what are the differences between a data analyst , a data engineer and a data scientist?

//what are the differences between a data analyst , a data engineer and a data scientist?

what are the differences between a data analyst , a data engineer and a data scientist?

Data has always been vital to any kind of decision. Today’s world is all about data, and no organization today survives without data-driven decisions and strategic plans. There are several roles in the industry today that deal with data because of its unparalleled insight and trust. In this article, we will look at the main differences and similarities between a data analyst, a data engineer, and a data scientist.

Have you ever wondered what distinguishes a data scientist from a data analyst and a data engineer? What is the distinguishing factor that helps them to analyze the data from another point of view? The answer is that being different is their main task!

For many employers of data engineers, data scientists, and data analysts, different names seem to be the same role. In fact, these roles involve a variety of different skills and responsibilities, although they all deal with datasets and play a key role in refining data strategies.

Data engineers create, test, and maintain data ecosystems. These ecosystems are essential for companies, especially data scientists, whose job it is to analyze data to build prediction algorithms. Likewise, what data engineers do is very effective for data scientists. In fact, the data engineer is part of a team of data scientists working collaboratively with data analysts and data scientists.

Data analysts create temporary and regular reports based on past and present data to find answers to business questions. This role is often seen as a dance floor for someone interested in a data-related job.

The difference between the role of data analyst and data scientist is that the scope of work of data analysts is limited to numerical data, while data scientists also work with complex data such as text. The task of a data scientist is to extract important future information and predictions from raw data.

In this article, we have compared these three roles to provide a comprehensive answer based on our experience and online resources.

Who is a data analyst, data engineer, and data scientist?

Data Analyzer:

Most easy-to-use professionals who are interested in working in a data-related job work as data analysts, and qualifying for this role is easy. All you need is a good bachelor’s degree and statistical knowledge. Strong technical skills are a plus and can set you apart from other applicants. In addition, companies expect you to be aware of data management, modeling and reporting practices with a strong understanding of business.

The process of extracting information from a data set is called data analysis, and the data analyst is the person who participates in this form of analysis. A data analyst extracts information through several methods such as data cleaning, data conversion and data modeling.

There are several different industries such as technology, medicine, social sciences, business, etc. that use data analysis. Industries are able to analyze market trends, their customers’ needs and their performance using data analytics, and this method allows them to make accurate data-driven decisions.

Two of the most important techniques used in data analysis are descriptive or summary statistics and inferential statistics. A data analyst is also quite familiar with several imaging techniques and tools. Having a presentation analyst is essential for a data analyst, and this option allows them to share the results with the team and help them come up with the right solutions.

Data analysis allows industries to process fast queries to produce practical results that are needed in a short period of time. Two of the most popular tools used by data analysts are SQL and Microsoft Excel.

Data Engineer:

The data engineer either obtains a master’s degree in a data-related field or gathers good experience as a data analyst. A data engineer needs to have a strong technical background with the ability to create and integrate APIs. They also need to understand the data and optimize their performance.

A data engineer is a person who specializes in preparing data for analytical use. Data engineering also includes the development of operating systems and architectures for data processing. In other words, a data engineer develops the basis of various data operations. The data engineer is responsible for designing the data scientist and data analyst template they need to work on.

Data engineers need to work with both structured and unstructured data, both of which require expertise in SQL and NoSQL databases. Data engineers help data scientists speed up their data operations. Data engineers need to deal with Big Data and be able to perform many operations such as data cleaning, management, transformation, data duplication, and more.

A data engineer who is familiar with the basic concepts and programming algorithms and even has experience can be much more professional. The role of a data engineer is closely followed by that of a software engineer, which is why a data engineer is dedicated to operating system development and architecture that uses software development guidelines. For example, cloud infrastructure development requires various development principles to facilitate real-time data analysis, so creating an API interface is one of the job duties of a data engineer.

In addition, a data engineer has good knowledge of engineering and testing tools. It is the job of a data engineer to manage the entire design to handle logging errors, test speed, build error-resistant paths, manage the database, and ensure a stable path.

Tools used by data engineers

Some of the tools that data engineers use are:

Hadoop

Apache Hadoop is a big data source software platform that has given bread and butter to all engineers. This platform includes a distributed Hadoop or HDFS framework designed to work with the product hardware. It is essential for a data engineer to be fully proficient in Hadoop because it is a standard Big Data platform for many industries.

Spark

Spark is a large data processing and analytics platform provided by Apache. This is an extension of Hadoop that can only handle batch data. However, Spark provides support for batch data as well as streaming data (requires updating).

Kubernetes

Kubernetes was created by Google for clustering, scaling, and automation. This is a new technology that has revolutionized the world of cloud computing.

Java

Java is the most popular programming language used to develop enterprise software solutions. A data engineer needs to be relatively proficient in this programming language to develop data paths and infrastructures.

Yarn

Yarn is part of the Hadoop Core project, which allows multiple data processing engines to handle data in one operating system. This is an efficient tool to increase the performance of the Hadoop computing cluster.

Data management

Data management is one of the basic skills of a data engineer, and SQL is a commonly accepted standard for this activity because they work with SQL databases on a regular basis.

Database systems

Data engineers must be proficient in SQL-based systems such as MySQL, PostgreSQL, Microsoft SQL Server, and Oracle Database, and be comfortable with NoSQL databases such as MongoDB, Cassandra, Couchbase, and Oracle NoSQL Database.

ETL solutions

Data engineers must have ETL tools in their toolkit to build processes for transferring data between systems. Examples of these technologies could be SAP Data Services, StitchData, Xplenty, Informatica and Segment.

Data warehousing software

The ability to set up a cloud-based data warehouse and connect data to it is essential for this role. Some data storage solutions include Amazon Redshift, Panoply, BigQuery and Snowflake.

Ability to encode

Experience with Python or Scala / Java is valuable among other programming languages ​​and in many cases even mandatory. Python is often used for ETL work. Data engineers are expected to learn their development skills, which is not critical for other data roles.

Big data tools

The most popular are Apache Spark, Apache Kafka, Apache Hadoop, Apache Cassandra, the first two being a common need. Likewise, it makes sense to focus on gaining a strong understanding of them. Gaining knowledge of Hadoop-based technologies is also a recurring requirement for this position.

Data scientist:

A Data Scientist is someone who analyzes and interprets complex digital data. While there are several ways to achieve the role of a data scientist, the most integrated way is to gain sufficient experience and learn different data science skills. These skills include advanced statistical analysis, a complete understanding of machine learning, data monitoring, and more.

As mentioned above, a data analyst’s core skill set revolves around data acquisition, management, and processing. On the other hand, a data engineer needs a mediocre level of programming to build complete algorithms with a mastery of statistics and mathematics! And finally, a data scientist must be a master of both worlds, data, statistics, and mathematics with in-depth programming knowledge for machine learning and deep learning.

Data Scientist is the most popular job in the technology sector, which has been crowned as “the most attractive job of the 21st century.” Almost everyone is talking about Data Science, and companies are in dire need of more data scientists. While Data Science is still in its infancy, it still occupies almost every sector of the industry. Every company is looking for data scientists to increase performance and optimize its production.

Top Comments on Data Science:

  1. Today, with the growth of Internet businesses and the equipping of the physical industry with advanced software equipment, there has been a massive explosion in data production, and the advancement of data science technology is helping to perform high-performance computing.
  2. This option gives industries a wide opportunity to extract meaningful information from their raw data.
    Companies extract data to analyze and gain insight into different trends and practices. To do this, they hire a data scientist who has knowledge of statistical tools and programming skills. In addition, a data scientist has knowledge of machine learning algorithms, which are responsible for predicting future events. Therefore, data science can be considered as an ocean that includes all data operations such as data mining, data processing, data analysis, and data forecasting to gain the necessary insights and make the best decision for the future.
  3. Data Science is not a single discipline, it is a quantitative and interdisciplinary field that is the result of sharing mathematics, statistics, and computer programming. With the advancement of technology, interdisciplinary approaches seem to be emerging and expanding. Industries with the help of data knowledge are qualified for accurate data-driven decision-making. Data is everywhere, and as a result, there are many opportunities for data science. However, due to the high learning curve, there are currently few data scientists, and this has led to a huge revenue bubble that provides data scientists with a lucrative salary.
  4. Contrary to popular belief, building machine learning models is just one step in the process that involves a data scientist. After model output and processing, a data scientist can transmit the findings to managers using data visualization. After accepting the results, the data scientists make sure that the work is done automatically and delivered regularly.

Roles, responsibilities, and salaries

The following are the main responsibilities of a data analyst:

  • Data analysis through descriptive statistics
  • Use database search languages ​​to retrieve and manipulate information
  • Filter data, clean and change in the early stages
  • Communicate and provide information with the relevant team through data visualization
  • Conversations with the management team to understand the business needs of the company
  • A little math talent is needed!
  • Fluency in Excel and SQL
  • Have a problem-solving attitude and a set of analytical skills

A data engineer is supposed to have the following responsibilities:

  • Development of construction and maintenance of data architecture
  • Perform experiments on large-scale data platforms
  • Manage error reports and provide a strong data structure
  • Ability to handle raw and unstructured data
  • Make recommendations to the relevant team to improve, increase the quality and efficiency of the data
  • Make sure you use the data structure used by scientists and data analysts
  • Development of data processes for data modeling, data extraction, and production
  • Knowledge of programming tools such as Python and Java
  • Comprehensive understanding of operating systems
  • Ability to develop scalable ETL packages
  • Essential in SQL as well as NoSQL technologies such as Cassandra and MongoDB
  • He must have knowledge of data warehousing and big data technologies such as Hadoop, Hive Pig, and Spark
  • You need to be creative and outside the box.

A data scientist is required to perform the responsibilities:

  • Perform data preprocessing that includes data modification as well as data cleaning
  • Using various machine learning tools to predict and classify patterns in data
  • Increase the performance and accuracy of machine learning algorithms through fine-tuning and further performance optimization
  • Understand the needs of the company and formulate questions that need to be addressed
  • Use powerful storytelling tools to communicate with team members!
  • You need to have mathematical skills and relative statistics
  • Ability to manage structural and non-structural information
  • In-depth knowledge of tools such as R Python and SAS
  • Be proficient in different machine learning algorithms and models
  • Be aware of SQL and NoSQL
  • Familiarity with Big Data tools

The job duties of data scientists and data engineers are almost identical, but a data scientist is the one who takes the lead in all data-related activities. When it comes to business decisions, data scientists are more skilled. Yes, obviously because he has spent more time learning than a data analyst and engineer.

The average salary of a data analyst is $ 67,377 per year (2020). A data engineer can earn $ 116,591 a year, while a data scientist can earn $ 117,345 a year.

Looking at these figures between a data engineer and a data scientist, you may not see much difference at first, but by exploring more numbers, a data scientist can earn 20 to 30 percent more than an average data engineer. Job advertisements for companies such as Facebook, IBM, and many others state an annual salary of $ 136,000.

General comparison and conclusion

A data analyst is responsible for actions that affect the company’s current scope. A data engineer is responsible for creating the platform on which data analysts and data scientists work. And, a data scientist is responsible for discovering future insights from existing data and assisting companies in data-driven decisions.
A data analyst does not participate directly in the decision-making process, but indirectly by providing static insights into the company’s performance. A data engineer is not responsible for making decisions. And, a data scientist is involved in an active decision-making process that influences the company process.
A data analyst uses static modeling techniques that summarize data through descriptive analysis. On the other hand, a data engineer is responsible for the development and maintenance of data pipelines. A data scientist uses dynamic techniques such as machine learning to gain insight into the future.
Machine learning knowledge is not important for data analysts. However, this is mandatory for data scientists. A data engineer does not need machine learning knowledge but needs to have knowledge of basic computational concepts such as programming and robust data system building algorithms.
A data analyst should only deal with structured data. However, both data scientists and data engineers are dealing with unstructured data.
A data analyst and a data scientist need to be proficient in data visualization. However, this is not required in the case of a data engineer.
Data scientists and analysts do not need the knowledge to develop and use APIs. However, this is the most urgent need for a data engineer.

In short:

So, what is it all about the differences between a data analyst, a data engineer, and a data scientist? Is. We have left behind different roles and responsibilities in these areas. I hope you now understand which is the best role for you. I love the job of Data Scientist and I advise you to do your best to have the most attractive job of the 21st century. So what are you waiting for? Start working on yourself and find a good specialization in data science.

Share your comments about this article through the comments section, your comments are appreciated 🙂

 

Sources:

  1. www.edureka.co
  2. www.data-flair.training
  3. www.ncube.com

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