Career in Machine Learning Vs Data Science? What’s the Difference?

Reader’s Digest- As we step into 2024, understanding the differences between Data Science and Machine Learning has become not just theoretical but a crucial part of the toolkit for businesses, professionals, and students in the tech world, highlighting their integral role in the industry.

Career in Data science, machine learning (ML), and artificial intelligence (AI) are highly in-demand fields in today's tech industry. They've spurred significant innovation across various sectors, notably in emerging technologies like self-driving cars, where AI and machine learning analyse sensor data for safe operation.

Data science and machine learning are technology fields that use data to enhance the creation and innovation of products, services, and infrastructure. Both offer in-demand, high-paying career paths.

The relationship between data science and machine learning is like that of rectangles to squares: data science is the broader category, while machine learning is a distinct subset. Both are integral to the work of data scientists and are being rapidly adopted across almost every industry.

This article explores the differences and similarities between data science and machine learning and the skills and career opportunities in each field.

Things you’ll find in this article: 

• What is Data Science?
• What is Machine Learning?
• Machine learning vs Data Science: What’s the difference 

Have a look at our Discover & Evolve Career Counseling Programs

What is Data Science?

Data science focuses on studying data and extracting meaningful insights from it. It employs various methods, algorithms, systems, and tools to analyse structured and unstructured data. This expertise is applied across business, government, and other sectors to enhance profits, innovate products and services, and improve infrastructure and public systems.

Skill Required

Developing programming and data analytics proficiency is crucial for pursuing a career in data science, including roles like data scientist.

• Competence in data processing and analysis for business requirements
• Familiarity with mathematical concepts, statistics, and probability
• Skills in data visualisation and data manipulation
• Proficiency in programming languages like Python, R, SAS, etc.
• Experience handling large volumes of structured and unstructured data
• Understanding of various machine learning algorithms and models
• Effective communication and teamwork abilities

Career Option in Data Science

Apart from the typical data scientist role, there are numerous other career options within the field of data science.

• Data scientist: Utilizes data to comprehend and elucidate phenomena to aid organisations in making informed decisions.
• Data analyst: Collects, cleans, and examines datasets to address business challenges.
• Data Engineer: Constructs systems for gathering, managing, and converting raw data into insights for business analysts and data scientists.
• Data architect: Evaluates and analyses an organisation's data infrastructure to strategise databases and execute solutions for data storage and management.
• Business intelligence analyst: Gathers, cleans, and interprets sales and customer data, disseminating insights to business teams.

Ask questions from our Expert Career Coach

Universities that offer Data Science

Institutions offer various programs, such as Bachelor's, Master's, and Ph.D. degrees in data science or related fields. It's recommended that you check the specific courses and admission requirements on their official websites.

• Indian Institutes of Technology (IITs)
• Indian Institute of Science (IISc)
• Indian Institute of Information Technology and Management (IIITM)
• National Institute of Technology (NITs)
• Indian Institute of Management (IIMs)
• Xavier Institute of Management (XLRI)

Primary recruiters of Data Scientist 

The main recruiters of data scientists include:

• Technology companies like Google, Facebook, Amazon, Microsoft, and Apple
• Consulting firms and analytics companies like McKinsey, Bain & Company, and Accenture
• E-commerce platforms like Amazon, eBay, and Alibaba
• Government agencies and research institutions
• Financial institutions such as banks, hedge funds, and insurance companies

Check out Highest Paying Design Careers in India: Degree, Salary, Job Roles, Growth

What is Machine Learning? (H2)

Machine learning, a branch of artificial intelligence, employs algorithms to extract data and forecast future trends. Engineers program software with models enabling statistical analysis to identify patterns within the data.

For instance, social media platforms such as Facebook, Twitter, Instagram, and YouTube collect user data. They anticipate interests and preferences using past behaviour, suggesting relevant products, services, or articles based on previous searches.

Machine learning, comprising tools and principles, finds application within data science and extends into various other domains. Data scientists frequently integrate machine learning into their tasks as needed, facilitating quicker data gathering and aiding in trend analysis.

Skill Required

To excel as a machine learning engineer, you must possess expertise in the following:

• Capability to perform data modeling and analysis
• Programming proficiency in languages like Python R, among others
• A comprehensive grasp of statistics and probability
• Familiarity with software engineering principles and system design
• Proficiency in computer science, encompassing data structures, algorithms, and system architecture

Career Option in Machine Learning

If you opt for a career in machine learning and artificial intelligence, you have various pathways available.

• Machine Learning Engineer: Conducts research and constructs AI systems for machine learning, focusing on enhancing and managing AI systems.
• AI Engineer: Develops and implements AI infrastructure for development and production purposes.
• Cloud Engineer: Constructs and manages cloud infrastructure.
• Computational Linguist: Designs computer systems to understand human language patterns.

Universities that offer Machine Learning

Institutions that offer various programs such as Bachelor's, Master's, and Ph.D. degrees in machine learning or related fields.

• Indian Institute of Technology Kanpur (IIT Kanpur)
• Indian Institute of Technology Bombay (IIT Bombay)
• Indian Institute of Technology Delhi (IIT Delhi)
• Indian Statistical Institute (ISI)
• Indian Institute of Science (IISc)
• Indian Institute of Information Technology (IIITs)
• International Institute of Information Technology (IIIT-Hyderabad)

Primary recruiters of Machine Learning Specialists

The primary recruiters of machine learning specialists are:

• Technology companies like Google, Facebook, Amazon, Microsoft, and Apple use machine learning extensively in their products and services.
• Financial institutions such as banks, hedge funds, and insurance companies employ machine learning for risk assessment, fraud detection, and trading algorithms.
• Healthcare organisations and pharmaceutical companies that apply machine learning for diagnostics, drug discovery, and patient care optimisation.
• Autonomous vehicle companies such as Tesla, Waymo, and Uber rely on machine learning for developing self-driving technologies.

Read more The Role of Career Coach in Career Planning: Why Every Parent Should Consider It?

Machine Learning vs Data Science: What’s the Difference?

Data science focuses on studying data and extracting meaningful insights, while machine learning specialises in developing methods to use data to enhance performance or make predictions. Machine learning is a subset of artificial intelligence.

Machine learning and artificial intelligence (AI) have recently taken a leading role in data science, significantly impacting data analytics and business intelligence. Machine learning automates data analysis and extends to making predictions by collecting and analysing large datasets from specific populations. This is achieved through the development of models and algorithms.

Data science focuses on understanding and extracting insights from data, while machine learning uses those insights to automate tasks and make predictions. Data science provides the basis for exploratory analysis and hypothesis testing, whereas machine learning transforms these insights into actionable intelligence.

In summary, data science utilises statistical techniques and methodologies to analyse data and extract valuable insights. From hypothesis testing to regression analysis and classification, data scientists employ various statistical tools to identify patterns and relationships in data, validate hypotheses, and make informed decisions.

Role and Responsibilities of Data Scientist and Machine Learning Specialist

Data Scientist	Machine Learning Specialist
Data scientists use software tools like Python and R to analyse and visualise data.	Create, develop, upkeep, and enhance machine learning algorithms for the organisation's products.
They are also responsible for effectively conveying their findings and insights to stakeholders who may not have technical expertise, ensuring clear understanding and communication across the organisation.	Ensure the organisation integrates machine learning principles into its decision-making processes, leveraging data-driven insights for strategic business decisions.
Their duties encompass gathering and scrutinising data, as well as providing recommendations for refining business procedures.	The core duty of a machine learning specialist entails creating, constructing, and deploying machine learning models to address various challenges and issues.
Data scientists need proficiency in coding and statistical software such as R or Python. They should handle diverse datasets, from social media analytics to healthcare data.	Another key responsibility of a machine learning specialist involves testing and assessing their machine learning models and algorithms using real-world datasets.

Conclusion

• Data science and machine learning are highly in demand, driving significant innovation across various sectors.
• Data science involves studying data to extract valuable insights, while machine learning specialises in using those insights to automate tasks and make predictions.
• Both data science and machine learning offer lucrative career paths that require proficiency in programming, statistical analysis, and data visualisation.
• Data scientists are responsible for analysing and visualising data, providing recommendations for business processes, and effectively communicating insights to stakeholders.
• Machine learning specialists design, develop and implement machine learning models for solving different problems, and they test and evaluate these models using real-world datasets.