Data Analyst Vs Data Engineer Vs Data Scientist

Have you ever wondered how a data scientist differs from a data analyst or a data engineer? What is the distinguishing feature that allows them to look at the facts from a new perspective? Their core TASK is the answer!

A Data Scientist's job is to extract future insights from raw data. A data engineer is responsible for the creation and maintenance of data pipelines. Data analysts primarily perform tasks that have an impact on the company's scope.

Are you still perplexed? Don't worry, this is only a summary. I'm going to provide you with a full comparison of Data Scientist vs Data Engineer vs Data Analyst in this article. First, you'll learn what a Data Scientist, Data Engineer, and Data Analyst are, and then you'll compare.

I guarantee that by the time you finish reading this article, you will have decided on the finest trending Data job for you. So, without further ado, let's get started.

What is Data Analyst?

Data analytics refers to the process of extracting information from a set of data. A person who performs this type of analysis is known as a data analyst. A data analyst extracts data using a variety of methods, including data cleansing, data conversion, and data modelling.

Data analytics is employed in a variety of areas, including technology, medical, social science, and business.

With data analysis, industries can examine market trends, client requirements, and overall performance. This enables them to make informed judgments based on data.

In data analytics, descriptive or summary statistics and inferential statistics are the two most significant methodologies. A Data Analyst also knows how to use a variety of visualisation techniques and tools. 

The ability to convey data is essential for the data analyst. This allows them to share the results with the rest of the team and assist them in finding appropriate solutions.

Data analytics enables industries to execute fast queries in order to produce actionable answers in a short amount of time. This limits data analytics to the industry's short-term growth, where swift action is essential.

SQL and Microsoft Excel are two of the most popular and widely utilised data analysis tools.

What is Data Engineer?

A Data Engineer is an expert in preparing data for analysis. Data engineering also entails the creation of data processing systems and structures.

To put it another way, a data engineer lays the groundwork for numerous data processes. A Data Engineer is in charge of creating the format that data scientists and analysts will use.

Data engineers must deal with both structured and unstructured information. As a result, they require knowledge of both SQL and NoSQL databases. Data engineers enable data scientists to complete their tasks.

Data engineers work with Big Data and perform a variety of tasks such as data cleaning, management, transformation, and deduplication.

A Data Engineer has a deeper understanding of fundamental programming principles and methods. A data engineer's job is quite similar to that of a software engineer. This is because a data engineer is tasked with creating platforms and architecture that adhere to software development requirements.

Developing a cloud infrastructure, for example, to enable real-time data processing necessitates a variety of development principles. As a result, one of a data engineer's roles is to create an interface API.

A data engineer also has a thorough understanding of engineering and testing tools. A data engineer is responsible for handling the full pipelined architecture, including log errors, agile testing, fault-tolerant pipelines, database administration, and pipeline stability.

Tools used by Data Engineers - 

• Hadoop
• Apache Spark
• Kubernetes
• Java
• Yarn
• Python

What is Data Scientist?

In the technology industry, data science is the most in-demand job. It has swiftly been known as the "Sexiest Job of the Twenty-First Century." Almost everyone is talking about data science these days, and businesses are suddenly in need of more data scientists.

While Data Science is still in its infancy, it has spread throughout practically every industry sector. Every business is searching for data scientists to help them improve their performance and productivity.

Data is exploding at a breakneck pace. Advances in computer technology, such as High-Performance Computing, have contributed to this growth. This has provided the industry with a huge potential to extract useful information from data.

Data is extracted by businesses in order to examine and obtain insights into numerous patterns and practices. To accomplish so, they hire data scientists with particular knowledge of statistical tools and programming expertise. Furthermore, a data scientist is familiar with machine learning algorithms.

These algorithms are in charge of foreseeing what will happen in the future. As a result, data science can be viewed as an ocean that encompasses all data activities such as data extraction, processing, analysis, and prediction in order to get the necessary insights.

Data Science, on the other hand, is not a single discipline. It's a quantitative discipline having roots in math, statistics, and computer programming. Industries are qualified to make careful data-driven decisions with the support of data science.

Because data is omnipresent, there is a variety of data science employment available. However, there is a scarcity of data scientists due to the steep learning curve. This has resulted in a gigantic revenue bubble, with data scientists earning handsome salaries as a result.

Data Analyst Vs Data Engineer Vs Data Scientist – Definition

• A data analyst is in charge of taking actions that affect the company's present scope. A data engineer is in charge of creating a platform on which data analysts and data scientists can work. A data scientist is also in charge of extracting future insights from existing data and assisting businesses in making data-driven decisions.
• A data analyst does not take part in the decision-making process directly; rather, he assists indirectly by giving static information about the company's performance. A data engineer is not in charge of making decisions. A data scientist is also involved in the active decision-making process that influences the company's direction.
• A data analyst employs descriptive analysis and static modelling approaches to summarise the data. A data engineer, on the other hand, is in charge of developing and maintaining data pipelines. To acquire insights into the future, a data scientist uses dynamic techniques such as Machine Learning.
• Machine learning knowledge is not required for data analysts. For data scientists, though, this is required. To design solid data systems, a data engineer does not need to understand machine learning, but he does need to understand essential computer principles like programming and algorithms.
• Only structured data is dealt with by a data analyst. Both data scientists and data engineers, on the other hand, work with unstructured data.
• Data visualisation skills are required of both a data analyst and a data scientist. In the case of a data engineer, however, this is not essential.
• Both data scientists and analysts do not need to be familiar with application development or API usage. This is, nevertheless, the most important prerequisite for a data engineer.

Data Analyst Vs Data Engineer Vs Data Scientist – Responsibilities

The main responsibilities of a Data Analyst:

• Analyzing the data through descriptive statistics.
• Using database query languages to retrieve and manipulate information.
• Perform data filtering, cleaning and early-stage transformation.
• Communicating results with the team using data visualization.
• Work with the management team to understand business requirements.

Data Engineer is supposed to have the following responsibilities:

• Development, construction, and maintenance of data architectures.
• Conducting testing on large scale data platforms.
• Handling error logs and building robust data pipelines.
• Ability to handle raw and unstructured data.
• Provide recommendations for data improvement, quality, and efficiency of data.
• Ensure and support the data architecture utilized by data scientists and analysts.
• Development of data processes for data modelling, mining, and data production.

Data Scientist is required to perform responsibilities:

• Performing data preprocessing that involves data transformation as well as data cleaning.
• Using various machine learning tools to forecast and classify patterns in the data.
• Increasing the performance and accuracy of machine learning algorithms through fine-tuning and further performance optimization.
• Understanding the requirements of the company and formulating questions that need to be addressed.
• Using robust storytelling tools to communicate results with the team members.

Data Analyst Vs Data Engineer Vs Data Scientist – Skills

In order to become a Data Analyst, you must possess the following skills:

• Strong mathematical aptitude
• Well versed with Excel, Oracle, and SQL.
• Problem-solving attitude.
• Proficient in the communication of results to the team.
• Should have a strong suite of analytical skills.

Key skills required to become a data engineer:

• Knowledge of programming tools like Python and Java.
• Solid understanding of Operating Systems.
• Ability to develop scalable ETL packages.
• Should be well versed in SQL as well as NoSQL technologies like Cassandra and MongoDB.
• He should possess knowledge of data warehouses and big data technologies like Hadoop, Hive, Pig, and Spark.
• Should possess creative and out of the box thinking.

For becoming a Data Scientist, you must have the following key skills:

• Should be proficient with Math and Statistics.
• Should be able to handle structured & unstructured information.
• In-depth knowledge of tools like R, Python and SAS.
• Well versed in various machine learning algorithms.
• Have knowledge of SQL and NoSQL.
• Must be familiar with Big Data tools.

So that's the difference between a Data Scientist, a Data Engineer, and a Data Analyst. The varied functions and duties of these disciplines were discussed. I hope you now know which role is suitable for you.