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Machine Learning vs. Data Science — Which Has a Better Future?

Last updated on: 
September 6, 2023
Abhinav Rawat
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About The Author!
Abhinav Rawat
Abhinav Rawat
Product Manager at Interview Kickstart. The skilled and experienced mastermind behind several successful product designs for upscaling the ed-tech platforms with an outcome-driven approach for skilled individuals.

Machine Learning and Data Science have been gaining importance over the past few years, and for a good reason. As the world goes more digital and automated, the curiosity among engineers to learn more about these two domains increases even more. If you’re one of these curious engineers and want to get into one of these fields, we’ll help you understand what the future holds for them so you can make an informed decision.

If you are preparing for a tech interview, check out our technical interview checklist, interview questions page, and salary negotiation ebook to get interview-ready! Also, read Amazon Coding Interview Questions, Facebook Coding Interview Questions, and Google Coding Interview Questions for specific insights and guidance on Machine Learning and Data Science tech interviews.

Having trained over 11,000 software engineers, we know what it takes to crack the toughest tech interviews. Our alums consistently land offers from FAANG+ companies. The highest ever offer received by an IK alum is a whopping $1.267 Million!

At IK, you get the unique opportunity to learn from expert instructors who are hiring managers and tech leads at Google, Facebook, Apple, and other top Silicon Valley tech companies.

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It’s worth keeping in mind that what we call a “better future” for a field is a culmination of several factors, including the number of jobs in demand, average salary, maximum salary/growth possible, reliability of the field being increasingly in demand for the next few decades, and so on. Your choice should be based on factors most relevant and important to you.

In this article, we’ll cover:

Machine Learning Engineer vs. Data Scientist

Machine Learning refers to a branch of Artificial Intelligence and Computer Science involving techniques that help provide computers the ability to learn from data without being explicitly programmed.

On the other hand, Data Science is an interdisciplinary field that uses a scientific approach, processes, and systems to extract meaningful insights from any raw data. Machine Learning, Deep Learning, Data Analysis, Computer Science, and Data Visualization are all at the core of Data Science.

Data scientists focus more on building statistical and Machine Learning models. Whereas, Machine Learning engineers focus on productionizing the model.

Let us look at some more aspects of the two fields to compare them better.

Future of Machine Learning and Data Science

Data Science is currently bigger in terms of the number of jobs than Machine Learning as of 2022. As a data science professional, you work as a Data Scientist, Applied scientist, Research Scientist, Statistician, etc. As a Machine Learning professional, you work as a Machine Learning Engineer who focuses on productizing the models.

That said, Machine Learning Engineers earn a slightly higher salary and are also growing at an increasing pace in terms of their careers and salaries. Here are some more interesting and useful tidbits about the two fields to help you make an informed choice.

Future of Machine Learning

Here are some interesting statistics, figures, and rankings associated with Machine Learning:

  • By 2025, according to the Future of Jobs Report 2020, there will be 12 million new jobs in Artificial intelligence created in 26 countries. 
  • In 2019, Machine Learning showed a 344% growth in job postings. 
  • Machine Learning ranks #17 in Glassdoor’s list of best jobs in America in 2021.

While AI and Machine Learning are new and advanced fields, more and more companies are using Machine Learning techniques now. That said, the official title of the professional who uses Machine Learning techniques can often just be Software Engineer who works on Machine Learning.

Future of Data Science

Here are some more statistics, figures, and rankings, this time focused on Data Science:

  • By 2026, according to the US Bureau of Labor Statistics, there will be an increase of 28% in Data Science jobs, leading to 11.5 Million jobs in Data Science and Analytics. 
  • The number of Data Science jobs is higher than the number of Machine Learning jobs as of 2022.
  • Data Scientist also ranks #2 in Glassdoor’s list of best jobs in America in 2021.

There’s still a shortage of skilled Data Scientists, and the demand has led to around 65% of Data Science jobs requiring just a bachelor’s degree. If you just have a bachelor’s degree and do not have much training or experience in AI or Machine Learning, Data Science might be the right step forward for you.

Which Language is Most Popular for Machine Learning and Data Science?

Python is the most popular language for Machine Learning and Data Science. This is so because Python offers concise and readable code, and this simplicity makes it easier to create reliable systems. 

Over the years, both Machine Learning and Data Science have increasingly gained importance and popularity, and there are many courses available for each. Starting with Python courses can be a good idea, given the popularity and ease associated with the language.

Python Machine Learning Course vs. Python Data Science Course

You may prefer taking Machine Learning courses in Python if you love software, programming, and algorithms. On the other hand, if you love statistics, calculations, and mathematics, you may prefer to go for Data Science courses in Python. 

Other popular Machine Learning and Data Science languages include R, JavaScript/Java, Julia, Lisp, Scala, and C/C++. You can explore Machine Learning and Data Science courses in any of these languages as well.

How to Choose between Machine Learning and Data Science?

We’ve discussed some facts about the two fields above, but the best choice for you will depend on your interests and skills. That said, learning Machine Learning will be useful for your career if you’re a Data Scientist, even if you don’t switch roles. Both the fields have a bright enough future to accommodate skilled professionals and pay them well. The upper cap for a Machine Learning Engineer salary is around $200,000. Although in some cases, a salary of $335,019 has been reported. For a Data Scientist's salary, the upper cap is $200,000 as of April 2022.

Before you decide, it might help to ask yourself questions such as:

  • Am I more into data structures and algorithms, or do I love statistics and mathematics more?
  • Will I be paid more long-term if I choose Machine Learning or Data Science, given my skill-set and strengths?
  • Is the difference in salary significant enough for me to make it the basis of the decision?
  • What am I most skilled at?

This exercise will help you make the right decision for yourself and carve out a career you’re more likely to be satisfied with in the future.

Machine Learning Engineer Salary vs. Data Scientist Salary

The average annual Machine Learning Engineer salary in the US is $131,001. The salary of a Machine Learning Engineer typically ranges from $88,000 to $200,000 in the US. 

The average annual Data Scientist salary is $117,212 in the US, between $82,000 and $200,000 per annum. However, professionals can earn significantly below or above our given ranges in some cases.

These details are as per our research in April 2022. 

Machine Learning and Data Science Interview Preparation

Machine Learning interviews are getting tougher as the popularity of the field and competition to score a job in it increases. Data Science interview preparation can also involve a significant amount of skill sharpening and effort. Let us look at some important Data Science and Machine Learning topics you must prepare and some sample interview questions you should be able to answer before your next interview.

Go through the Machine Learning Engineer Resume guide to learn tips, best formats, and look at the resume sample included.

Cracking the Data Science Interview: Topics

When preparing for your Data Science interview, you should understand and get some experience with the complete Data Science life cycle. This includes the following seven stages:

  1. Business Understanding
  2. Data Mining
  3. Data Cleaning
  4. Data Exploration
  5. Feature Engineering
  6. Predictive Modeling
  7. Data Visualization

There are components, tools, and techniques associated with each of these stages. You should be thoroughly familiar with anything that could be relevant to your role. You should also try to gain more expertise and experience in the skill sets that are a part of your role requirements.

Some topics it’d help you to focus on are:

  • Statistical Models
  • Machine Learning Models
  • SQL
  • Statistics 
  • Big Data
  • Coding in Python/R
  • Business Intelligence
  • Data Structures
  • Algorithms
  • Mathematics
  • Machine Learning
  • Modeling in Data Science

Take a look at the 7 Best Data Science Books for Interview Preparation here.

How to Prepare for Data Science and Machine Learning Interview: Topics

Many ML topics are also required for Data Science interviews as well. That said, ML interviews focus more on productionizing the ML models, and Data Science interviews focus more on the creation of statistical and ML models. 

While a lot of prep is required to clear a Machine Learning interview, it might serve you well to sharpen your knowledge and skills on the following topics before your Machine Learning interview:

  • How to productionize a Machine Learning model
  • Supervised and unsupervised learning
  • Artificial Neural Networks, Deep Neural Networks, and Neural Networks in general
  • Linear and Logistic Regression
  • K Nearest Neighbors
  • Overfitting and methods to prevent it
  • Cross-validation
  • Regularisation
  • L1 and L2 Regularisation
  • Underfitting
  • Regression, performance metrics: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE)
  • Performance metrics classification problems: Confusion matrix, ROC curve
  • Gradient Boosting: Decision trees
  • Random Forest
  • Deep Learning, Ensemble Learning

Machine Learning Interview Questions

Take a look at these ten common Machine Learning interview questions and see if you can answer them to gauge your preparedness for your upcoming interviews:

  1. How would you build a system to predict whether an Uber driver will accept a ride request or not?
  2. Build a credit risk prediction system based on past user transaction history.
  3. You are given a repository of data files consisting of scanned pdfs, images, and text documents. How would you build a system to categorize and tag them?
  4. What does Overfitting mean in Machine Learning, when does it occur, and how can we avoid it?
  5. What’s the difference between unsupervised, semi-supervised, and supervised Machine Learning?
  6. What are some popular algorithms used in Machine Learning?
  7. What are the stages of building a hypothesis or model in Machine Learning?
  8. What’s the difference between Machine Learning, Deep Learning, and Data Mining?
  9. What do you understand by Transduction?
  10.  Tell me what you know about Reinforcement Learning.

Data Science Interview Questions

Here are ten popular Data Science interview questions to help you practice: 

  1. Describe selection bias and its types.
  2. What do you understand by the bias-variance tradeoff?
  3. What are some ways to deal with missing data in a data set?
  4. What do you understand by recall and precision?
  5. Tell me what you know about the confusion matrix.
  6. Why is R used in Data Visualization?
  7. What’s the difference between residual error and error?
  8. Differentiate between Standardisation and Normalisation.
  9. How would you find MSE and RMSE in a linear regression model?
  10.  Tell me what you know about NLP.
  11.  Talk a bit about the Markov chains.
  12.  What do you understand by the ROC curve?
  13.  Describe the various regularization methods.

FAQs on Machine Learning vs. Data Science

Q1: What is the relationship of AI with Machine Learning and Data Science, respectively?

Artificial Intelligence is at the root of Machine Learning and Deep Learning. On the other hand, while Data Science may use AI in its operations, it doesn’t come under the umbrella of Artificial Intelligence and does not fully represent it.

Q2: Who gets paid more: Data scientists or Machine Learning Engineers?

A Machine Learning engineer works on AI, which is a relatively new field, and gets paid slightly more currently than a Data Scientist job. That said, the number of Data Science jobs is actually higher than the number of Machine Learning engineer jobs.

Q3: Do Data Scientists work on Machine Learning?

Data Science is an interdisciplinary field, and hence, Data Scientists often work using knowledge of many fields and techniques like mathematics, statistics, data mining, cluster analysis, visualization, and Machine Learning. Not every Data Scientist uses all of these skills at once, but all Data Scientists use some combination of these to get the job done.

Q4: What is the difference between artificial intelligence and Machine Learning?

Machine Learning comes under Artificial Intelligence, which acts as an overarching field. AI refers to making machines capable of doing tasks independently in a way that we’d consider advanced and smart. Machine Learning is about providing machines data and making them capable, so we can let them derive insights from the data themselves.

Q5: Is Python enough for Data Science?

While in some cases, Python might suffice for the tasks at hand, in a majority of the cases, and especially long term, Python is one among many skills that a Data Scientist needs to have to do their job well.

Ace Your Machine Learning Interview 

If you need help with your prep, join Interview Kickstart’s Machine Learning Interview Course — the first-of-its-kind, domain-specific tech interview prep program designed and taught by FAANG+ instructors. Click here to learn more about the program. 

IK is the gold standard in tech interview prep. Our programs include a comprehensive curriculum, unmatched teaching methods, FAANG+ instructors, and career coaching to help you nail your next tech interview. 

Sign up for our FREE webinar to uplevel your career!

Posted on 
November 8, 2021

Abhinav Rawat

Product Manager @ Interview Kickstart | Ex-upGrad | BITS Pilani. Working with hiring managers from top companies like Meta, Apple, Google, Amazon etc to build structured interview process BootCamps across domains

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