B.Tech in AI vs. B.Tech in Data Science: Which One is Better?

B.Tech in AI vs. B.Tech in Data Science

What is B.Tech in Artificial Intelligence?

Bachelor of Technology in Artificial Intelligence or B.Tech is an undergraduate engineering curriculum that lasts four years. The main goal of artificial intelligence is to create knowledge-based systems and an inference engine that can solve issues expertly. The goal of the B.Tech with AI program is to give students a solid foundation in both computer science and AI throughout their first two years of study. The following two years of courses will address the creation of intelligent machines as AI products, intelligent software as computer software, or applications that combine machine learning and artificial intelligence approaches.

Eligibility Criteria for B.Tech program in Artificial Intelligence

1. You can enroll in the course you want to take in essentially two ways. An institution might use the results of the 10+2 matriculation examination to determine the merit of candidates, or it might use an entrance exam.
2. Some colleges solely use their entrance exams, which are used to decide who is accepted into a certain program. When determining whether to admit an applicant to a B.Tech program, other colleges also consider the candidate’s performance in the JEE or similar all-India exam.
3. A detailed overview of the admissions procedure for B.Tech Artificial Intelligence program can be found below. Most colleges and universities require applicants to have a minimum 10+2 grade point average of 50% to be considered for admission.
4. The next phase is counseling, and some institutions could even require the candidate to pass a stage of in-person interviews.
5. Tech AI Course duration is 4 years.

Admission Procedure for B.Tech Artificial Intelligence Engineering

Both the entrance exam and the merit method are options for admission to the four-year B.Tech Artificial Intelligence program. To be considered for admission, several schools and universities require applicants to take both an entrance exam and a qualifying exam. Some universities use entrance exam results at the state or national level, such as JEE, in addition to qualifying exam marks when making admission decisions.

B.Tech Courses and Subjects in Artificial Intelligence

The following subjects are often covered in the curriculum for a B.Tech or M.Tech in Artificial Intelligence program:

1. Artificial Intelligence
2. Networks of neurons
3. Identification of patterns
4. Algorithms
5. Learning Machines
6. Processing of language
7. Robotics

 B.Tech AI Scope

Artificial intelligence will thus play a bigger role in the future. There are increasing opportunities in the field of artificial intelligence (AI). Don’t be afraid to try if you’re considering making the switch to artificial intelligence. Apart from that, positions in the artificial intelligence industry pay well.

India’s Top 10 Recruiters for B.Tech Students in AI

Artificial intelligence there is many employment options available to B.Tech graduates, and some of the biggest companies in India and abroad actively seek them out. A list of some of these companies is provided below:

1. Google
2. Facebook
3. Wipro
4. IBM
5. NIIT
6. Oracle
7. Genpact
8. HDFC Bank

Leading Recruiting Sectors

A graduate with a concentration in this field can work in the public or commercial sector in the following capacities:

1. Engineer in Machine Learning
2. AI & Big Data Architect
3. Scientist of Big Data
4. Engineer in Artificial Intelligence
5. Engineer for Artificial Intelligence Research
6. Consultant for Data and AI
7. Professional Robotics

B.Tech in Data Science

Graduates of the B.Tech degree in Data Science & Engineering are prepared to work as proficient data scientists. Along with the associated computer science, statistics, and computational mathematics courses, the focus is on fundamental data science topics. B.Tech DS Duration is 4 years.

Concentrate

A distinctive interdisciplinary fusion of computer science, computational mathematics, statistics, and management is data science. Designing business data storage and retrieval, as well as data-collecting techniques, are all part of data engineering. Predictive modeling, data analytics, visualization, and analytics for data-driven decision-making are the main topics of the fundamental data science courses. Relevant case studies will be used to teach knowledge representation, machine learning, artificial intelligence, and deep learning. Students can take electives in blockchain technology, quantum computing, data forensics, data privacy, algorithmic trading, and data security that are tailored to their company. A minor in business, finance, or healthcare analytics is another option available to students.

Outcomes

Graduates in engineering will be capable of:

1. Engineering knowledge: Solve complicated engineering issues by applying your understanding of science, math, engineering principles, and your engineering expertise.
2. Problem analysis: Using the fundamentals of mathematics, the natural sciences, and engineering sciences, identify, create, examine, and analyze difficult engineering issues to draw well-supported conclusions.
3. Design and development of solutions: Create solutions for challenging technical issues and system parts or procedures that satisfy requirements while taking into account cultural, socioeconomic, environmental, and public health and safety factors.
4. Investigate complicated issues: To provide reliable results, apply research-based knowledge and research techniques such as experiment design, data analysis and interpretation, and information synthesis.
5. Usage of modern tools: Develop, choose, and apply suitable methods, materials, and contemporary IT and engineering technologies, such as modeling and prediction, to intricate engineering tasks while being aware of their limits.
6. The engineer and society: Evaluate social, health, safety, legal and cultural concerns and the ensuing obligations pertinent to professional engineering practice using reasoning based on contextual information.
7. Sustainability and the environment: Show that you are aware of the need for sustainable development and comprehend how professional engineering solutions affect social and environmental surroundings.
8. Ethics: Adhere to professional ethics, duties, and engineering practice norms by putting ethical concepts into practice.
9. Individual and team work: Perform well both on your own and as a leader or member of a variety of teams in interdisciplinary contexts.
10. Communication: Effectively communicate complicated engineering tasks to the technical community and the general public. This includes understanding and producing quality reports and design documentation, giving and receiving clear directions, and making successful presentations.
11. Lifelong learning: In the broader context of technological change, acknowledge the necessity of, and possess the skills and readiness to engage in autonomous, lifelong learning.

Opportunities for Employment in B.Tech Data Science

Graduates of B.Tech program with a Data Science curriculum are prepared to apply for AI-driven solutions, create predictive models, and analyze complicated information. Professionals in this industry have access to attractive employment prospects in technology, finance, healthcare, e-commerce, and consulting due to the increasing need for data-driven decision-making across industries.

Employment Possibilities:

1. Create machine learning models as a data scientist to glean insights from data.
2. Examine and present data to help inform business choices.

Company Analyst:

1. Analyze data patterns to improve company tactics and procedures.
2. Create and manage scalable data structures and pipelines.
3. Create and apply AI models for analytics and automation.

B.Tech DS Scope

1. Growing Demand: Data scientists are in high demand across a range of sectors, including technology, healthcare, retail, and finance. Businesses are constantly searching for qualified people with the ability to decipher and evaluate complicated data.
2. Cross-Industry Application: Data science expertise may be applied in a variety of fields. The applications are numerous and diverse, ranging from forecasting patient outcomes in healthcare to evaluating customer behavior in retail.
3. Integration of Cutting-Edge Technologies: Professionals in the industry frequently deal with cloud computing, AI, machine learning, and big data technologies, giving them exposure to contemporary technology stacks.
4. Entrepreneurial Ventures: Graduates with the necessary abilities can work for data-driven firms that provide insights and data-driven solutions.
5. Opportunities for Advanced Education: To expand their expertise and improve their professional prospects, B.Tech graduates can choose to pursue additional coursework such as an M.Tech, MS, or specialty certifications in data science and big data.
6. Impactful Work: Data science enables practitioners to work on initiatives that improve public health outcomes, streamline corporate processes, and improve consumer experiences, among other real-world applications.

Do Check B.Sc CS vs. B.Tech CS: Key Differences & Career Scope

Why opt for IIMT?

Curriculum with an Industry Focus

1. A combination of big data, AI, machine learning, and statistical technologies intended to address practical issues.
2. Expertise in cloud computing, deep learning, predictive modeling, and data visualization.
3. Courses that incorporate industry-driven projects, hackathons, and hands-on learning.

Data Science Center of Excellence

1. AI-powered laboratories and state-of-the-art research facilities.
2. Industry partnerships are promoting data science and artificial intelligence innovation.
3. Specialized courses in Microsoft Azure, AWS Cloud, and Big Data Analytics.

About College

IIMT College of Engineering’s B.Tech in Artificial Intelligence (AI) and B.Tech in Data Science programs are developed to give students a solid foundation in the quickly changing technological domains. Both of these programs seek to educate students for employment in data-driven and AI-driven sectors by giving them the tools they need to innovate and solve challenging challenges in a variety of fields, including healthcare, finance, and the automotive industry.

Conclusion

B.Tech AI and B.Tech DS Difference ultimately comes down to personal interests, professional objectives, and the particular skill sets one wants to acquire.  For those who are enthusiastic about developing autonomous solutions, B.Tech in AI is the perfect choice because it concentrates more on the creation of intelligent systems, robots, and machine learning techniques.  However, people who are interested in deciphering intricate datasets and generating insights could find B.Tech in Data Science program appealing since it places a strong emphasis on data analysis, statistical techniques, and data-driven decision-making. Both professions are expanding quickly, have good career prospects, and are in high demand across a range of sectors. Students must think about their areas of strength, the kinds of projects they like, and the latest developments in the labor market. Finding the appropriate fit for a successful career in the computer business ultimately comes down to matching academic pursuits with personal interests and professional goals. There is no clear-cut answer as to which path is superior.

This blog provides an overview of B.Tech in AI vs. B.Tech in Data Science.