grasp of the Hadoop ecosystem, streaming, and large-scale computing. 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 self-driving cars, online shopping, large financial networks, etc., use artificial intelligence, the role of data engineers is considered very critical and in increase.

Data Scientists – The Ubiquitous Role

The role of Data Scientist has been projected as an essential entity for all disruptive technology projects. The Data Scientist primarily focuses on understanding fundamental human abilities such as vision, speech, language, decision making, and other complex tasks, and on designing machines and software to emulate these processes.

The responsibilities of the Data Scientist are focused on finding the right model to solve tasks such as “to augment or replace complex and time-consuming decision-making processes” or “to automate interactions with customers so that they are more natural and more human” or “to discover subtle patterns and make decisions that involve complicated new types of streaming data.”

Data scientists need to have a very good understanding of statistics, machine learning, artificial intelligence concepts, and model building techniques. Knowledge of data visualization and conceptual thinking approaches to problem solving is very critical. Without these, a Data Scientist would not be able to add value to organizations. From a knowledge of tools, generally having a good working knowledge of the R stack and python Data Science (e.g. NumPy, SciPy, pandas, scikit-learn, etc.), one or more deep learning frameworks (eg TensorFlow Torch, etc.) and distributed data tools (eg, Hadoop, Spark, etc.). is required

Data Engineer Vs Data Scientist – “What will get my Ferrari faster and how to get started”

Data engineers and data scientists are in high demand. According to a recent survey by INDEED, in INDIA there will be a need for 200,000 Data Scientists and Data Engineers over the next 5 years. From a salary point of view, the two positions are remunerated equally. A recent survey conducted by LinkedIn suggests that the average salary for a Data Scientist or Data Engineer is around 18 lakhs per year in India and around $ 100,000 per year in the United States.

Since there is a high demand for data science and data engineering skills, a new field called “Computational Data Science” where data engineering concepts and AI concepts are also being brought up. emphasis, is one of the Ivy League’s most sought-after study programs. and other leading universities around 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, while data engineers make sure that data flows smoothly through the pipeline. Both are very essential and have huge demand with limited supply. It all depends on the interests and strength of each. You won’t go wrong choosing either of these professions.

By Dr Venkatesh Sunkad, Professor at the International School of Engineering (INSOFE).

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