Monday, September 23, 2019

What is the Difference Between a Data Scientist and a Data Engineer?

The main difference between a data scientist and a data engineer.
A data scientist focuses on advanced mathematics and statistical analysis and interprets complex data and organizes big data.
In contrast, a data engineer involves building the infrastructure and architecture for data generation and transforming data for data scientists.
Data Scientists vs Data Engineers
The Difference between Data Science and Data Engineering

What is the Difference Between a Data Scientist and a Data Engineer?

Data Science vs. Data Engineering

Data science is a mixture of mathematics, statistics, trend-spots, and computer sciences.
The role of a data scientist is to understand large amounts of data and perform further analysis to find trends in the data. 

Data engineering is a data science aspect and a software engineering approach that often focuses on practical applications of data collection and data analysis. 
The role of a data engineer is to design and build massive reservoirs for big data and develop database management systems and large scale data processing tools.

The Difference between a Data Scientist and a Data Engineer

A data scientist is someone who cleans and organizes big data; he focuses on advanced mathematics and statistical analysis of this generated data.

A data engineer, on the other hand, is someone who develops architectures, examines them carefully, builds analytical tools, and maintains large-scale processing systems and databases.

Data Scientists' Responsibilities

Data scientists work between the business and information technology worlds and drive industries by analyzing complex datasets so that companies can take advantage of them. 

Data scientists will typically already have data that has gone through the first round of cleaning and manipulation. They can use that data to feed machine learning programs and advanced analytical techniques and statistical tools so that the data can be used in prescriptive and predictive modeling.

Some of the most common job titles for data scientists include the following:

Data scientists translate a business case into an analytics agenda, make predictions, and interpret data — as well as to measure patterns in how they impact businesses.

Data scientists also find and choose algorithms to help analyze data. They use business analytics to not only describe what impact data has on a company in the future but can also help to design solutions that will help the company move forward.

A senior data scientist can predict what the future needs of a business will be. In addition to collecting data, they analyze it thoroughly to solve highly complex business problems efficiently.
Through their experience, they can not only design but advance the creation of new standards, as well as create ways to use statistical data, and can also develop tools to help analyze data.

Data scientists sometimes involve searching and examining data to find hidden patterns. They have to do research and create business questions to show the hidden obstacles and build better business models.
Sometimes, they have to utilize large amounts of data from internal and external sources to answer emerging questions and meet business needs.

Data scientists and business intelligence analysts use the data to help understand market and business trends by analyzing the data where the company stands.

What Skills are Needed to Be a Data Scientist?
If you are thinking about how to start a career in data science, you will need hard skills like analysis, machine learning, statistics, Hadoop, etc. But if you master it, then you will excel in this type of role too.
You are a great listener and problem solver so you will also need data science training for sure.

Here, we have mentioned the essential data science skills that need to be mastered:
  • Computer science skills.
  • Mathematics, especially statistics, linear algebra, and calculus. 
  • Communication skills.
  • Data analysis techniques for data wrangling.
  • Unstructured Data - Analyzing and Modeling.
  • Python Coding and the R programming language.
  • Hadoop platform and application framework.
  • SQL database skills.
  • Apache spark basics.
  • Data visualization techniques.
  • Machine Learning, deep learning, and artificial intelligence.
Read this article, if you want to know how you can become a data scientist.

Data Engineers' Responsibilities

Data Engineers work in an environment that follows Agile principles and use practices from Scrum and XP.

Data Engineers not only examine the data for their company or business domain but also third-party data, in addition to analyzing data and creating sophisticated algorithms. They build test-driven development (TDD) tools using Python and Node.js to help data scientists analyze data.

Data engineers develop data set processes for data modeling, mining, and production to deliver and distribute the data to data scientists and improve existing data systems and data flow between them.

Data Engineers create an automated code infrastructure using platforms such as Kubernetes, CloudFormation, and Docker.

Data Engineers work closely with users, system designers, and developers to create blueprints that data management systems use to centralize, integrate, maintain, and protect data sources.

Data engineers deal with raw data that contains machine, equipment or human errors.
The data cannot be validated and may contain suspicious records; this will happen without notice and may contain codes or symbols that are system-specific. They identify and solve problems.
This action may involve contacting other technical / non-technical staff.

Data Engineers design and build new data systems as we progress and expand our data using machine learning platforms. They optimize our algorithms that derive data from incoming data sets.

Data Engineers work closely with remote colleagues (technical and non-technical) who also work on data projects.

Data engineers are sometimes required to recommend and implement methods to improve data reliability, quality, and efficiency.
To do this, they need to employ a variety of languages ​​and devices to simultaneously enclose systems or explore the opportunities to obtain new data from other systems so that system-specific codes, e.g., can be created by data scientists in the further processing.

What Skills Does a Data Engineer Need?
If you want to become a data engineer, you must definitely qualify in one or more programming languages.
You must be able to think about the behavior of systems before you start building them.
You must have experience with various data visualization and business intelligence systems
You must be able to communicate clearly in both written and oral forms. 
You must have:
  • Basic language requirements such as R and Python
  • Solid knowledge of operating systems
  • Data warehousing – MapReduce, Hadoop, Apache Spark, HIVE, PIG, Kafka.
  • In-depth database knowledge – SQL and NoSQL
  • Basic machine-learning familiarity

Trying any of the following techniques will be a major reward:
  • AWS
  • NodeJS (or simply JavaScript)
  • MongoDB
  • Postgres
  • Elasticsearch
  • Geographic Information Systems

Here, we have discussed data science and data engineering and the difference between them. We have answered the question; what is the difference between a data scientist and a data engineer and explained their responsibilities and job duties. 

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