Data Science and Machine learning, both are buzzwords in the talent market, ever since digitization is on peak and data is spreading like a wildfire. You will find endless job opportunities around these two fields, rising prominently each day.
People often mistake the two as interchangeable, but that is not right. Data Science is a very vast field that incorporates Machine Learning as a subset. Both of them are quite dependable on each other.
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.
Many job onlookers are often confused between Data Science vs Machine Learning in respect to making a career. 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 “is Data Science and Machine Learning same”, and are actually looking for differences between the Data Science vs Machine Learning - in terms of pursuing a tech career, continue reading, as you will unfold answers to:
- 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 Career Option - Data Science or Machine Learning?
- What is Recommended Learning Routine for Data Science and Machine Learning Career?
- Is Data Science and Machine Learning Same?
What is Data Science - You May Ask?
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?
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?
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?
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?
Machine learning though is very useful at eliminating the intervention of data engineers or ML engineers in further procedures, but still, such professionals would be needed around to make data models, systems, algorithms enabled for solving new problems, if arises.
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 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
Input data is ready for human read & transformation
Input data requires transformation based on the algorithm 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
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.
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
Both Data Science and Machine Learning careers are attractive in terms of job opportunities & career expansion. Whichever you choose as a career option, the skills, prerequisites, qualifications each profile demands are very simple & 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.
- 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
Difference Data Science Vs Machine Learning Salary
The Data Scientist’s salary on average can be between $90,000 to $123,345 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 $84,000 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 $97,090 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.
Data Science Vs Machine Learning Certifications
Getting certified in both technologies can add a lot to your candidature. Securing 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.
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.
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.
Data Science vs Machine Learning - Which one Has Better Job Trends?
Data Science Job Trends
- Almost 11.5 Million Data Science jobs are expected to be created by 2026 - predicts the U.S Bureau of Labor Statistics.
- As per Glassdoor, the Data Scientist is a shiny job title that made it to the “50 best jobs in America in 2020”.
- 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 61% positions available that require basic bachelor’s degree & training online, while there are only 31% profiles with top-tier companies that especially ask for candidatures with Master or Ph.D. in the technological field.
- Linkedin even has observed that data Science jobs have increased by 650% ever since 2012. In 2016, Linkedin had only 1700 job postings in the US, and then in 2020, the postings rose to 6500.
Machine Learning Job Trends
- Globally, machine learning jobs are predicted to grow to worth $31 billion by the year 2024 (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.
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
- Retail & eCommerce
- 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.
- Netflix for understanding customer behavior, subscribers preferences, improving searches and more.
Software & Hardware
- Media & Entertainment
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 the two, 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.
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 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 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. 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?
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