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Data Science vs Machine Learning- Career That is Right for You


Data Science vs ML

Data Science and Machine learning are buzzwords in the talent market ever since digitization is at its peak and data spreads like wildfire. You will find endless job opportunities around these two fields, rising prominently daily.

People often mistake the two as interchangeable, but that is not right. Data Science is a vast field incorporating Machine Learning as a subset. Both of them are quite dependable on each other. So, what exactly is data science vs machine learning?

Data Science is required to extract data, clean & drive actionable insights from them. Machine Learning is basically about self-innovating the systems or applications so that they can help in reading & interpreting the data for final understanding by the company, stakeholders & other beneficiaries.

When making a career, many job onlookers often need clarification on the difference between data science and machine learning. Since both the profiles share similarities to an extent, it can get overwhelming to compare & contrast the two and get the best one as a choice.

If you are past the doubt of “Are data Science and Machine Learning the same?” and are looking for differences between data science vs machine learning - in terms of pursuing a tech career, continue reading as you will unfold answers to:

  • What is data science and machine learning?
  • What are Data Science and Machine learning, their benefits & limitations?
  • Data Science vs Machine Learning engineer roles & responsibilities.
  • Difference between Data Science and Machine Learning based on different factors.
  • Which is Better for a Career Option - a Data Science Course or a Machine Learning Certification?
  • What is the Recommended Learning Routine for a Data Science and Machine Learning Career?
  • Is machine learning or data science the Same?

What is Data Science - You May Ask?

data scientist skill

To understand the difference between data science vs machine learning, you should understand the basics of data science. From a technical viewpoint, Data Science is a discipline where certain scientific systems, algorithms, processes, methods, tools, and intuitiveness are used to draw insights from any raw, structured, or unstructured data. And that extracted knowledge & insight from data is further applied across various businesses to build efficient business processes, operations, solutions, and power every intrinsic business decision.

In simple words, it is the study of data. The purpose of Data Science is to store, process, measure, analyze, and visualize the scattered data into meaningful information that can be acted upon. 

Supposedly, a company that has petabytes of sales data, user data, or any market data to be translated into simple, understandable visual or written information, then Data Science & its methods come in handy to transform that data into complete usable information for business or industry.

What are the Benefits of Data Science?

data science help

For any business, Data Science helps highly to:

  • Make any data better for processing
  • Make products smarter & better
  • Help make fact-based, fast decisions
  • Helps in flowing better business information, which helps to hit better business opportunities like identifying new products in the market for increased expansion or introducing personalized customer experiences

Data Science & Its Limitations

Every process, the technique has some shortcomings, so does Data Science have:

  • If data is missing a value or is too small, unorganized, incomplete, the whole outcome can be misleading.
  • Not all data collected could be similar in quality or format, which might either lead to wastage of complete data or manpower to bring those data to a certain level.
  • Data could be arbitrary, chosen on a random whim, which might not produce effective analysis as required.

What’s the solution to beat this shortcoming? To get the perfect analysis, it is always preferred to collect effective & measurable data in a similar format from credible sources only!

What is Machine Learning - You Might Wanna Know?

To know about machine learning vs data science, learn what machine learning is in detail. Machine learning is a part of AI, where computer algorithms are improved with experience & use of data. It’'s like the system automatically learns to improve the user experience, without having to be programmed especially each time. Machine Learning algorithms help build computer programs that can further access the data & use it to improve themselves.

How is Machine Learning Beneficial?

ML help

Machine Learning helps to revolutionize the way things work, it helps data-driven enterprises:

  • Helps in reviewing a large volume of data & discovering trends & patterns. 
  • Since ML is all about giving machines the ability to self-learn & react, it automates the process of making predictions & improving algorithms per se.
  • They improve systems or applications on their own, and each time brings amazing accuracy & efficiency which makes predictions better & faster.
  • They can easily operate multi-variety & dimensional data in any certain or uncertain environment.
  • Good for applications within any vertical be it healthcare, banking, or more, it helps businesses to give more targeted customer reach & personalized user experiences.

What are Machine Learning’s Limitations?

Before you drive into machine learning vs data science, learn about the limitations of machine learning. Machine learning, though, is beneficial in eliminating the intervention of data engineers or ML engineers in further procedures. However, such professionals would still be needed to make data models, systems, and algorithms that are enabled to solve new problems if they arise. 

Other than that:

  • Labeling training data is a laborious task
  • Batch training can be time taking
  • Training data needs to be tagged
  • Learning generally needs to be supervised
  • It can be hard to debug the systems.

How are these Machine Learning challenges curbed? However, these limitations or complexities are nothing if very skilled Machine learning trainers are deployed and the data transformation isn’t very complex.  

Data Science vs Machine Learning - Let’s Differentiate these Two

Let’s help you differentiate between Data Science and Machine Learning based on several factors like concepts, skills, job titles, and a lot more. So that you could gain an understanding about which one is better in comparison to others, for making a career or just building your learning curve around.

Difference Between Data Science vs Machine Learning Based on Concepts


Data Science

Machine Learning


Data Science helps to extract insights from data to improve decision-making & processes.

Machine learning helps in advancing the systems by letting it predict & analyze the outcome of new datasets, based on past or old datasets.

Requires Understanding of 

  • Different business domains & verticals
  • SQL, NoSQL systems
  • Data Visualization
  • ETL & Data profiling
  • Standard Reporting
  • Data Wrangling
  • Python & R Programming
  • Mathematical & statistical knowledge
  • SQL Model Visualization

Data Type

Input data is ready for human read & transformation

Input data requires transformation based on the algorithm type

System Type

Scalable systems needed to handle loads of data

GPUs are recommended to support intensive vector operations


Requires handling unstructured, raw complex data, which leads to misleading interpretations.

Mathematical concepts & algorithms can be complex to handle.

Components or types

There are 4 components of data science:

  • Data Strategy
  • Data Engineering
  • Data Modeling & Analysis
  • Data Visualization and Operationalization

Machine Learning has 3 types:

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning

Real Life Case Studies To Understand Data Science Vs Machine Learning!

To understand data science vs machine learning better let us check out some real life case studies of data science and machine learning and how they have been pioneer in success of many organizations. Data science has become integral to modern businesses and organizations, driving decision-making, optimizing operations, and improving customer experiences. 

Case Study 1: Predictive Maintenance in Manufacturing

  • GE:General Electric (GE), a global industrial conglomerate, leverages data science to implement predictive maintenance solutions. By analyzing sensor data from their industrial equipment, such as jet engines and wind turbines, GE can predict the need for maintenance before a breakdown occurs. This proactive approach minimized downtime and reduced maintenance costs.

Case Study 2: Healthcare Diagnostics and Treatment Personalization

  • IBM Watson Health: IBM Watson Health employs data science to enhance healthcare by providing personalized diagnostic and treatment recommendations. Watson's natural language processing capabilities enable it to sift through vast medical literature and patient records to assist doctors in making more informed decisions.

Case Study 3: Fraud Detection and Prevention in Finance

  • PayPal:PayPal, a leader in online payments, employs advanced data science techniques to detect and prevent fraudulent transactions in real-time. They analyze transaction data, user behavior, and other relevant factors to identify suspicious activity.

Case Study 4: Urban planning and smart cities

  • Singapore: Singapore is pioneering the smart city concept, using data science to optimize urban planning and public services. They gather data from various sources, including sensors and citizen feedback, to manage traffic flow, reduce energy consumption, and improve the overall quality of life in the city-state.

Case Study 5: E-commerce Personalization and Recommendation Systems

  • Amazon: Amazon, the e-commerce giant, heavily relies on data science to personalize the shopping experience for its customers. They use algorithms to analyze customers' browsing and purchasing history, making product recommendations tailored to individual preferences. This approach has contributed significantly to Amazon's success and customer satisfaction by reducing customer service response times by 40%.

Case Study 6: Transportation and Route Optimization

  • Uber: Uber revolutionized the transportation industry by using data science to optimize ride-sharing and delivery routes. Their algorithms consider real-time traffic conditions, driver availability, and passenger demand to provide efficient, cost-effective transportation services.

Case Study 7: Environmental Conservation and Data Analysis

  • NASA: NASA collects and analyzes vast amounts of data to better understand Earth's environment and climate. Their satellite observations, climate models, and data science tools contribute to crucial insights about climate change, weather forecasting, and natural disaster monitoring. Machine learning, a subset of artificial intelligence, has received significant attention in recent years. It has revolutionized various industries by enabling computers to learn and make intelligent decisions without explicit programming.

To gain more information on machine learning vs data science, explore real-life use cases and success stories that demonstrate the practical applications and transformative impact of machine learning.

  1. Netflix is a great example of a company using machine learning to improve the customer experience. The streaming giant has long been using machine learning algorithms to personalize its recommendations for viewers. By using data like a customer's viewing history, the viewing history of customers with similar entertainment interests, information about individual shows and movies, and even data about when a customer is likely to be most active on the platform, Netflix is able to recommend relevant content to individual viewers. This recommendation system is estimated to drive “80 percent of hours of content streamed on the platform.”
  2. YouTube :YouTube is another company that has been using machine learning for business for a long time. The site uses a technique called deep learning to recommend videos to viewers, which is based on modeling massive amounts of historical data.
  3. MIT: The MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) created a program called ICU Intervene that uses machine learning to predict possible treatments. It learns from massive amounts of intensive-care-unit (ICU) data, including symptom data, to make real-time predictions. It also explains the reasons behind the decisions it makes, which helps to improve patient care. This could ultimately make a huge difference in the quality of care that patients receive.
  4. In April 2017, Wells Fargo launched a chatbot for Facebook Messenger that helps customers with their banking needs. Wells Fargo’s bot for Messenger focuses on incorporating financial services into third-party environments to meet customers where they are and into the moments they want to use them. Their goal is to deliver information ‘in the moment’ to help customers make better informed financial decisions using AI technology instead of requiring customers to navigate through several pages on our website, and turn it into a simple conversation in a chat environment. 
  5. Facebook: Facebook is using machine learning for a variety of tasks, including facial recognition, content moderation, and ad targeting. In addition, they're using it to build chatbots and to improve the performance of their search engine.
  6. Airbnb: Airbnb is another company that relies heavily on machine learning. They use it to personalize your search results, to match you with potential hosts, and to help you find the perfect listing.
  7. Tinder: Modern dating wouldn't be the same without Tinder. This app has changed the game by using machine learning to create a smooth user experience and perfect matches. Tinder's algorithm looks at a variety of factors, such as your age, location, and interests, to find matches for you. This might have helped you understand data science and machine learning difference in a better way. If you seek a vibrant future in machine learning or data science, check out career the respective career path.

Difference Between Data Science Vs Machine Learning Engineer Roles & Responsibilities

Let’s look into the roles & duties of each technology, one by one.

Data Scientist Roles & Responsibilities

The main obligation of any Data Scientist is to:

  • Pull out an information investigation, a process of accumulating information or datasets for further analysis & transformation.
  • To collect, oversee, clean & prepare the 2 types of information for further processing. The two types of information or data sets are --- Organized information, where it is easy to sort & process, for instance, data like sales figures, ledgers, bills, etc. The other is unstructured information, such information isn’t often organized, varies, has no sync, does not have room for innovation, and requires quite a time to analyze, like the different data from customer surveys, metrics of social media performance, lead inquiry, etc.
  • To act as a lead information strategist for utilizing new datasets & build groups to bring forth the improvement of information items.
  • To perform logical trials efficiently to handle different issues & have a positive effect on different areas and ventures of business.
  • To bring forth important information sources and place them for digging customer business needs, and from them gather structured and unstructured datasets & factors.
  • To create and use calculations & intelligent models to mine massive information stores, perform information & blunder examination to improve models, further clean & approve information for exactness & consistency.
  • To look into the information & analyze it for patterns or examples & decode that information from the point of view of the targets.
  • To implement models & algorithms by teaming up with programming designers & Machine Learning engineers.
  • To communicate scientific answers for business partners, stakeholders, organizations in totality and achieve enhancements as expected to operational frameworks.

Machine Learning Engineer Roles & Responsibilities

Machine Learning Engineer & Data Scientist roles & responsibilities are quite similar & both require impeccable data management & transformation skills. The role of Data Science, we just saw is to generate insights from data to drive decisions, processes & other tasks. While the role of Machine Learning Engineers is to analyze data & create self-running software that could learn & innovate on its own for predictive automation models.

Machine Learning engineers & Data Scientists work alongside each other. Machine Learning engineers ensure that the models used by Data Scientists are accurate & enough to analyze & process any type, kind, or volume of datasets. With each section the idea of data science vs machine learning will become clear.

Here are the common duties of Machine Learning Engineers with any firm:

  • To analyze and convert data science prototypes.
  • To develop smart, real-time Machine Learning systems & schemes.
  • To conduct statistical analysis & enhance the efficacy of models using test results.
  • To discover effective & reasonable datasets online for batch training purposes.
  • To train & re-train ML systems or models whenever required or applicable.
  • To expand & improve the ML frameworks and libraries.
  • To design & develop Machine Learning apps according to business customer/client specifications & user experiences.
  • To conduct colossal research, experiment, and implement effective ML algorithms and tools.
  • To measure the problem-solving capacity, capabilities & use-cases of ML algorithms & put them in rank based on their success probability.
  • To explore, transform, and visualize data for better understanding and identify major differences in data distribution that could affect model performance while deploying against real-time business scenarios.

Difference Between Data Science vs Machine Learning Skills/Prerequisites

If you ask machine learning vs data science which is easy, both Data Science and Machine Learning careers are attractive in terms of job opportunities & career expansion. Whether you choose machine learning or data science as a career option, the skills, prerequisites, qualifications each profile demands are very achievebale & possible for anyone.

To be a Data Scientist, you can pursue a diploma or degree in disciplines like computer science, mathematics, statistics, engineering, or any technology-related subject. However, it won’t be an issue, even if you are from a non-technical background, with little upskilling courses online, you can still make a profound career in Data Science. Further, going for a master's degree or Ph.D. degree in technical disciplines is purely subjective to your preference.

Along with degrees, you would be required to address the following technical skills in your resume:

  • Proficiency in any programming language Python, R, SQL, or any other that’s in demand or is your preference.
  • Understanding of Data mining methods like network analysis, linear models or regressions, boosting, and statistical analysis skills. 
  • Ability to work around machine learning techniques such as AI neural networks, clustering, statistical modeling, datasets manipulation.
  • Understanding of visualization & presentation of data.
  • Knowledge of data & computing tools such as MYSQL, Spark, Hadoop, or more.

To be a Machine Learning Engineer, you can go for graduation or a master’s degree in any technical domain. Since this field is quite lacking in terms of demand & supply, you can easily make it into this career. However, it won’t be an issue, even if you are from a non-technical background, with little upskilling courses online, you can still make a profound career in Machine Learning!

Provided, you need to have the following skills or specialization at hand:

  • Understanding of machine learning algorithms, APIs, packages, libraries.
  • Knowledge of deep neural networks, visual processing, supervised learning, unsupervised learning & reinforcement learning.
  • Proficiency in programming languages like R, Java, Python, Javascript, C++, Scale, etc.
  • Understanding disciplines like mathematics, statistics, the probability to help systems understand machine learning algorithms.
  • Practical knowledge of the use of machine learning-related tools like zookeeper, ETCD, MATLAB, etc.
  • Strong data management, analytical skills, and knowledge of machine learning evaluation metrics.

Pursuing a master's in data science and machine learning-related subjects is purely subjective & personal preference. After finishing up the basic degree and enrolling in the formal training program, you can choose any one of these as a long-haul career option.

Difference Between Data Science Vs Machine Learning Job Titles

By completing the Data Science and Machine Learning course online, you can stand eligible for the following job titles:

Job Roles Under Data Science

Job Titles Under Machine Learning

  • Data Scientists
  • Data Analyst
  • Data Engineer
  • Data Architect
  • Data Storyteller
  • NLP data scientists
  • Machine Learning Engineer
  • Machine Learning Scientist
  • Machine Learning Engineers
  • Machine Learning Researchers
  • Data mining and analysis specialist
  • Data Scientist
  • Data Analyst
  • BI - Business Intelligence Developer
  • Software Engineer/developer
  • Designer in Human-centered machine learning
  • Computational 

Difference Data Science Vs Machine Learning Salary

The Data Scientist’s salary on average can be between $90,000 to $156,331 per year. However, this range can vary based on programming language, experience, locations, employer, industry, skills, certifications, interview performance & more such factors.

The average machine learning engineer salary can range between $110,000 to $163,000 per year. This range can vary in reality based on skills, certifications, location, seniority level, employer, industry, interview & resume, and more such factors.

An entry-level machine learning engineer can earn $96,090 on average, per year if they have skilled-up with professional online boot camps. However, an entry-level data scientist can earn around an average of $95,000 per year, fulfilling the demands for employers with their skill-up and professional candidature. And with the right certifications and skill and experience in the filed, the data scientist can earn upto $210k per annum and a senior machine learning engineer can earn $180- $200k per annum. Now you are also aware of the salary difference between data science and machine learning

Data Science Vs Machine Learning Certifications

Getting certified in both technologies can add a lot to your candidature. Securing a data scientist certification or machine learning certification will state that you have validated knowledge & you are full-proof in working on real-time challenges of business. 

However, both disciplines don’t have a particular body that conducts the certification exam and you will find a lot of variety of certification authorities listed on the internet. To help you realize & clear the right ones, we are jotting down your options of machine learning and data scientist course.

14 Type of Data Science & Machine Learning Certifications You can Go for After Formal Training

  • Microsoft Certified: Azure AI Fundamentals
  • Microsoft Certified: Azure Data Scientist Associate
  • Dell EMC Data Science Track (EMCDS)
  • Data Science Council of America (DASCA) Principle Data Scientist (PDS)
  • Data Science Council of America (DASCA) Senior Data Scientist (SDS)
  • Certified Analytics Professional (CAP)
  • Google Professional Data Engineer Certification
  • IBM Data Science Professional Certificate
  • Open Certified Data Scientist (Open CDS)
  • Tensorflow Developer Certificate
  • Amazon AWS Big Data Certification
  • SAS Certified Big Data Professional
  • SAS Certified AI & Machine Learning Professional
  • SAS Certified Data Scientist

To get complete detail on these certifications, you can visit here!

Data Science vs Machine Learning Engineer - Learning Curve!

Is Machine Learning Easy to Learn?

To excel in machine learning, you don’t need hard & fast mathematical skills, on applying a bit of creativity, tenacity, and experimentation, you can skill up in Machine Learning with ease (from any career or educational background).

Machine learning is very practical and no rocket science to learn. It just depends on what kind of machine learning training you opt for. Choose the one taught with real use cases, ML tools & projects.

Machine Learning Training & Certification

  • No cost for a Demo Class
  • Industry Expert as your Trainer
  • Available as per your schedule
  • Customer Support Available

Is Data Science Easy to Learn?

Yes, similar to machine learning, data science is another very possible & easy technology to explore & have expertise at. However, this one requires a lot of determination, time & patience to learn but don’t worry, you will get there. A quick formal training, after completing the basic education would be helpful for you to skill up in this technology.

You can choose machine learning or data science according to your liking and understanding. Both the fields have equal opportunities to offer. 

Data Science vs Machine Learning - Which one Has Better Job Trends?

Data Science Job Trends

  • Employment of data scientists is projected to grow 35 percent from 2022 to 2032, much faster than the average for all occupations. About 17,700 openings for data scientists are projected each year, on average, over the decade - predicts the U.S Bureau of Labor Statistics.
  • Data Scientists rank #6 in Best Technology Jobs.
  • Data Science jobs stay up for 5 days extra than other jobs because of 2 reasons  - first, organizations are hungry for this title & are willing to give opportunities at any cost, second, there is no competition in this profile, so employers take plenty of time to get the right resources at lucrative paychecks.
  • Data concentric industries are so hungry to close down these jobs that they have 79% positions available that require basic bachelor’s degree & training online, while there are only 38% profiles with top-tier companies that especially ask for candidatures with Master or Ph.D. in the technological field.
  • Since 2012 the number of data scientists starting their first job has increased at a rate that is consistently 50% higher than that for software engineers and data analysts.
  • 55% of all the data scientists on LinkedIn are located in the United States.

Machine Learning Job Trends

  • Globally, machine learning jobs are predicted to grow to worth $31 billion by the year 2027 (a 40% growth rate for 6 cumulative years).
  • As per Gartner, there will be 2.3 million AI-related Machine Learning jobs in near future.
  • AI titles with subfields of Machine Learning Engineers, Big Data Engineers are LinkedIn’s fastest-growing job titles. As per the Bureau of Labour Statistics, U.S, there will be 11.5M jobs in the ML & its ecosystem by the year 2026.
  • As per LinkedIn research, at present, there are 9.8 times more Machine Learning Engineers compared to 5 years ago. And the job posting for Machine Learning engineers has outgrown by 344% between the last 5 years.

As a subset of AI, both careers are growing steadily & heavily, and have an amazing talent gap in the current job market. On exploring the popular job portals, you will find a lot of Data Science or Machine learning jobs and explore difference between machine learning and data science .

Data Science vs Machine Learning - What Company or Industry Needs Them?

Here are the Top companies & industries that use Machine learning in cool ways:

Companies Using Machine Learning

Top Industries Using Machine Learning

  • Google for image classification, NLP, prediction systems, search ranking, speech recognition, etc.
  • Edgecase for enhancing their traditional shopping experience online & improving conversions.
  • Twitter for managing curated timelines to score the quality of tweets based on certain metrics.
  • Facebook to build customer-centric chatbots, filter spammy or poor quality content, make image reading easy for visually impaired people.
  • Yelp to curate & manage images in great bulk.
  • Pinterest to help with content discovery, eliminating spam, advertising monetization, and reducing email newsletter churn.
  • Hubspot to innovate its internal content management system for management & customer acquisition.
  • Salesforce. inc to analyze every aspect of customer relationship
  • IBM for their healthcare vertical to make an accurate recommendation for healthcare treatment of patients
  • Software & Hardware
  • Education
  • Healthcare
  • Retail & eCommerce
  • Marketing
  • Finance & Banking

Here are Top Companies & Industries that Leverage Data Science in cool ways:

Top Big Data Companies Using Data Science

Top Industries Where Data Scientists are Needed

  • Google for network infrastructure optimization, enhancing advertisement value and driving other data processing-related tasks.
  • HP for improving their products, workflows, and service performance.
  • SAP for performing deeper analysis around its AI & ML.
  • Amazon for understanding their user needs through data, streamlining the process, and much more.
  • Microsoft to extract & understand users' feedback for more improvement & workforce productivity.
  • VMware for predictive analytics, identifying business trends, users' needs, supporting virtualization & much more.
  • Salesforce for extracting & analyzing across industries & to understand their needs in terms of leveraging integrated cloud solutions for customer satisfaction.
  • Crunchbase
  • Netflix for understanding customer behavior, subscribers preferences, improving searches and more.

Software & Hardware

  • Healthcare
  • BFSI
  • Telecommunication
  • Media & Entertainment
  • Automotive

Which is Better for Career Option - Data Science or Machine Learning?

Both Data Science and Machine Learning are growing tech profiles. The job demand, pay scale are very high in both fields. However, if you are confused between Data Science or Machine Learning, see which one’s skills interest you the most, or which one has a job opportunity at your dream company.

Whichever you choose between the two, you are anyway going to be benefitted. If still confused, try a free demo class of each field with us. We will help you underline the best option.

How Do You Know if Data Science Is the Career for You?

If you’re considering a career in data science, it’s essential to determine whether it’s the right fit for you. Here are some top tips to help you determine if data science is the career path you should pursue.

  1. Learn What the Role Entails
  2. Does Coding Make You Happy?
  3. Think About Your Current Soft Skills
  4. Talk to Junior and Experienced Data Scientists
  5. Dip Your Toes Into Data Science With an Online Course
  6. Consider Whether You Like Experimentation and Testing
  7. Are You a Numbers Person?
  8. Consider Your Ideal Career Trajectory

Before you begin applying for jobs, make sure you have a solid grasp on the following, which pop up frequently as requirements for machine learning engineer roles:

  • Researching, designing and implementing ML models and systems
  • Implementing machine learning algorithms and tools
  • Scaling data science prototypes
  • Selecting appropriate data sets, verifying data quality, cleaning and organizing data (in collaboration with data engineers)
  • Performing statistical analysis
  • Executing tests and optimizing machine learning models and algorithms
  • Monitoring systems in production and retraining them to improve performance
  • Utilizing machine learning libraries

What is Recommended Learning Routine for Data Science and Machine Learning Career?

To go about either Data Science or Machine Learning.

  • Evaluate & research the field in terms of prerequisites, skills, duties, future scope, compensation, employers & more.
  • Get formal training or invest in some Data Science and Machine Learning courses online.
  • Talk to experts already working in the industry, expand your network, and take career counseling.
  • Secure a credential, certification as discussed above.
  • Start applying for summer internships, short-term projects, jobs to gain real-field experience.
  • You can learn both of them one by one as both the fields are intertwined.
  • Other than taking training, do invest in watching data scince tutorial videos, reading whitepapers, practicing sample questions yourself, and indulging in other relevant learning aspects on your own.

Is Data Science and Machine Learning Same?

Data Science and Machine Learning may seem similar as somewhat their nature & components involve similar action, but in reality, they can’t be interchanged. Both processes are needed in conjunction with each other, like without each other, the other’s task is incomplete. 

Data Science is a broad field that involves Machine Learning as a subset. Data Science is a complete process of extracting, analyzing & drawing insights from the data with the help of algorithms & statistics made possible by Machine Learning.

Data Science Training - Using R and Python

  • No cost for a Demo Class
  • Industry Expert as your Trainer
  • Available as per your schedule
  • Customer Support Available


Data Science and Machine Learning are two broad tech branches that offer vast career opportunities. Though both may seem very similar at glance, while going in-depth, it seems both are not, but rather are intertwined and have great dependency on each other.

Data Science is all about gathering data and transforming it into powerful insight through data models & frameworks that are prepared under Machine Learning. Data science vs machine learning, both the branches have different career opportunities in data-driven organizations, which are also led by automation.

Choosing one between the Data Science vs Machine Learning engineer title is an intense decision. Though we looked within the most important factors to compare & contrast between these two as in-demand disciplines, choosing one still seems a difficult deal, as both opinions are important, high in demand, and are perfect for carving careers around.

Just choose either between Data Science vs Machine Learning that matches your interests. In case you can’t decide which one to upskill in, how about taking a free demo class of data Science and Machine Learning would look to you, with us?


Q1. What is data science and machine learning?

Ans- 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. Learn more about data science vs machine learning through various sources. Secure your career with machine learning or data scientist course online. 

Q2. What is the difference between data science and machine learning?

Ans- At its core, data science is a field of study that aims to use a scientific approach to extract meaning and insights from data. Dr. Thomas Miller of Northwestern University describes data science as “a combination of information technology, modeling, and business management”. Universities have acknowledged the importance of the data science field and have created online data science graduate programs.

Machine learning, on the other hand, refers to a group of techniques used by data scientists that allow computers to learn from data. These techniques produce results that perform well without programming explicit rules.

Q3. Among machine learning vs data science which is easy?

Ans- The consensus is that data science is in fact easier than machine learning. Data science involves more statistics, while machine learning involves more computer science in addition to statistics.

Q4. What is the similarity between data science and machine learning?

Ans- Both machine learning and data science require clean data. They use maths, statistics, and algorithms to extract value from it. Both require companies to have clear business goals specified beforehand and can result in process optimization, revenue increase, or cost reduction. Opt for machine learning or data scientist course online. 

Q5. Market outlook of data science vs machine learning.

Ans- 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. If you decide to learn programming and statistical skills, your knowledge will be useful in both careers. You can choose machine learning or data science, both have lucrative career opportunities. Earn a data scientist certification or machine learning engineer certification via data scientist course online or machine learning training respectively.


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    Rana Pratap

    Earlier I was confused whether I should prefer data science or machine learning as a career because both these fields are equally demanding. But after going through it, I am able to choose one.

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    Calvin Harris

    One of the best blogs I have seen on data science and machine learning. Good comparison plus informative.

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      Thank you so much for your comment, we appreciate your time. Keep coming back for more such informative insights. Cheers :)

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    Data science is more about dealing with data to convert it into an informative one and machine learning is part of artificial intelligence. They are entirely different.

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      Hi, Thank you for reaching out to us with your query. Drop us your email id here and we will get back to you shortly!

  • F


    As you mentioned in the post that anyone from a non-technical background can pursue these both as a career. Is it practically possible, I mean would tech companies be ready to hire a candidate from the non-technical background? I don’t think so.

    • logo16


      Hello, JanBask Training offers online training to nurture your skills and make you ready for an amazing career run. Please write to us in detail at Thanks!

  • I


    I want to learn machine learning, searching for its certification exams. Can you help me with that?

    • logo16


      Glad you found this useful! For more such insights on your favourite topics, do check out JanBask Training Blogs and keep learning with us!

  • J


    Thanks! Your article helped me have a clear vision of Data science Vs Machine learning.

    • logo16


      Hello, JanBask Training offers online training to nurture your skills and make you ready for an amazing career run. Please write to us in detail at Thanks!

  • S


    I have completed B.Tech this year in the pandemic. And I want to pursue my career path in Data Science. Can anyone guide me in how to fill the form and where to fill it?

    • logo16


      Glad you found this useful! For more such insights on your favourite topics, do check out JanBask Training Blogs and keep learning with us!

  • R


    The blog was really helpful, I read it thoroughly and now I am very clear in my mind which course to choose as my career further?

    • logo16


      Thank you so much for your comment, we appreciate your time. Keep coming back for more such informative insights. Cheers :)

  • A


    Can anyone provide me with the link of Data science certification. I want to apply for that?

    • logo16


      Hi, Thank you for reaching out to us with your query. Drop us your email id here and we will get back to you shortly!

    • logo16


      Hello, JanBask Training offers online training to nurture your skills and make you ready for an amazing career run. Please write to us in detail at Thanks!

  • C


    Can anyone tell me how I can join the course at your institute?

    • logo16


      Glad you found this useful! For more such insights on your favourite topics, do check out JanBask Training Blogs and keep learning with us!

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