Home » Data Scientist vs. Data Engineer – The Difference in 2022

Data Scientist vs. Data Engineer – The Difference in 2022

by Saloni Singh
Data-Scientists

The field of Data Science continues to evolve every day with newer and prevalent roles like that of data scientists, data analysts, engineers, and data architects becoming restructured and more essential to every industry. Big Data has changed how organizations deal with their problems, making their path of success dependent more on facts than chance. Yet, due to this ever-evolving nature of the world of Data Science, the concept and workings remain unclear to many, leading to errors in the process. If you too would like to enlighten yourself and learn more about Data Science and two of the most essential roles that it has given to the world’s businesses and organizations, let’s dive in further.

What is Data Science?

Data Science is an interdisciplinary field of study which uses methods, algorithms, statistics, and more to derive information and actionable knowledge from a vast world of unstructured prevalent data. Today in the modern world, Data Science applies everywhere from Governments to Financial organizations to consumer-oriented businesses. By using already available data and the right methods, companies can prepare for upcoming business opportunities as well as prepare for oncoming disasters. Data Science can be and is utilized from the most minor to major face-alternating organizational concerns.

Now that we know what the field of Data Science is, let’s take up the essential professional roles that are responsible for executing the theoretical knowledge of Data Science into real-world applications – Data Scientists and Engineers.

Who is a Data Scientist?

Harvard University in a report had ranked the profession of a Data Scientist as one of the most inviting jobs, and we agree. But what is the job of a Data Scientist? They are deemed analytical experts who work on all kinds of structured and unstructured data, study it and model it as they deem fit, and finally analyze it to create executable plans for the company’s future.

To become a Data Scientist, you need to be knowledgeable on many grounds as has been discussed below.

Technical and Soft Skills Required to be a Data Scientist

  • A Data Scientist needs to be well-oriented with at least one programming language if not more. These may be Python, R, C++, Julia, etc.
  • Data Analysis with platforms such as Excel, SQL, Pandas, etc.
  • Heavy Knowledge of Statistics and probability is a must without which machine learning will be impossible.
  • Strong mathematical skills.
  • Needs to know how to use Data Visualization tools such as Tableau.
  • Moderate knowledge of machine learning methods and AI.
  • Data Modeling must be in a Data Scientist’s forte. The popular choice for which is the Hadoop platform.
  • A Data Scientist must be a good communicator to convey their findings to the stakeholders.
  • The Data Scientist must be a curious and critical thinker so that they can think ‘out of the box to provide necessary solutions.
  • A Data Scientist must be a clear storyteller in order to be transparent in conveying solutions and collaborating with teams.

Who is a Data Engineer?

A Data Engineer is responsible for providing the data that Data Scientists can work on. They collect raw data from everywhere, manage it and convert it to usable data that can be studied by analysts, scientists, and so on. They are the Data Handlers of an organization.

Like a Data Scientist, a Data Engineer needs to be skilled on many grounds.

Technical and Soft Skills Required to be a Data Engineer 

  • Acquaintance with coding languages like SQL, Python, Java, Scala, and so on.
  • They must be well oriented with the Extract, Transform and Load System. Popular tools include Talend, Xplenty, Stitch, Alooma, etc.
  • Data Engineers must know about popular Warehousing Solutions and also be aware of relational and non-relational databases for storage.
  • A Data Engineer needs to be familiar with the concept and systems of Machine Learning.
  • Be able in Task Automation.
  • Have a solid grip on cloud computing.
  • Along with Data engineering, comes the need for the ability to keep the data secure and protected.
  • Know about big data and should be able to use big data tools like MongoDB, Kafka, Hadoop, etc.
  • Data Engineers need to be good collaborators and communicators to work fluently with working teams, analysts, and business units, and so on, especially to source data.
  • A Data Engineer may be required to perform data analysis and present results to stakeholders. Thus they must have excellent presentation skills.

Data Scientist and Data Engineer: A Comparison

Data Scientist Data Engineer
Educational Background A Bachelor’s and preferably a post-graduate or an advanced degree in Data Science or Data Analysis. Business Analytics, Data Science and Analytics and other relevant fields are also viable. Bachelor’s Degree in Mathematics, Science or a Business Oriented Field
Knowledge Required and Technical Skills ·   The basics of Data Science

·   A Programming Language (preferably python)

·   Basics of Big Data and Software Engineering

·   Statistics

·   Mathematics

·   Probability

·   AI and Machine Learning Methods

·   Data Modelling

·   Data Visualization

·   Data Analysis

 

·   Multiple coding languages.

·   Database Systems

·   Data Warehousing

·   Machine Learning

·   ETL systems

·   Data Distribution

·   Cloud Computing

·   Data Security

·   Machine Learning

·   Big Data and its tools

Popular Tools Used Apache Hadoop, Tableau, Excel, MongoDB, Python Julia, MATLAB, Google Analytics, Apache Kafka, Jupiter, D3.js, Amazon Redshift, Apache Spark, SAS, Big ML etc. Python, SQL, MongoDB, PostgreSQL, Apache Spark, Snowflake, Amazon Redshift, Apache Airflow, Amazon Athena, Big Query, Tableau, Looker, Apache Hive, Segment, Reddish, Periscope, debt etc.
Popular Programming Languages Python, R, Julia, C++, Java, SCALA, JavaScript, SQL, MATLAB, Swift, SAS etc. Python, Scala, JavaScript, C++ etc.
Salaries A Certified Senior Data Scientist is paid an annual average of around 195,000 USD. Countries like Japan, Canada, Israel, and Australia also pay their Data Scientists quite well. An experienced Data Engineer makes up to 112,493 dollars annually in countries like USA, Australia which pay their engineers the highest.

 

Multinational giant companies like Apple, Amazon, Oracle, Facebook, IBM, Google and many are currently hiring Data science Certification In Austin and Engineers, especially due to the critical role they can play in an organization’s success. In 2022 the top industry options for data scientists are Banking and Financial Services, Media, Healthcare, Telecommunications, Retail, Digital Marketing, Cybersecurity, and so on. Top industry options for Data Engineers on the other hand are Technology, Healthcare, Pharmaceuticals, Internet Industry, Energy, Automotive, and so on.

Conclusion 

So Data Scientist or Engineer? Which professions seem more palatable for you? As established, to be a Data Scientist your resume will indeed need to be a little heavier but the pay is also more than convenient. But a Data Engineer is also extremely prized and may even be paid higher than a data scientist, depending upon the hiring party and company. Want to know how you can amplify your resume and make yourself an appealing hire for these mentioned hiring companies? Reputable Data Science Certification Data Scientists will not only make for the needed difference but also prepare you with all the necessary skills one would need to be a data scientist or engineer.

 

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