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

Posted on 
November 8, 2021
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Machine Learning and Data Science have been increasingly 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. 

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 vs. Data Science
  • Future of Machine Learning and Data Science
  • Salary: Machine Learning Engineer vs. Data Science Engineer
  • Machine Learning Interview Preparation
  • Data Science Interview Preparation
  • FAQs on Machine Learning vs. Data Science: Which Has A Better Future?

Machine Learning vs. Data Science

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.

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 2021. 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 2021.
  • 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 not much training or experience in AI or Machine Learning, Data Science might be the right step forward for you.

Which Language Is the 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 going 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 both Machine Learning and Data Science Engineers salaries is $200,000 as of October 2021.

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.

Salary: Machine Learning Engineer vs. Data Science Engineer

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

For a Data Science Engineer, the average annual salary is $117,212 in the US, between $82,000 and $200,000 per annum.

These details are as per our research in October 2021. 

Machine Learning Interview Preparation

Machine Learning interviews are getting tougher as the popularity of the field and competition to score a job in it increases. Let us look at some topics you should prepare and interview questions you should practice before your Machine Learning interview.

How to Prepare for Machine Learning Interview: Must-learn Topics

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:

  • 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. What does Overfitting mean in Machine Learning, when does it occur, and how can we avoid it?
  2. What’s the difference between unsupervised, semi-supervised, and supervised Machine Learning?
  3. What are some popular algorithms used in Machine Learning?
  4. What are the stages of building a hypothesis or model in Machine Learning?
  5. What are some ways to deal with missing data in a data set?
  6. What do you understand by recall and precision?
  7. Tell me what you know about the confusion matrix.
  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 Preparation

Data Science interview preparation can involve a significant amount of skill sharpening and effort. Let us look at some important Data Science topics you must prepare and some sample interview questions you should be able to answer before your Data Science interview.

Cracking the Data Science Interview: Must-learn 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 on the skill sets that are a part of your role requirements.

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

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

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. Why is R used in Data Visualization?
  4. What’s the difference between residual error and error?
  5. Differentiate between Standardization and Normalization.
  6. How would you find MSE and RMSE in a linear regression model?
  7. Tell me what you know about NLP.
  8. Talk a bit about the Markov chains.
  9. What do you understand by the ROC curve?
  10. Describe the various regularization methods.

FAQs on Machine Learning vs. Data Science

Question 1: 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.

Question 2: Who gets paid more: Data Scientist 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.

Question 3: 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.

Question 4: 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.

Question 5: 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.

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