Data Science / Machine Learning Apprentice Engineer (2022)

Data Science / Machine Learning Apprentice Engineer

Get ready! We will start accepting applications on 10/7. In the meantime, please review the roles listed and get your application materials and essays ready (no resume submission needed).

Is Data Science / Machine Learning Engineering a good fit for me?

Data Science and Machine Learning (DS / ML) Engineers at LinkedIn develop cutting-edge machine learning models impacting millions of members. The DS / ML track may be a good fit for you have strong quantitative, analytical and problem-solving skills, regardless of the areas in which you developed or to which you apply them. 

There are endless possibilities for the experiences that have prepared you for this apprenticeship. For example, you might be someone: 

  • Working in analytics or software engineering who has been wanting to switch to machine learning or data science 

  • With a background in social science who has applied quantitative methods in that arena 

  • Whose hobby is thinking about improvements to sports statistics like expected goals 

Explore Data Science and Machine Learning Further!

  • Learn about LinkedIn’s ML Team
  • Learn about LinkedIn’s Data Science Team
Data Science / Machine Learning Apprentice Engineer (1)
Apprenticeship DetailsApplication QuestionsHow To ApplyFAQ

More about the DS / ML Engineering Apprenticeship

As an  Apprentice Engineer, you will be placed on either a Data Science or a Machine Learning engineering team at LinkedIn developing cutting-edge machine learning models impacting millions of members, learning from fellow engineers and managers, and fostering key skills applicable to a career in Data Science or Machine Learning. Furthermore, apprentices are guaranteed a percentage of time to focus on their personal technical development, using both LinkedIn’s internal ecosystem and external educational opportunities.The time in program is a one year minimum and a five year maximum in the apprenticeship.

At LinkedIn, we trust each other to do our best work where it works best for us and our teams. This role offers both hybrid andremote work options. This means you can work from home and commute to a LinkedIn office, depending on what'sbest for you and when it is important for your team to be together, or you can work remotely frommost locations within the country listed for this role.

For those considering hybrid option, the majority of our engineering teams are hybrid out of our Sunnyvale, CA office. However, we do have some teams in our San Francisco office and on occasion, our New York City office.

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This role is not eligible for visa sponsorship. Applicants must be authorized to work in the US for LinkedIn without requiring visa sponsorship now or in the future. 

Job Responsibilities: 

  • Contribute a unique perspective and creative approach to solving problems at LinkedIn
  • Continue to learn and develop machine learning and data science skills
  • Under the mentorship and guidance of seasoned LinkedIn engineers, produce high-quality software that is tested, code reviewed, and checked in regularly for continuous integration
  • Solve difficult problems with machine learning, write code to put those solutions into production or inform business decision making, and deliver with an appropriate amount of urgency and quality
  • Develop machine learning models that will serve our 740 million members onLinkedIn.com

Basic Qualifications: 

  • An undergraduate or graduate degree (bachelor’s, master’s or doctorate) in any field
  • Demonstrated history of independent problem solving, data-driven thinking, and quantitative skills. These skills may have been gained or demonstrated as part of a degree program or prior work experience.  These do not have to be in a technology setting, but rather any situation in which you used math to help drive decisions (e.g.What supplies to order in a logistics setting, where to place new stores as an analyst, or working on marketing analytics project)
  • Demonstrated history ofData Science or Machine Learning-related related projects. There is a wide range of examples that will qualify, including but not limited to:
    • Open-source contributions
    • Personal analytics or machine learning projects
    • Analytical,mathematical, orstatistical work as part of a job or research

Preferred Qualifications: 

  • Understanding of CS basic concepts: variables, recursion, algorithms, data structures, object orientation, error handling, etc. Knowing what some of these are will make it easier for you to learn more complex software topics
  • Basic knowledge of common machine learning techniques such as regression, clustering, and tree-based methods
  • Compelling desire to have a career in Data Science - Machine Learning and a strong passion for the subject
  • Strong collaborative skills
  • Ability to clearly articulate your perspective
  • Entrepreneurial mindset to bring in a new and unique perspective to the team
  • Desire to learn and develop skills in the fields of Data Science and/or Machine Learning

 Application Requirements

Our application process is designed to give individuals the opportunity to show us a range of qualities we believe will make them successful engineers. This includes their drive and ability to learn, tenacity and work ethic, unique perspective and passion for the role. As part of the application process, individuals are required to submit responses to all components of the following questions and responses will be reviewed for completeness as well as content.

Please note, resumes and LinkedIn profiles will NOT be considered as part of the evaluation for REACH.Therefore, please make sure that any information you would like us to know is highlighted in your application responses.

While LinkedIn profiles will not be reviewed in our hiring process, you must have a LinkedIn profile when applying in order for LinkedIn to receive your application through our applicant tracking system. In case you are new to LinkedIn or if you’d like some help in updating your profile, please visitthis pageorthis videoto see tips for creating a LinkedIn profile.

Application Questions

1. Your Personal Story and Impact

Please answer all parts listed below. We recommend your complete answer to this question be between 400 and 700 words.

a. At LinkedIn, we strive for a culture that embraces and represents diverse ways of thinking, background, and approaches to solving the world’s problems. Tell us how your unique experiences and background shape the point of view that you will bring to LinkedIn. We are looking to understand your unique perspective, story, and background, along with how that influences the point of view you will bring to LinkedIn and your work in Data Science / Machine Learning.

b. We are looking for apprentices who are committed to reaching their goal of becoming an engineer. Tell us how you have demonstrated continuous perseverance and tenacity to achieve a long-term goal or overcome challenges and setbacks throughout your life.

2. Your Journey into Data Science /Machine Learning

Please answer all parts listed below. We recommend your complete answer to this question be between 500 and 900 words.

a. We recognize that there are many paths to Data Science and Machine Learning and we’d like you to walk us through yours. Tell us what sparked your interest in the subject and why you decided to explore that interest.

b. How have you grown your skills independently or formally (consider personal projects, volunteer work, bootcamps, courses, or professional roles)? Tell us about the last technical topic you learned or are learning right now, and what your approach was. Beyond applying to this program, share how you plan to continue mastering machine learning and programming and how it fits into your long-term goals.

c. What appeals to you specifically about the Data Science /Machine LearningApprentice Engineer role over the other REACH apprenticeship roles? We recommend you reference the position guidance to understand the role you are applying to. We are looking to understand how this interest has been demonstrated. Consider projects you have worked on, courses you have taken, prior positions you have enjoyed, etc.

3. Your Engineering Talent

Please answer all parts listed below. We recommend that your complete answer to this question be at least 400 words, but please use as many words as you need so that we fully understand your coding examples. In your answer, we would ideally like to see direct links to code you have written that you are most proud of, and that demonstrate your awesome ability. For example, you can send us a link to yourGithubproject, but even better would be a link to files inside theGithubproject that shows off your skills! We also love demo sites, videos, and outside of the box thinking! Do not use formatting or hyperlinks. Include the full URL link to any media (YouTube, GitHub, Vimeo, etc.).

If you cannot share a link to the code that you’ve written, please make sure to describe the project in detail and give us as much information as you can about the project. We will need to understand your project without seeing the code, so please help us to do that.


At LinkedIn, every Data Scientist and Machine Learning engineer is responsible for both modeling and engineering work. Part of our evaluation process for this program is to understand your ability in both machine learning and programming.

a. Tell us about the machine learning project you are most proud of, whether it is one you completed in the past or are currently working on. You can share links to code you’ve written, a product or website you’ve built, an open-source project you’ve contributed to, and/or your favorite problem that you’ve solved. Where possible, please include independent or solo projects where we can see your direct contributions. If your examples include group work, please describe your individual contribution to the project.

b. In addition to your examples, please tell us what your objective was, what machine learning model, statistical model or embedding you trained, what results you achieved and the challenges along the way. Highlight how this coding work demonstrates your interest in this particular role.

4. Undergraduate Degree

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Please state any undergraduate degrees you have completed. It is a requirement for this position to have an undergraduate degree and for the degree to be noted here for us to consider the basic qualifications for this position met.

Accepting applications beginning 10/7!

How to Apply

  1. Review the job descriptions and application questions in the “Apprenticeship Roles- Winter 2023 Cohort” section (details will be posted on October 7th, 2022). Please identify the role(s) you feel are the best fit.While we invite you to apply to multiple roles, you can move forward with at most one role. We, therefore, advise you to only plan to apply for the roles you would want to be hired into, and for which you are qualified.
  2. Review the application questions for the positions you are interested in (posted in the role-specific pages) and draft your responses per the guidelines given.
  3. Submit your application and essay application responses during the posting date, which will go live on October 7th, 2022. The posting period will be open for 2 weeks.

What to Expect During Hiring

Data Science / Machine Learning Apprentice Engineer (2)

Essay Application:

Our essay application process is designed to give individuals the opportunity to show us a range of qualities we believe will make them successful at LinkedIn. This includes their drive and ability to learn, tenacity and work ethic, unique perspective and passion for the role. As part of the essay application process, candidates are required to submit responses to all components of the four application essay questions. Responses will be reviewed for completeness as well as content. Candidates are expected to submit their responses by the application deadline (October 21st, 2022).

Data Science / Machine Learning Apprentice Engineer (3)
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Take-home Project: 

A recruiter will contact you if you are selected for virtual interviews following initial application review. You will be asked to complete and submit an independent take-home project prior to the virtual interviews (early November- mid December).

Data Science / Machine Learning Apprentice Engineer (4)

Virtual Interview:

During the virtual interview, candidates will go through two interviews (one focused on technical skills and the other on soft skills) and a REACH Meet & Greet. In the technical interview, candidates will be expected to explain and extend their solution to the previously submitted take-home challenge. During the soft skill interview, a manager will get to know you beyond your technical skills (early November- early January).

Data Science / Machine Learning Apprentice Engineer (5)

Offer: 

Candidates who receive an offer will find out more details about their future team and the program, including their minimum time in program based on their current experience and skill level (by late January).

Data Science / Machine Learning Apprentice Engineer (6)

Start date: 

There will be a set hiring date so that apprentices will start in groups and go through a custom REACH onboarding experience together. Your recruiter can provide further detail on this start date during the hiring process so that there is sufficient time to prepare (February).

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FAQ

Q: When is the next application period?  
A:We accept applications a few times a year. The dates will be posted on this site once confirmed.

Q: How many apprentices are you accepting? 
A: We expect to hire approximately 15-35 apprentices each cycle. Exact number of hires will depend on the program’s capacity as well as business need at the time. 

Q: What roles are you hiring for? 
A:  Our available roles differ by cohort. In general, we have Software Engineering (Backend, Frontend, Mobile, etc.), Data Science / Machine Learning, and other roles (User Experience Researcher, Technical Program Manager, Cyber Security). Please check our job description section.

Q: Is relocation offered? 
A:  Yes, relocation is offered. Our standard relocation policies and packages apply. 

Q: What office location will these roles be based in? 
A:  At LinkedIn, we trust each other to do our best work where it works best for us and our teams. This role offers both hybrid andremote work options. This means you can work from home and commute to a LinkedIn office, depending on what'sbest for you and when it is important for your team to be together, or you can work remotely frommost locations within the country listed for this role.

For those considering the hybrid work option, the majority of our teams are hybrid out of our Sunnyvale, CA office. However, we do have some teams in our San Francisco office and on occasion, our New York City office.

Q: Will LinkedIn sponsor my visa? 
A: Apprentice roles are not eligible for visa sponsorship. Applicants must be authorized to work in the US for LinkedIn without requiring visa sponsorship now or in the future. 

Q: How is team placement determined? 
A: The program team will determine which team an individual joins, keeping in mind the apprentice’s interests. Team assignments will be based on several factors in order to set the individual up for success.  

Q: Are all applications reviewed?
A: Based on high volume of interest, we will not be able to review all applications. However, all applicants within this period will have an equal likelihood of review (i.e. applications will not be reviewed on a first come first serve basis).

Q: Do you need a LinkedIn profile in order to apply?
A: While LinkedIn profiles will not be reviewed in our hiring process, you must have a LinkedIn profile when applying in order for LinkedIn to receive your application through our applicant tracking system. In case you are new to LinkedIn or if you’d like some help in updating your profile, please visit this page or this video to see tips for creating a LinkedIn profile.  

Open Tracks

Check out theHow to Applysection for general application and hiring information.

Disclaimer: We invite you to apply to multiple roles, however, you can move forward with at most onerole. We, therefore, advise you to only apply to roles you would want to be hired into.

  • Mobile Apprentice Engineer
  • User Interface (UI) Apprentice Engineer
  • Backend Apprentice Engineer
  • Data Science/Machine Learning Apprentice Engineer
  • User Experience Research (UXR) Apprentice

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FAQs

Do machine learning engineers need to know data science? ›

The most relevant skills that data scientists need to learn to become an effective machine learning engineer is software engineering including the ability to write optimized code, preferably in C++, rigorous testing, and understand and build and operate existing or custom tools and platforms for reliable model ...

Who earns more data engineer or ML engineer? ›

machine learning engineer: who makes more? At present, machine learning engineers make more, but the data scientist role is a much broader one, so there is a wide variety of salaries depending on the job's specifics.

Can data engineer become machine learning engineer? ›

Data engineering isn't always an entry-level role. Instead, many data engineers start off as software engineers or business intelligence analysts. As you advance in your career, you may move into managerial roles or become a data architect, solutions architect, or machine learning engineer.

Is ML engineer and data engineer same? ›

Machine learning engineers are further down the line than data scientists within the same project or company. A data scientist, quite simply, will analyze data and glean insights from the data. A machine learning engineer will focus on writing code and deploying machine learning products.

Who gets paid more AI engineer or data scientist? ›

According to PayScale, the average data scientist salary is 812, 855 lakhs per annum while the artificial intelligence engineer salary is 1,500, 641 lakhs per annum.

Do ML engineers need SQL? ›

SQL is needed for machine learning. It is the de facto standard language for querying data; it is required to format data to be used by machine learning algorithms for improved pattern detection.

Is machine learning harder than data science? ›

When compared to the traditional statistical analysis techniques, machine learning evolves as a better way of extraction and processing the most complex sets of big data, thereby making data science easier and less chaotic.

Is it difficult to become machine learning engineer? ›

Is it hard to become a machine learning engineer? Becoming a machine learning engineer requires commitment. The role is multidisciplinary, requiring the technical development skills of a software engineer and the analytical skills of a data scientist.

Who earns more AI ML or data science? ›

The average salary of a Machine Learning Engineer is more than that of a Data Scientist. In the United States, it is around US$125,000 and, in India, it is ₹875,000.

How do I get an ML job with no experience? ›

How to get a machine learning job without a degree
  1. Learn the required skills. ...
  2. Competitions. ...
  3. Building your own projects. ...
  4. Open source projects. ...
  5. Create a machine learning blog. ...
  6. Hackathons. ...
  7. Consider a bootcamp. ...
  8. Go to networking events.
13 May 2019

Can I learn machine learning in one month? ›

1 Answer. NO! you cannot learn Machine learning in one month and even if you did cover the topic, then also it wouldn't be fruitful to you as you might not have grasped the subject's depth and because of lack of practice, you will not be technically strong.

Which is better full stack or data science? ›

Both offer a lot of opportunities. If you're interested in data analysis and working with data, then data science is the way to go. If you're interested in web development, then full stack development is the way to go.

Can I be both a data scientist and ML engineer? ›

Of course, there are data scientists and machine learning engineers who are great at both, and some companies will make this a requirement, of which you will need to confirm with them so that you know if you will be a more statistics-focused data scientists or a more software engineering and machine learning-focused ...

Are ML engineers in demand? ›

Employment website Indeed.com has listed machine learning engineer as #1 among The Best Jobs in the U.S., citing a 344% rate of growth and a median salary of $146,085 per year. Overall, computer and information technology jobs are booming, with employment projected to grow 11% from 2019 to 2029.

Should I study machine learning or data science? ›

According to US News, data scientists ranked as third-best among technology jobs, while a machine learning engineer was named the best job in 2019 [1, 2]. If you decide to learn programming and statistical skills, your knowledge will be useful in both careers.

Are ML engineers paid more? ›

On average, Machine Learning Engineers are paid higher salaries than regular software engineers.

Why are machine learning engineers replacing data scientists? ›

Data scientists will still be needed by many companies, to solve new or more complex problems. But once the hype is over, there will be less “data scientists” making the work of data analysts or reinventing the wheel for problems that can be solved easily with pre-made solutions.

Which is better AI and ML or data analytics? ›

Data science focuses on managing, processing, and interpreting big data to effectively inform decision-making. Machine learning leverages algorithms to analyze data, learn from it, and forecast trends. AI requires a continuous feed of data to learn and improve decision-making.

What skills do machine learning engineers need? ›

Skills Needed for Becoming a Machine Learning Engineer
  • Applied Mathematics. Maths is quite an important skill in the arsenal of a Machine Learning engineer. ...
  • Computer Science Fundamentals and Programming. ...
  • Data Modeling and Evaluation. ...
  • Neural Networks. ...
  • Natural Language Processing. ...
  • Communication Skills.
5 Sept 2022

What language is best for machine learning? ›

First, let's look at the overall popularity of machine learning languages. Python leads the pack, with 57% of data scientists and machine learning developers using it and 33% prioritising it for development.

Is SQL still relevant 2022? ›

Yes, SQL mastery is still valuable in 2022. This is because SQL is a popular language in programming and is a top choice for software applications to this day. Many of the top RDBMS frameworks use SQL. SQL experts are flexible and versatile in handling various database management systems.

Is data science harder than AI? ›

Data Science vs Artificial Intelligence – Key Difference

The tools involved in Data Science are a lot more than the ones used in AI. This is because Data Science involves multiple steps for analyzing data and generating insights from it. Data Science is about finding hidden patterns in the data.

Can I learn machine learning without data science? ›

Because data science is a broad term for multiple disciplines, machine learning fits within data science. Machine learning uses various techniques, such as regression and supervised clustering. On the other hand, the data' in data science may or may not evolve from a machine or a mechanical process.

Which is best AI or data science? ›

If you want to go for research work then preferably the field of data science is the one for you. If you want to become an engineer and want to create intelligence into software products then machine learning or more preferably AI is the best path to take.

Why is ML so hard? ›

Factors that make machine learning difficult are the in-depth knowledge of many aspects of mathematics and computer science and the attention to detail one must take in identifying inefficiencies in the algorithm. Machine learning applications also require meticulous attention to optimize an algorithm.

Is machine learning a stressful job? ›

However, as an ML expert, you have to follow the traditional 9-5 schedule, but since Machine Learning is a field that demands a lot of study in every project. Therefore, half of the time, you'll be busy learning something new and exciting. This makes Machine Learning an exciting and less stressful career.

Is it easy to be a ML engineer? ›

It's Not An Easy Route, But It's Worth It

Becoming an ML Engineer won't happen overnight, but once you have obtained the correct qualifications, skills, and experience, you will be in a field that provides you with a solid future. It requires a lot of hard work and determination, all you need to do is put in the work.

Do ML engineers train models? ›

These systems are then deployed to production where they can serve real users - this is known as the inference stage. Machine learning engineers manage the entire data science pipeline, including sourcing and preparing data, building and training models, and deploying models to production.

Can data scientist become AI engineer? ›

Statisticians and data scientists can't become AI engineers without knowing how to manipulate data and deploy machine learning models. Software engineers can't become AI engineers without having knowledge of statistics and deep learning.

Can I get a data science job without experience? ›

To learn and get into accurate learning of skills enrolling in a data science course is the best way. There's no need to worry as any interested person can thus become a data scientist without any experience.

How I got my first data science job? ›

You need to have those answers before you start applying for any jobs as it will save you time and effort.
  • Apply to as many relevant jobs as possible. ...
  • Create a spreadsheet to keep track of applications. ...
  • Look at smaller job boards and company websites. ...
  • Check company websites for Data Science jobs.
21 Oct 2021

How do you gain data science experience before your first data science job? ›

How to Get Real-World Data Science Experience
  1. Build a Portfolio of Personal Projects.
  2. Collaborate on Open Source Data Projects.
  3. Take on Data Science Freelancing.
  4. Volunteer for Data Science Work.
  5. Compete in Hackathons and Data Science Competitions.
  6. Solve Practice Problems and Work on Case Studies.
  7. Grow by Teaching.
28 Oct 2021

What should I learn first AI or ML? ›

So, should I learn machine learning or artificial intelligence first? If you're looking to get into fields such as natural language processing, computer vision or AI-related robotics then it would be best for you to learn AI first.

How many hours is machine learning? ›

To learn machine learning, expect it to take 6 months if you dedicate 40 hours a week to just learning it. Expect to study 2 years at 10 hours a week or through practical experience weaved into your job to have a solid foundation with machine learning.

How much time does it take to learn ML? ›

Machine learning courses vary in a period from 6 months to 18 months. However, the curriculum varies with the type of degree or certification you opt for. You stand to gain sufficient knowledge on machine learning through 6-month courses which could give you access to entry-level positions at top firms.

Can an average student become data scientist? ›

Can an average student become a data scientist? Answer: You certainly can. You may become a Data Scientist or Data Analyst if you are enthusiastic about numbers, have a strong grasp of statistics, and are eager to master new analytics technologies.

Who earns more data scientist or web developer? ›

According to information from the job search and company review website Glassdoor, the average yearly compensation for data scientists in India is Rs. 10 lakh. The average yearly income for a full stack developer in India is 6.6 lakhs, with salaries ranging from 2.5 lakhs to 17.0 lakhs.

Which developer has highest salary? ›

Top 10 highest paying software jobs in 2022
  • Data security analyst. ...
  • Data scientists. ...
  • DevOps engineer. ...
  • Mobile app developer. ...
  • Full-stack developers. ...
  • Data warehouse architects. ...
  • Site reliability engineers (SRE) ...
  • System engineer.

Do machine learning engineers write code? ›

Machine Learning Engineers are part software engineers and part data scientists, utilizing their coding and programming skills to collect, process, and analyze data. It's Machine Learning Engineers who create algorithms and predictive models utilizing machine learning to help organize data.

Is ML engineer a good career? ›

Yes, machine learning is a good career path. According to a recent report by Indeed, Machine Learning Engineer is one of the top jobs in the United States in terms of salary, growth of postings, and general demand.

Can machine learning engineer work from home? ›

Machine learning engineers can work from home easily as they can accomplish their main tasks using their computer and various software programs. State-of-the-art project management software, as well as chat and conference programs make working from home secure and efficient.

Does machine learning pay well? ›

An entry-level machine learning salary ranges broadly, but the average is approximately $97,090. However, if you consider potential bonuses and profit-sharing, that number can rapidly rise to $130,000 or more.

Does data science require coding? ›

You need to have knowledge of various programming languages, such as Python, Perl, C/C++, SQL, and Java, with Python being the most common coding language required in data science roles.

Does data science and machine learning go hand in hand? ›

Data Science is just Data Analysis without Machine Learning. Data Science and Machine Learning go hand in hand. Machine Learning makes the life of a Data Scientist easier by automating the tasks. In the near future, Machine Learning is going to be used prominently to analyze a humongous amount of data.

Is data science just statistics? ›

Statistics is a mathematically-based field which seeks to collect and interpret quantitative data. In contrast, data science is a multidisciplinary field which uses scientific methods, processes, and systems to extract knowledge from data in a range of forms.

Do data scientists do machine learning? ›

As a set of tools and concepts, machine learning is applied in data science, but also appears in fields beyond it. Data scientists often incorporate machine learning in their work where appropriate,to help gather more information faster or to assist with trends analysis.

Is machine learning harder than data science? ›

When compared to the traditional statistical analysis techniques, machine learning evolves as a better way of extraction and processing the most complex sets of big data, thereby making data science easier and less chaotic.

Why are machine learning engineers replacing data scientists? ›

Data scientists will still be needed by many companies, to solve new or more complex problems. But once the hype is over, there will be less “data scientists” making the work of data analysts or reinventing the wheel for problems that can be solved easily with pre-made solutions.

Can a data scientist become a ML engineer? ›

So, instead of finding out the difference between data science and machine learning and debating on which one is better, it will be beneficial to know and learn data science because if you learn data science, you will be able to master both of them and can have a career either as a data scientist or a machine learning ...

Can I be both a data scientist and ML engineer? ›

Of course, there are data scientists and machine learning engineers who are great at both, and some companies will make this a requirement, of which you will need to confirm with them so that you know if you will be a more statistics-focused data scientists or a more software engineering and machine learning-focused ...

Which is better AI ML or data science? ›

Data science and machine learning go hand in hand: machines can't learn without data, and data science is better done with ML. As well as we can't use ML for self-learning or adaptive systems skipping AI. AI makes devices that show human-like intelligence, machine learning – allows algorithms to learn from data.

Can I learn machine learning without data science? ›

Because data science is a broad term for multiple disciplines, machine learning fits within data science. Machine learning uses various techniques, such as regression and supervised clustering. On the other hand, the data' in data science may or may not evolve from a machine or a mechanical process.

What should I learn first data science or machine learning? ›

The basis to any attempt to answer the question of which to learn first between Data Science or Machine Learning should be Big Data. Why this is so is very simple. It is on Big Data that both Data Science and Machine Learning are built. These two technologies are unthinkable without Big Data.

Is data science just statistics? ›

Statistics is a mathematically-based field which seeks to collect and interpret quantitative data. In contrast, data science is a multidisciplinary field which uses scientific methods, processes, and systems to extract knowledge from data in a range of forms.

Is machine learning a hard skill? ›

Machine learning involves computation on large data sets; hence one should possess strong basic fundamental skills such as computer architecture, algorithms, data structures, complexity, etc. Getting in-depth into the programming books and exploring new things will be a good advantage.

How hard is it to become a machine learning engineer? ›

Is it hard to become a machine learning engineer? Becoming a machine learning engineer requires commitment. The role is multidisciplinary, requiring the technical development skills of a software engineer and the analytical skills of a data scientist.

Is becoming machine learning engineer hard? ›

A career in machine learning typically requires a Master's of Science degree. The education and training involved in machine learning can require intense dedication, depth of knowledge, and attention to detail.

Is ML engineer a good job? ›

Yes, machine learning is a good career path. According to a recent report by Indeed, Machine Learning Engineer is one of the top jobs in the United States in terms of salary, growth of postings, and general demand.

Which pays more data science or machine learning? ›

According to PayScale data from September 2019, the average annual salary of a data scientist is $96,000, while the average annual salary of a machine learning engineer is $111,312. Both positions are expected to be in demand across a range of industries including healthcare, finance, marketing, eCommerce, and more.

Do machine learning engineer make more than software engineers? ›

Here is a salary summary of machine learning engineers and software engineers: Average Overall Machine Learning Engineer → $112,000. Average Overall Software Engineer → $88,000.

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