Also, read 8 Tips to Excel in the New-Age Job Market

Data Science and Machine Learning: Key Differences

Data science is the study of data from several perspectives to draw conclusions and inform action. Information is collected, cleaned, analysed exploratively, and predictive models are built using a variety of software and hardware tools.

The goal of exploratory data analysis (EDA) is to unearth previously undiscovered associations and relationships within a dataset.

The purpose of statistical analysis is to examine big data sets in order to make conclusions and take appropriate action. Statistical methods such as hypothesis testing, regression, and clustering can be applied.

In order to convey complicated information in an understandable and interesting way, data visualisation is a crucial tool. Report clarity and readability are both enhanced by data visualisation.

To anticipate or classify incoming data, data scientists employ predictive modelling, which involves spotting patterns in previously collected information. As these models are trained using past data, they progressively improve to perform better.

Machine learning, a branch of AI, is concerned with teaching computers to draw inferences and conclusions from data without being explicitly programmed to do so. In order to make accurate predictions or categorizations, machine learning algorithms can automatically learn these patterns and relationships.

Automatically Increasing Performance by Studying Examples and Fine-Tuning Parameters Machine learning algorithms can automatically increase their performance by studying examples. As they collect more information, they iteratively improve their forecasts.

Using their ability to recognise patterns and relationships in data, machine learning algorithms can then use this information to make predictions or classifications.

Machine learning models are taught to recognise patterns and generalise from training data through a process called “training” and “inference.” These models, once trained, can predict or take action based on novel, previously unseen data.

Healthcare, the financial sector, and the retail sector are just a few of the many industries that have found uses for data science and machine learning. In healthcare, data science is used to create algorithms that make medical diagnoses more precise. Watson Health from IBM, for instance, aids in the detection of cancer and Alzheimer’s disease. Using machine learning, doctors can better determine which patients will respond best to which treatments.

In the financial sector, data science is used to monitor fraudulent credit card activity by analysing transaction patterns. The risk of insuring an individual or company, for example, can be calculated using machine learning.

In the retail sector, data science is used to tailor product recommendations to individual customers based on their previous purchases. By analysing historical demand and supply data, machine learning can calculate the best possible price.

These illustrations demonstrate the widespread application of data science and machine learning, which have facilitated developments in many fields, including healthcare diagnostics, fraud detection, personalised recommendations, and pricing optimisation. We can expect even more creative uses and future contributions to these fields as they develop further.

Also, read Top Career Options after 12th Class

To understand better let’s look at the key differences in a tabular form.

Data science and machine learning are comparable areas, as shown. The two areas differ in aims, techniques, and applications.Data science includes machine learning. Statistics, machine learning, and data visualisation help data scientists get insights. Data science’s machine learning subfield builds models to predict or categorise data.

Machine learning has many fascinating uses. Healthcare, banking, fraud detection, natural language processing, and computer vision employ machine learning models. There are numerous wonderful resources for data science and machine learning students. These fields have many employment openings, therefore your talents will be in demand.

Data Science and Machine Learning: Top Courses

• Data Science Specialization by Johns Hopkins University on Coursera

• Machine Learning Specialization by Andrew Ng on Coursera

• Python for Data Science and Machine Learning Bootcamp by Udemy

• Deep Learning Specialization by Andrew Ng on Coursera

• Introduction to Machine Learning for Coders by fast.ai

• Statistics and Machine Learning for Data Science by Imperial College London on edX

• Machine Learning with TensorFlow by Google on Udacity

• Data Science MicroMasters by UC San Diego on edX

These courses are all well-respected and offer a comprehensive introduction to data science and machine learning. They are also relatively affordable, making them a great option for those who are looking to learn about these topics without breaking the bank.

Data Science and Machine Learning: Best Books to refer

In addition to these online courses, there are also a number of great books and tutorials available on data science and machine learning. Some of the most popular books include:

• Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani

• Machine Learning by Tom Mitchell

• Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville

• Introduction to Machine Learning with Python by Ameet Talwalkar

• Data Science for Business by Galit Shmueli

• Principles of Data Science by Abhinav Grover and Prashant Yadav

• Machine Learning for Absolute Beginners by Krishnan Ramanathan

• Data Science with R by Sanket Choudhary

Conclusion

As we continue our examination of Data Science and Machine learning, we hope you have learned about their important distinctions, found some great courses to improve your abilities, and uncovered a treasure mine of suggested books to further your understanding. Data Science helps us gain insights from massive data sets for materials research decision-making. Machine learning is a narrower and more focused approach to dealing with specific problems in revolutionising industries and influencing our future. Bridging these domains opens us to a world of data-driven machine learning, synthesis, and characterisation. Whether you select Data Science, Machine learning, or the intriguing intersection of the two, remember that innovation and discovery are limitless. Let curiosity guide you!

Also, read 8 Lucrative Career Paths for Mathematically-Gifted Students

Offered by the Mehta Family School of Data Science and Artificial Intelligence, this online program is designed to equip students with cutting-edge skills in AI and data science, providing industry-aligned learning, global exposure, and flexible exit options.

For more information and application submission, applicants can visit office website: www.iitg.ac.in/oes/odp/

About the program

Prof. Hemant B. Kaushik, Dean, Online Education and Skilling, IIT Guwahati, spoke about the program.

The application portal for the 2025 batch of our Online BSc (Hons) in Data Science & Artificial Intelligence is now open, said Prof Hemant of IIT Guwahati.

This program is a gateway to the future, designed to equip students with the most in-demand skills in the rapidly evolving fields of Data Science and AI.

At IIT Guwahati, we believe in making world-class education accessible to all, he said.

‘Our flexible, interactive, and industry-aligned curriculum ensures that students not only gain strong theoretical foundations but also hands-on experience with real-world applications.’

Whether you are a recent high school graduate or a professional looking to upskill, this program offers an opportunity to learn from renowned IIT faculty and industry experts without geographical constraints, said Prof Hemant of IIT Guwahati.

This is more than just a degree; it is an investment in your future! The program is designed to be highly affordable, with a very low fee structure, ensuring quality education from IIT Guwahati is accessible to all aspiring students, he said.

I encourage all prospective students and supportive parents to explore this incredible opportunity, he said.

Why choose IIT Guwahati’s B.Sc. (Hons.) in DS & AI?

The B.Sc. (Hons.) in DS & AI at IIT Guwahati follows a trimester system with three terms per year, offering:

Ø Comprehensive Curriculum: Covers fundamental to advanced topics, combining theoretical knowledge with hands-on training.

Ø Industry-Relevant Learning: Includes internships, term projects, and a final-year capstone project to ensure practical exposure.

Ø Engaging Course Structure: Each 12-week course features interactive video lectures, real-world case studies, and live sessions by IIT Guwahati faculty and industry experts.

Ø Global Student Community: Over 1,700 students from 21 countries, including the USA, Canada, China, UAE, Singapore, UK, Bangladesh, and Japan, are already enrolled.

Ø Recorded Lectures & Live Sessions: Attend recorded online classes by IIT Guwahati faculty and industry experts for flexible learning, with live doubt clearing sessions.

Ø Hands-on Industry Tools: Access to tools like Jupyter Notebook, RStudio, and VS Code via Coursera labs, eliminating the need for additional setup.

Ø Supercomputer Access: Leverage PARAM Kamrupa & PARAM Ishan, India’s most powerful supercomputers, for computational tasks during the capstone projects.

Ø Optional IIT Guwahati Campus Visit: A 4-week on-campus immersion (spread over four slots) for in-person training and networking.

Ø Annual IITG AI Confluence: Attend the annual AI confluence at IITG campus, a celebratory and collaborative event designed exclusively for IITG students, showcasing diverse academic and professional activities in Data Science and Artificial Intelligence.

In addition to this, the program allows students to exit at different stages with recognised certifications. These include:

· After 1st year – Advanced Certificate in DS & AI

· After 2nd year – Diploma in DS & AI

· After 3rd year – B.Sc. Degree in DS & AI

· After 4th year – B.Sc. (Honours) Degree in DS & AI

Students who exit at Certificate, Diploma, or B.Sc. degree stage can rejoin after a one-year gap to continue their education.

The program is designed for a diverse range of learners, including high school graduates from both science and non-science backgrounds who wish to pursue a dual degree or earn a second degree from a premier institute.

It is also ideal for working professionals and entrepreneurs seeking to upskill in AI and Data Science, as well as individuals looking to transition into new careers or expand their expertise in high-demand fields.

Ø Mathematics as a compulsory subject in Class 12 or equivalent

Ø Direct admission for candidates who were eligible for JEE (Advanced) in any year

Ø Other candidates must pass an online qualifier test (a preparatory online course is available to help students prepare)

Industry standards

With a curriculum designed to meet industry standards, students gain hands-on experience through real-world projects, ensuring they are well-prepared for the rapidly evolving job market in AI and data science.

Graduates of this program can pursue roles such as Data Scientist, Machine Learning Engineer, AI Researcher, Data Analyst, Business Intelligence Analyst, Big Data Engineer, and AI Consultant, among others, say sources from IIT Guwahati.

Upon completing the B.Sc. (Hons) degree in DS&AI, students will be eligible to appear for GATE and other competitive exams, enabling them to pursue higher education or advance into professional roles, say sources from IIT Guwahati.

As part of this initiative, a career readiness program has been designed, featuring curated courses, interactive workshops with industry veterans, and exclusive networking opportunities.

This program equips students with the essential skills and knowledge needed to accelerate their careers in today’s competitive job market.

Also Read – AAT 2025 Registration Begins from 2 June 2025, For B Arch in IITs