Data Scientist vs Machine Learning Engineer | DS vs ML



Data science and machine learning field is growing exponentially in recent times. In this video I will be explaining the difference between two very important roles in this field, data scientist and a machine learning engineer. We will evaluate it on three fronts,

⭐️ Timeline ⭐️
00:00 Introduction
00:23 Job Duties
02:53 Skills
05:57 Salaries
12:09 Summary of differences (MOST IMPORTANT)

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❗❗ DISCLAIMER: All opinions expressed in this video are of my own and not that of my employers’.

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49 thoughts on “Data Scientist vs Machine Learning Engineer | DS vs ML”
  1. Thanks for this video explaining the differences and similaries between these two fields.

  2. Now i know im been learing data science all the way called myself machine learning engineer

  3. So, ML Engineer gets an opportunity to build models or they work only on production deployment of the models provided by Data Scientists?

  4. Should I go in the field of Machine Learning after age 32. What is percentage of Job Opportunity in America. Please Reply.

  5. Excuse me sir can u please say which one s scope in future ( btech cse ai and ml or btech cse ai and ds) ?.

  6. thank you for the video , I am a 4th year university computer science student and I am interested in career in machine learning. How good do I have to be in math and statistics ? Is machine learning engineer job stressful or lot of pressure ?

  7. can I have a question? if I’m a biz person and I have lots of ideas but I don’t know who to bring on board, do u recommend me starting to learn how to be a data scientist first? just because I know the biz thx

  8. hello sir, i am a mechanical engineer. what should i choose MS in data science or MS in Machine learning?

  9. Hello Sir! Thank you so much for this Video and this is really very helpful, I request you to please help me in my concern, I am having total 7 years of work experience , working as Team Leader operation in back office, I am good in excel I can read write and create different different types of charts Graphs etc, I wanted to become ML engineer but I am from non tech background , I am learning python currently , My question is will I get a job in ML industry because I am from Non Tech background and may be not that much good enough but I need chance is I am on a right path ?

  10. this is fantastic video! i have a question. engineering colleges offer BE im computer and data science', do think it would be good to take this vs just computer science? can provider your perspective on this.?

  11. What if both my programming and business domain knowledge is bad. Like i can code i know concepts of oop and dsa but not very good with coding but i know ml dl and basic maths needed for ml models. can get a job then?

  12. Can you also compare Artificial intelligence vs machine learning career and salaries ?

  13. Dhaval found your amazing channel yesterday and quickly subscribed.

    Your knowledge, content and help to the community is greatly beneficial and appreciated.

    If I may comment on the Job Duties as below:

    1) Understanding business problem -> here the TECH (is a technical team which is aligned with same LOB as DS community) is responsible to know the data requirement and build a highly efficient, adaptable, scalable repository of the data (implementation can be in data lake, ponds, AWS S3, GRAPH DB, AWS REDSHIFT, RDBMS depending on various factors)
    2) Data Collection -> TECH is responsible for collecting the data in the most efficient means (cost, time, quality to name a few)
    3) Data Cleaning and Exploration -> TECH is responsible to cleanse the data and provide the exploration bedrock (partnership with DS will help build AI into the cleanse process itself)

    Basically DS are specialized in help reach AI and TECH is responsible to provide the raw materials for easily building the desired blocks. Actually many organizations is creating LOB/Centralized Data Team playing TECH role.

    TECH also need to partner with ML Engineer (along with SA, DBA, Network) to ensure the eco system, content is providing the required data in an efficient cost effective manner and easily adapt; scale. Future proofing is critical. Also TECH should plan to design for hybrid cloud to have the ability to move from one cloud vendor to another or back to on-prem. Finally we should also keep an eye on alternative technologies like Quantum, Edge Computing

  14. Great video. I love coding a lot, but I do not have a computer science degree. I have a masters in statistics and two years of work experience building ML algos. After this, I have a 5 year gap without data, during which, I have been working towards my PhD in theoretical economics models. I wish to make a move back to the data world. Would love to hear your advice about which option is better for me to pursue

  15. Sir please make videos in Hindi language + English presentation
    That would be more understandable. And u will get more audience.

  16. can u talk about masters in canada sir like there are many courses mmath ms msac msc can u pls elaborate about thm i am confused!! thank u in advance

  17. Is there any difference between ML Engineer and AI engineer based on their paths and roles in Data Science community?

  18. As I'm thinking about doing MSc in Data Science, so should I change my decision from data science to machine learning ?
    Please help !

  19. Hello sir I wanted to ask that data scientist is demanding filed ds is best carrer option

  20. Thank you so much sir for making this video. And thanks for all the tutorials. Your videos help us a lot. Thank you sir

  21. Dear sir, could you please make a video on how to become a machine learning engineer which includes career path, guidelines and course recommendations?

  22. Just wow. Thanks. Many people really doesn't know the difference between DS and ML engineer. They use these words interchangeably very often. Sad part is famous data science youtube channel doesn't even discuss this.

  23. Could you please share laptop configuration for data science and machine learning …And it's cost

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