Big Data and Data Science job responsibilities have expanded and spread out at an unprecedented rate since data became the new currency of the 21st century. For those who want a promising career path, Data Engineer and Data Scientist are two of the best options. When it comes to “greatest jobs of this century,” Data Engineer isn’t far behind Data scientists.
The simmering dispute between Data Science and Data Engineering is the focus of today’s essay, which examines the issue through the eyes of Data engineers and Data scientists’ job descriptions. There is no such thing as “Data Science” or “Data Engineering.”
Data Science is an interdisciplinary area of study that encompasses various topics from several academic disciplines. Scientific techniques, methods, and algorithms are used to glean significant patterns and insights from massive datasets in this area of research work. Big Data, Machine Learning, and Data Mining are the three main pillars of Data Science.
On the other hand, Data Engineering is a subfield of Data Science focused on using data collection and analysis in real-world applications. Building and developing data pipelines that can gather, convert (structured and unstructured) data into forms that data scientists can use is the emphasis of this course.
Engineers use data engineering to build the data process stack, which enables real-time or batch collection, storage, cleaning, and processing to prepare it for further analysis. For the most part, Data Engineers are responsible for assisting Data Scientists.
Data Engineer vs. Data Scientist a detailed comparison
For the sake of this discussion, we must first explore the commonalities between Data Engineers and Data Scientists before we get into the contrasts. The educational background of Data Engineers and Data Scientists is the most critical resemblance between their profiles. Both professionals are often educated in one of the following disciplines: mathematics, physics, computer science, information science, or computer engineering.
An engineering background
These academic specialities are highly sought after for positions in the rapidly growing field of data science. Programmers with expertise in Java, Scala, Python, R, C++, JavaScript, SQL, and Julia make up both the Data Engineers and the Data Scientists.
Data Engineers and Data Scientists have fundamentally different roles and responsibilities
Focus is the most significant distinction between Data Engineers and Data Scientists. Engineers develop the infrastructure and architecture for data creation, while Data Scientists analyze and interpret it using complex mathematical techniques.
It has already been stated that Data Engineers are responsible for creating and integrating data gathered from a variety of sources. They build free-flowing data pipelines for real-time analyses on complex data using Big Data tools and technology. To make data more accessible, Data Engineers also construct complicated queries.
Rather than only looking for ways to save expenses and improve the customer experience, Data Scientists are more interested in discovering solutions to business problems like these. To answer relevant questions, Data Scientists look for hidden patterns hypotheses and finally arrive at appropriate conclusions using the data format provided by Data Engineers.
Skills
Data Engineers and Data Scientists have pretty diverse skill sets. In addition, their abilities differ. As an example, a Data Scientist’s analytical skills will be much more extensive than those of a Data Engineer’s.
Data Engineer abilities
” Programming
” Networked computer systems
” Design and setup of the database for the system
” Configuration of sensors and interfaces
Data Scientists have a wide range of abilities:
” Programming
” The term “cloud computing” refers to the use
” Organizing and manipulating data
” Managing data in a computer system
” Visualization of information
” Probability and statistics
” Mathematical analysis of several variables and linear algebra
” Artificial Intelligence (AI) and deep learning
Tools
It is the job of Data Engineers to work with sophisticated programming languages (such as Python and Java), distributed systems (such as Apache Kafka and Talend), and Big Data frameworks (such as Hive and Spark) to analyze large amounts of data.
Even while Data Scientists often use Python and Java, they also employ sophisticated analytics and BI technologies like Tableau Public and Splunk. Many Data Scientists also use frameworks like TensorFlow, PyTorch, Apache Spark, and DLib, as well as Caffe and Keras to train their neural networks.
Package of remuneration
Both data engineers and data scientists have a bright future in front of them, as well as generous yearly salaries to show for it. Amazon, IBM, TCS, Infosys, Accenture, Capgemini, General Electric, Ernst & Young, Microsoft, Facebook, and Apple Inc. are among the top recruiters for these positions.
In India, the average compensation for a Data Engineer is INR 843,140 LPA; in the United States, it is $ 92,260 LPA. INR 813,593 LPA is the average annual income for a Data Scientist in India; it is US$ 96,089 in the United States.
Data Scientists and Data Engineers work together to solve complex problems
The jobs of Data Engineer and Data Scientist complement one another, which must be recognized. To realize the full potential of Big Data, a company’s workforce must be well-versed in both data science and business analytics.
Data Engineers are essential to the work of Data Scientists because they provide the infrastructure necessary for collecting and analyzing large amounts of data. Similar to Data Engineers, the data they create is of little practical value without the research of data scientists.
The IoT Academy is the best-suited platform for individuals willing to get a hold on the career facets related to Data Engineer and Data Scientist. With industry experts at work, you can think of a promising career in the related field.
