A career in AI and data science offers a lucrative career path for B. Tech graduates. It's a rapidly evolving field that uses cutting-edge technology to gain insights and drive decision-making from the vast sets of data available. AI and data science needs a strong foundation in mathematics, statistics, programming languages like Python and R, and machine learning. The AI and data science job opportunities are diverse and include data scientists, machine learning engineers, AI researchers, and business analysts across the finance, healthcare, e-commerce, and automotive industries.

Machine learning, a subset of AI, not only supports accelerated career growth but also offers a platform for personal growth and development. Professionals who excel in machine learning techniques like neural networks, deep learning, and natural language processing are highly sought after by companies, providing them with exciting opportunities to push their boundaries and significantly impact their field.

What's BTech in AI & Data Science?

A B. Tech in AI & Data Science is not just a degree; it's a comprehensive programme designed to gain knowledge and skills necessary to thrive in AI and data science. It integrates computer science, mathematics, statistics, and machine learning concepts to provide a deep understanding of how to analyse and extract valuable insights from large datasets. Through projects, internships, and practical experience, you'll gain proficiency in programming languages like Python and R, along with the latest tech tools and software used in AI and data science, giving you the confidence to excel in your career.

Scope of AI and Data Science

The scope of AI and data science is vast and expanding, with several career options worldwide. With advancements in technology and the increased utilisation of data, organisations and businesses rely on AI and data science for innovation, improved decision-making, and competitive advantages. From finance, healthcare, retail and manufacturing, AI and data science are not just revolutionising processes but transforming industries. These fields enable businesses to harness the power of data to identify trends, predict outcomes, personalise experiences, automate tasks, and optimise operations, making them more efficient, effective, and competitive.

AI and data science are significantly impacting healthcare, as these facilitate the analysis of medical data to diagnose diseases accurately and predict patient outcomes for optimised treatments and drug usage. In finance, AI and data science are used in risk management, detecting fraud, scrutinising investments, and personalising customer experiences. Algorithms can analyse financial data to identify patterns, significantly reducing risk for financial companies. AI-powered automation is also involved in manufacturing processes, increasing efficiency, and improving product quality to rectify errors before they occur to maximise productivity. Fields like transportation, agriculture, energy, and cybersecurity also utilise AI and data science. As AI and data science evolve, the demand for skilled professionals increases. Overall, the scope of AI and data science is tremendous and can revolutionise the future.

Understanding B. Tech in AI & Data Science

The B. Tech Computer Science and Engineering (Artificial Intelligence & Data Science) programme at MIT-WPU covers various aspects of AI and data science, like statistics, knowledge discovery, machine learning, big data analytics, data visualisation, cognitive computing, and deep learning, which are the key areas. Beyond technical skills, the course helps students develop analytical and critical thinking skills that can help them easily navigate industries like healthcare and finance. Students can pursue different career paths with Systems and Edge Computing, Extended Reality, Business Analytics, and Computational Intelligence. Learning through industry expert lectures, faculty mentorship, and industry internships, students can be industry-ready!

Career Opportunities

Graduates are highly sought after across finance, healthcare, e-commerce, manufacturing, and technology. With the increasing reliance on data-driven insights and AI-powered solutions, organisations seek professionals to increase optimised processes. This includes data scientists, machine learning engineers, AI researchers, business analysts, and data engineers for customer segmentation, manufacturing, quality control and more.

Graduates can expect competitive salaries due to their specialised skill set and high demand. Entry-level positions offer higher salaries, with a chance for growth and advancement. Pursuing advanced certifications or higher education can enhance earning potential and career prospects.

Admission Process and Eligibility

For all B. Tech programs, candidates are required to have appeared in exams such as JEE 2024, MHTCET 2024, PERA 2024, and MHTCET-B* 2024. A minimum aggregate score of 50% in Physics, Chemistry, Mathematics/Biology or Physics, Mathematics, and any Technical Vocational Subject is necessary. Diploma candidates must have a minimum aggregate score of 60% in an appropriate branch from a UGC-approved University for both courses.

Now that you have read about the advantages of choosing a career in AI and data science, you can choose one of these—B. Tech Computer Science and Engineering (AI and Data Science) or Integrated B.Tech Computer Science and Engineering (AI and Data Science) at MIT-WPU—and set your career with the highest starting salary in this emerging and competitive field!

FAQs

What career paths can one pursue after completing a B. Tech in AI & Data Science?

After completing a B. Tech in AI & Data Science, you can become a data scientist, machine learning engineer, AI researcher, business analyst, or data engineer in finance, healthcare, e-commerce, and technology.

Should I choose CSE or AI and data science?

Choosing between Computer Science Engineering and AI and data Science is based on your interest; CSE offers broader knowledge, while AI and Data Science provide specialised analytics and machine learning skills for data-driven decision-making.