3 Types of Data Experts

Data Science has been the most rising career during and after the Covid-19 pandemic. The rise in popularity of Data Science among college graduates has paved the way for several new professional occupations to emerge, each with great opportunities and benefits. Among the most promising and popular Data Science career pathways are Data Scientist, Data Engineer, and Data Analyst. Continue reading this article to learn more about 3 types of data experts.

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3 Types of Data Experts

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Data Science has been the most rising career during and after the Covid-19 pandemic. LinkedIn has witnessed a 650% increase in Data Science jobs since 2012. Forbes, in turn, has named Data Science one of the top ten fastest growing jobs going into 2030.

The trajectory of Data Science jobs is, indeed, astonishing. The rise in popularity of Data Science among college graduates has paved the way for several new professional occupations to emerge, each with great opportunities and benefits. Among the most promising and popular Data Science career pathways are Data Scientist, Data Engineer, and Data Analyst.

Have you ever wondered about the difference between the three of them, or about the skills and responsibilities required for each one? In this article, we will clarify these things for you.

Data Scientist

This job is probably best known as "the sexiest job" of the 21st century as declared by Harvard Business Review, back in 2012; and it is ranked second in Glassdoor's annual ranking of the 50 best jobs in the U.S. in 2021.

Data scientists are polyvalent, sharp-witted experts that combine analytical, statistical, and programming skills to collect, analyze, and interpret data.

Responsibilities:

Data scientists are expected to:

  • Have sharp technical skills to spot business-related problems and solve them.
  • Have good story-telling techniques and in-depth management skills in order to efficiently convert findings into effective, data-driven business strategies.
  • Work with both structured data and unstructured data.
  • Be well-versed in Machine Learning to build and maintain powerful predictive models.

Skills:

Key skills for a data scientist include:

  • In-depth knowledge of programming languages, such as SPSS, R, Python, SAS, Stata.
  • Solid understanding of Statistics and Machine Learning.
  • Data visualization and communication skills.
  • Solid grasp of Big Data software tools like Hadoop and MapReduce.
  • Ability to work with unstructured data.
  • Sound skills in databases such as MySQL and Postgres.

Data Engineer

Data engineers work in a variety of settings to build systems that collect, manage, and convert raw data into usable information. They work hand in hand with data analysts and data scientists. Their ultimate goal is to prepare data scientists to use data to promote better business decisions.

Responsibilities:

Data engineers are in charge of:

  • Providing data scientists with data in usable formats that can be easily analyzed.
  • Dealing with both structured and unstructured data.
  • Developing, building, testing, and maintaining architectures including databases and large-scale processing systems.

Skills:

Key skills for a data engineer include:

  • Extensive knowledge of programming languages like Python, R, Ruby, Scala and Java.
  • Solid grasp of ETL tools.
  • In-depth experience in SQL and NoSQL technologies like Cassandra and MongoDB.
  • Good understanding of data warehouse, data lakes and big data technologies including Hadoop, Pig, Hive, and Spark.

Data Analyst

Data analysts are hired to help organizations analyze trends in the market, better understand their clients' needs, and improve their performance indicators. They help stakeholders to better understand data and use it wisely to make deliberate business decisions.

Responsibilities:

Data engineers are in charge of:

  • Providing data scientists with data in usable formats that can be easily analyzed.
  • Dealing with both structured and unstructured data.
  • Developing, building, testing, and maintaining architectures including databases and large-scale processing systems.

Skills:

Key skills for a data engineer include:

  • Extensive knowledge of programming languages like Python, R, Ruby, Scala and Java.
  • Solid grasp of ETL tools.
  • In-depth experience in SQL and NoSQL technologies like Cassandra and MongoDB.
  • Good understanding of data warehouse, data lakes and big data technologies including Hadoop, Pig, Hive, and Spark.

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