Free Quiz
Write for Us
Learn Artificial Intelligence and Machine Learning
  • Artificial Intelligence
  • Data Science
    • Language R
    • Deep Learning
    • Tableau
  • Machine Learning
  • Python
  • Blockchain
  • Crypto
  • Big Data
  • NFT
  • Technology
  • Interview Questions
  • Others
    • News
    • Startups
    • Books
  • Artificial Intelligence
  • Data Science
    • Language R
    • Deep Learning
    • Tableau
  • Machine Learning
  • Python
  • Blockchain
  • Crypto
  • Big Data
  • NFT
  • Technology
  • Interview Questions
  • Others
    • News
    • Startups
    • Books
Learn Artificial Intelligence and Machine Learning
No Result
View All Result

Home » Difference in Data Scientist and Machine Learning Engineers skills

Difference in Data Scientist and Machine Learning Engineers skills

Tarun Khanna by Tarun Khanna
April 17, 2021
in Data Science, Language R, Machine Learning, Python
Reading Time: 6 mins read
0
data-scientist
Share on FacebookShare on TwitterShare on LinkedInShare on WhatsApp

Data scientists and Machine Learning Engineers are both equally in demand and cause an overlap within their roles and responsibilities. Therefore, it is pivotal to know about the skills pre-required for parts that primarily differentiate between them.

Data scientists anticipate working on the modelling side while their work is where to base the same model. Working on the ins and outs of algorithms is for data scientists.

The machine learning engineers work on how to bring the model in a production environment that will even provide a space to interact with its potential users.

Furthermore, the detailed differences in the skills required for these two roles (machine learning engineers and data scientists)

Advertisements

Table of Contents

  • Skills for Data Science
    • Python/R
    • Jupyter Notebook – Popular IDE
    • SQL
  • Machine Learning
  • Skills for Machine Learning
    • Python
    • GitHub/Git
    • Deployment Tools
  • Conclusion

Skills for Data Science

The information provided below is on personal experience context-based in 2021. There are plenty of articles on the web about communication, skills, and tools required in the work of data scientists. In the article, below there are tools applied in daily use by many people.

However, many new and upcoming skills will flourish in the market. Yet, these three skills mentioned below will always remain the eminent ones and are well known for investing money and time.  

Python/R

Python is a popular programming language for data scientists. We can expect every data scientist to use this programming language in their everyday work. There is yet another language in use known as R.

Advertisements

The motive behind using these languages is more or less the same. It involves ingesting data, exploring the data, processing it, feature engineering, model build. It also communicates with the results just with the use of Python. 

Jupyter Notebook – Popular IDE

 Most data scientists prefer to use Jupyter Notebook. It is the preference because the Jupyter Notebook tool acts as one single platform to place code, write text, and project different outputs like results and visualizations.

This tool is the go-to for data scientists, plus this has come to stay and will not change any time soon.

Advertisements

Furthermore, some additional extensions are active to make the coding process a lot easier. However, another popular integrated development environment that is primarily concerned with coding is PyCharm and Atom.

SQL

As data is the base and foundation of any machine learning, it essentially requires a structured query language. The machine learning algorithm eventually becomes part of the final data science model.

In their initial work, the data scientists use SQL for their data science process, querying the first data, creating new features, and, finally, for the data science process.

Advertisements

The final step is where SQL required in the model runs and deployed results saved in the corporation’s database. There are ample amounts of databases/platforms of SQL like MySQL, PostgreSQL, and Microsoft SQL Server. However, it depends upon the company associated. All of these are more or less similar. 

 Moreover, mastering these three skills mentioned above will pave the path for your successful career as a data scientist. Meanwhile, you can learn many others skills and languages to work as a data scientist.

It is common to master the skill-set while holding a job because companies share different tools and need different skills. The points to keep in consideration are:

Advertisements
  1. A programming language
  2. An IDE/visualization platform
  3. A querying language

Machine Learning

The work of machine learning engineers comes into existence after the model is ready by the data scientists. The primary focus is on the in-depth analysis of code and its shipping. For example, there is no need for a machine learning engineer to ponder how random forest works.

However, there is a need to gain sufficient knowledge to save and load a file automatically, predicted within a production environment. In short, they require to be more software engineering-oriented. 

Skills for Machine Learning

Python

Python is a programming language that both data scientists and machine learning engineers should know well. Although, with the similarity of having this programming language, they require more training in Python otherwise. Machine learning engineers tend to focus on object-oriented programming (OOP) in Python.

Advertisements

On the other hand, the data scientists are not equipped with the OOP heavy- mainly concerning their job as required to build the model and focus on the analytics and statistics, not primarily all of the code. There are data scientists and machine learning engineers with the skills to work at both.

There is a requirement of confirmation with the company to be better aware if he/she is more statistics-focused data scientist or more software engineering and machine learning-focused data science. 

GitHub/Git

Engineers usually use Git and GitHub to version and store code repositories. This code management tool and platform is essential for machine learning engineers for code changes and pulls requests.

Advertisements

Generally, data scientists and machine learning engineers are well-equipped with this skill. But, the main focus of machine learning engineers is on Git and GitHub only. 

Deployment Tools

This ability is possibly wherein machine learning engineers and data scientists vary the most. Although a few data scientists understand how to set up a model, and a few corporations require it if the position is machine learning engineer.

They may anticipate the principal part of the task to focus on deploying data science models. In addition to this, there are tools like AWS, Google Cloud, Azure, Docker, Flask, MLFlow, and Airflow, to name a few.

Advertisements

When the title pops up with machine learning engineer, it refers to the machine learning operations engineer, which can be misleading.

The reason behind is the expectation with machine learning engineer is to focus on how machine learning algorithms work and to make sure the part you are hopping into keeps stricken to either algorithm-focused or operations-focused (MLOps) 

Conclusion

To recapitulate, the firms favour all-around scientists capable of data science and machine learning (operations). Plenty of companies will put forward a specialist in one area to have two roles separated on their team. It becomes a lot for one person to try and do everything from beginning to end.

Advertisements

Thus, it becomes possible after having two selected individuals, wherever one individual target model building and the other one focuses on the model deployment, proves as the coherent approach. 

Now, these are some essential skills for each role. These are not the complete package of skills required by machine learning engineers. Yet, these are necessary skills:

  1. Python/R
  2. Jupyter Notebook/IDE
  3. SQL
  4. GitHub/Git
  5. Deployment Tools
Tags: data scienceData scientistsGithubJupyter NotebookMachine LearningMachine Learning EngineerspythonSQL
ShareTweetShareSend
Previous Post

Upcoming Indian DeepTech Ecosystem

Next Post

Top Blockchain Courses for Blockchain Development

Tarun Khanna

Tarun Khanna

Founder DeepTech Bytes - Data Scientist | Author | IT Consultant
Tarun Khanna is a versatile and accomplished Data Scientist, with expertise in IT Consultancy as well as Specialization in Software Development and Digital Marketing Solutions.

Related Posts

Data Science Interview Questions and Answers
Interview Questions

Top Data Science Interview Questions and Answers for 2023

March 21, 2023
deep-learning-guide
Deep Learning

Deep Learning for Beginners: A Practical Guide

January 26, 2023
Machine Learning Prediction Examples
Machine Learning

Machine Learning Prediction Examples

January 22, 2023
future-of-data-science
Data Science

Future of Data Science

January 20, 2023

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

TRENDING

How SSL Encryption Secures Big Data In Cloud Computing?

by Tarun Khanna
April 14, 2022
0
ShareTweetShareSend

5 Ways to Improve the Conversion Rate of Your Website’s Service Pages

conversion-rate website pages
by Tarun Khanna
January 25, 2023
0
ShareTweetShareSend

Stochastic Optimization Algorithms:- A Gentle Introduction

Stochastic-optimisation
by Manika Sharma
February 16, 2021
0
ShareTweetShareSend

Best Free Datasets Resources To Help You In Your Data Science Projects

best-free-datasets
by Tarun Khanna
September 7, 2021
0
ShareTweetShareSend

Top 10 Esteemed Big Data Analytics Companies

data-analytics
by Tarun Khanna
May 14, 2021
0
ShareTweetShareSend

7 Things to track in a Machine Learning Model-Complete Checklist

machine learning
by Tarun Khanna
March 12, 2021
0
ShareTweetShareSend

DeepTech Bytes

Deep Tech Bytes is a global standard digital zine that brings multiple facets of deep technology including Artificial Intelligence (AI), Machine Learning (ML), Data Science, Blockchain, Robotics,Python, Big Data, Deep Learning and more.
Deep Tech Bytes on Google News

Quick Links

  • Home
  • About Us
  • Write For Us
  • Submit Startup Story
  • Advertise With Us
  • Terms of Service
  • Disclaimer
  • Cookies Policy
  • Privacy Policy
  • DMCA
  • Contact Us

Topics

  • Artificial Intelligence
  • Data Science
  • Python
  • Machine Learning
  • Deep Learning
  • Big Data
  • Blockchain
  • Tableau
  • Cryptocurrency
  • NFT
  • Technology
  • News
  • Startups
  • Books
  • Interview Questions

Connect

For PR Agencies & Content Writers:

connect@deeptechbytes.com

Facebook Twitter Linkedin Instagram
Listen on Apple Podcasts
Listen on Google Podcasts
Listen on Google Podcasts
Listen on Google Podcasts
DMCA.com Protection Status

© 2023 Designed by AK Network Solutions

No Result
View All Result
  • Artificial Intelligence
  • Data Science
    • Language R
    • Deep Learning
    • Tableau
  • Machine Learning
  • Python
  • Blockchain
  • Crypto
  • Big Data
  • NFT
  • Technology
  • Interview Questions
  • Others
    • News
    • Startups
    • Books

© 2023. Designed by AK Network Solutions

Welcome Back!

Login to your account below

Forgotten Password? Sign Up

Create New Account!

Fill the forms below to register

All fields are required. Log In

Retrieve your password

Please enter your username or email address to reset your password.

Log In