What is the difference between a Data Scientist and a Data Engineer?

Introduction:

Data is being mass-produced which Data scientists and Data Engineers are now important more than ever. The job of a Data Scientist is renowned as ‘the sexiest job of the 21st century’ according to Harvard Business Review in 2011. The world now understands the importance of data. More importantly, companies are aware of the possibilities big data presents. We can witness that from the explosion of employment for data scientists and engineers. Data jobs are still open to misinterpretations because this profession is still developing. Many see it as a hazy technical "thing" that may be used to implement their good or service. This misperception could prevent resources from being used effectively. The Bureau of Labor Statistics predicts a 22% rise in job growth from 2020–2030, which is substantially more significant than the typical growth of other occupations, reflecting the recent surge in demand for careers in the field of data science. This need isn't going away as long as organizations continue to prioritize creating, gathering, and analyzing big data to assist them in managing their operations.

The necessary knowledge, abilities, and education may overlap in job ads for data scientists and data engineers. In actuality, an organization's objectives for the two jobs may be comparable. But it is important to remember that there are marked differences between a data scientist and a data engineer.

The field of obtaining useful information and insights from unstructured data is known as data science. People seeking the best-fitting data science profession face a problem because the discipline provides many employment prospects. Sometimes some guidance is beneficial.

Although there are overlaps between a data scientist and a data engineer, there are some key differences:

Data visualization, model construction, team management, communication tools for statistics Mathematics and machine learning are the concerns of a data scientist. On the other hand, data engineers cover Programming dialects, database administration, data conduits, and software-related problems, increasing an organization's effectiveness and access to data.

What are the career options available for a data scientist?

• Data analyst
• Machine Learning Engineer
• Application architect
• Business analyst
• Statistician
• Database administrator
• Business fields such as consultant, sales, product development, and business development

What are the career options available for a data engineer?

• Hadoop developer
• BI developer
• Technical Architect
• ETL developer
• Data warehouse engineer
• Quantitative data engineer
• Data platform engineer
• Data infrastructure engineer
• DevOps Engineer

Objectives of a Data Engineer:

Data engineers prepare the data from raw data, which is unformatted and may contain human or automated mistakes, to address business issues. The data scientists or data analysts continue to study the clean data. Data engineers work to gather, organize, and manage data from a variety of sources in order to increase the effectiveness, dependability, and quality of the data. In addition to writing intricate queries to guarantee data availability, data engineers also create free-flow data pipelines utilizing a variety of big data technologies to allow real-time analyses. To process data, data engineers employ a variety of technologies, including MySQL, Hive, Oracle, Cassandra, Redis, Riak, PostgreSQL, MongoDB, and Sqoop. A data engineer works as an independent body.

Objectives of a Data Scientist

Working with the data that the data engineer has supplied is a data scientist. A data engineer is necessary for a data scientist. A data scientist examines the data and offers recommendations on how the business should operate based on that analysis. To prepare data for use in predictive and prescriptive modeling, data scientists utilize a variety of machine learning and statistical models. Data scientists do research using a vast quantity of data from internal and external sources to forecast, investigate, and evaluate data in order to identify hidden patterns that will serve as the cornerstone of decision-making. Numerous computer languages, including Python, R, SAS, SPSS, Julia, and a number of libraries for data processing and visualization, are used by data scientists.

Data engineering is basically the upkeep of the infrastructure that enables data scientists to examine data and create models. Although the position of "data engineer" is a new one, it has long conceptual origins. Data modeling and system design are to data engineering what fundamental statistics are to data science.

Who deals with big data?

Big Data is fundamentally a unique application of data science when the data sets are very large and handling them involves solving logistical difficulties. Efficiency in gathering, storing, extracting, processing, and interpreting data from these massive data sets is of utmost importance.

Data mining, modeling, description, and visualization are all steps performed by a data scientist. In order for data to be meaningfully used, data must first be collected, pre-processed, and stored. Following this procedure, the data may be extracted, handled, characterized, examined, and utilized to create models that are both descriptive and predictive. This is done by the Data Scientists. Data science is an extremely complicated profession, partly because it incorporates so many different academic fields and technological advancements. Mathematics, statistics, computer science, programming, statistical modeling, database technologies, signal processing, data modeling, artificial intelligence and learning, natural language processing, visualization, predictive analytics, and other fields are all incorporated within data science.

Data Analysts are data scientists while data builders are data engineers. It is important to recognize that data engineers have no say in the decision-making process.

Pre-requisites for a data engineer:

Programming languages like Java, Python, SQL, and Scala are among the ones that data engineers are often adept in. They typically have backgrounds in software engineering. Alternatively, they could hold a degree in statistics or mathematics, which enables them to use a variety of analytical techniques to resolve commercial issues.

Pre-requisites for a data scientist:

Large amounts of data are typically offered to data scientists without any specific business challenges to solve. The data scientist will be required to investigate the data, create the appropriate queries, and report their results in this scenario. Because of this, data scientists must possess a thorough understanding of several approaches in big data infrastructures, data mining, machine learning algorithms, and statistics. To run their algorithms successfully and efficiently, they must also interact with data sets that come in a variety of formats, so they must stay abreast of all the most recent technological advancements.

Salaries of a data scientist and a data engineer:

The average salary for a data engineer is about $142,000 per year and a data scientist earns about $139,000 per year.

The Bureau of Labor Statistics predicts a 22% rise in job growth from 2020–2030, which is substantially greater than the typical growth of other occupations, reflecting the recent surge in demand for careers in the field of data science. This need isn't going away as long as organizations continue to prioritize creating, gathering, and analyzing big data to assist them in managing their operations.

In conclusion, Both the data scientist and the data engineer are now essential to building the millions of data models that organizations utilize today. While it is recommended that data scientists have some data engineering knowledge, prospective data engineers should be careful to flex their analytics muscles as well.