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What Is The Difference Between Data Science (DS) And Data Engineering (DE)

Big Data and Data Science employment responsibilities have varied and branched out at an abnormal rate.Data Scientist and Data Engineer are two of the most promising employment roles with a bright future.

Although Data Scientist has been dubbed the “best job of the twenty-first century,” Data Engineer isn’t far behind. Regardless, both the Data Scientist and the Data Engineer are members of the same team that aims to turn raw data into valuable business insights. Check out our data science courses from prominent colleges if you want to receive professional data science training.

In this article, we will be discussing the difference between Data Science and Data Engineering, as seen through the eyes of Data Engineer and Data Science job profiles.

What is Data Science (DS)?

The study of data is known as data science. It is the process of extracting, analysing, managing, and storing data to get insights. These insights help the businesses in making informed data-driven decisions. Data science uses scientific methods, procedures, algorithms, and systems to obtain information and insights from structured and unstructured data and then implement that knowledge and actionable data to a wide range of application domains. It is one of the popular careers and has a good pay scale. Data processing, machine learning, and big data are all important components of data science. In general, Predictive causal analytics, prescriptive analytics, and machine learning are employed in data science to make judgments and forecasts.

What is Data Engineering (DE)?

Data engineering has arisen as a distinct and related position that works in tandem with data scientists as the data field has matured. Data engineering is a series of processes aimed at building information flow and access interfaces and procedures. Maintaining data such that it is available and usable by others necessitates dedicated specialists – data engineers. In a nutshell, data engineers put up and keep the organisation’s data infrastructure ready for analysis by data analysts and scientists.

Data Engineering vs Data Science

Data Science is a vast and multidisciplinary topic of study that incorporates mathematics, statistics, computer science, information science, and domain expertise from the business world. It uses scientific techniques, methodologies, procedures, and algorithms to extract significant patterns and insights from massive datasets. Big Data, Machine Learning, and Data Mining are three of the most important aspects of Data Science.

On the other hand, Data Engineering is a subset of Data Science that focuses on the practical applications of data collecting and analysis. It concentrates on creating and constructing data pipelines that can collect, prepare, and transform data (both structured and unstructured) into formats that Data Scientists can understand.

Data Engineering makes it easier to build a data process stack that can collect, store, clean, and process data in real-time and prepare it for further analysis. Data Engineers, in essence, develop support systems for Data Scientists.

Comparison of Data Engineers (DE) and Data Science (DS)

Before we go into the differences between Data Engineers and Data Scientists, let’s look at what they have in common. The most crucial point of comparison between the profiles of Data Engineers and Data Scientists is their educational background. Both the experts usually have a background in mathematics, physics, computer science, information science, or computer engineering. Some of the best data science institutes in India offer a variety of data science programs for your ease and interest.

These are the most popular study fields for Data Science career profiles. Data Scientists and Data Engineers are competent programmers who are fluent in Java, Scala, Python, R, C, JavaScript, SQL, and Julia.

Distinctions between Data Engineers and Data Science

The following are the key distinctions between Data Engineers and Data Science:

Job description

The primary distinction between Data Engineers and Data Scientists is one of concentration. Data Scientists are primarily concerned with performing complex mathematics and statistical analysis on the obtained data. In contrast, Data Engineers are mainly concerned with constructing the infrastructure and architecture for data collection.

Data Engineers, as previously stated, design, create, test, integrate, and optimise data collected from various sources. They design free-flowing data pipelines that use Big Data tools and technology to enable real-time analytics applications on complex data. Data Engineers also write complicated queries to improve data accessibility.

On the other hand, data scientists are more concerned with finding answers to critical business concerns such as how to improve corporate operations, cut expenses, and improve customer experience. Data Scientists pose pertinent questions, identify hidden patterns, hypotheses, and finally reach appropriate conclusions using the data format provided by Data Engineers.

Skills

Data Engineers and Data Scientists have quite diverse skill sets. Furthermore, their skill levels differ. A Data Scientist’s analytical skills, for example, will be far more advanced than those of a Data Engineer.

Data engineer Skills

  • Configuration with Database Design
  • Sensor Configuration along with Interface
  • Programming
  • The architecture of the System
  • Distributed Systems

Data Scientist Skills

  • Visualisation of Data
  • Linear Algebra along with Multivariate
  • Deep Learning along with Machine Learning
  • Statistics and Probability
  • Management of the DataBase
  • Cloud Computing
  • Programming
  • The wrangling of the Data

 

So when it comes to skills and duties, data engineers and data scientists have a lot in common. The most significant distinction is one of focus. Data engineers are responsible for developing data generation infrastructure and architecture. On the other hand, data scientists are concerned with advanced mathematics and statistical analysis of the data generated.

Tools

Data engineers deal with advanced programming languages such as Python, Java, Scala, distributed systems, data pipeline technologies ( DataStage, Pentaho, Apache Kafka, and others) and Big Data frameworks such as Hive and Spark.

Data scientists employ advanced analytics and BI technologies such as Tableau Public, QlikView, and Splunk, in addition to Python and Java. Apart from these technologies, Data Scientists rely significantly on machine learning libraries such as TensorFlow, PyTorch, Apache Spark, DLib, Caffe, and Keras, to mention a few.

Salary Package

Both Data Engineers and Data Scientists have a bright future ahead of them, with lucrative annual salaries. Amazon, IBM, TCS, Infosys, Accenture, Capgemini, General Electric, Ernst & Young, Microsoft, Facebook, and Apple Inc. are among the top recruiters for these positions.

According to these definitions, the data scientist has a broader skill set than the data engineer and, as a result, gets paid more.

It’s important to remember that job names can be deceptive. Certain data scientists, for example, are essentially data analysts who examine data using simple summary statistics. Actual data scientists would be paid more than these forms of data scientists. On the other hand, if the data engineer also does real software development work and ETL labor, they will be paid more. As a result, data engineers may be paid more than data scientists on occasion.

Borderline

Data scientists interact with the data infrastructure constructed and maintained by data engineers regularly, but they are not responsible for it. Instead, they are internal clients tasked with undertaking high-level market and business operation research to detect trends and relationships—tasks that require them to engage with and act on data using a range of sophisticated technologies and methodologies.

On the other hand, data engineers support data scientists and analysts by providing infrastructure and tools that they may utilise to offer end-to-end business solutions. Data engineers construct batch and real-time analytical solutions and scalable, high-performance infrastructure for delivering clear business insights from raw data sources. They also conduct complicated analytical projects emphasising gathering, managing, analysing, and visualising data.

You can take a data science course to broaden your understanding and upskill at the same. Institutes like Great Learning, which is one of the best data science institutes in India, offers a PGP data science and engineering among several data science courses at various levels. Choose one that suits your level and advance your career. 

 

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