MS in Information Science: Machine Learning Pursue an MS in the USA without a GRE or TOEFL from the University of Arizona. This Machine Learning program lets you save 55+ Lakhs INR, get a 3-year STEM OPT visa, and potentially earn scholarships, making it an ideal study abroad program. *Applicable for selected countries only Pursue an MS in the USA without a GRE or TOEFL from the University of Arizona. This Machine Learning program lets you save 55+ Lakhs INR, get a 3-year STEM OPT visa, making it an ideal study abroad program. *Applicable for selected countries only Hybrid • 2 years Download Brochure Apply Now Application Closes 20th Oct 2023 Enquire: +1 520 341 2109 Program Delivered by: In Collaboration with: TOP 50 PUBLIC UNIVERSITY U.S. News & World Report 2021 TOP 100 GLOBAL UNIVERSITY U.S. News & World Report 2021 TOP 0.47% WORLDWIDE UNIVERSITY Center for World University Rankings 2022 Why Choose this MS in Information Science: Machine Learning Program? Hybrid Format Hybrid mode of learning with first-year online and second-year on-campus in US. Save USD 67,750 as compared to full-time masters in USA. Eligible for up to 3 years STEM OPT Visa in the US. Learn from World-Class Faculty Learn from the University of Arizona faculty over live online classes and in-person classes. Curriculum designed by leading experts to cover information science skills from fundamentals to complex, hands-on applications. Read More Practical, Hands-on Learning Get the best practical learning experience with the faculty of The University of Arizona, a world leader in research and innovation. Hands-on Projects and case studies to apply machine learning techniques to solve real-world business problems. Read More Hybrid mode of learning with first year online and second year on-campus in US. Save USD 67,750 as compared to full-time US master’s. Eligible for up to 3 years STEM OPT Visa in the US. Master's Degree from The University of Arizona Learn More Earn Master’s degree from One of the Top 100 Global Universities in the World, University of Arizona. Hybrid • 2 years Download Brochure Top Recruiters that hire from University of Arizona Career Opportunities and Earning Potential Computer and Information Research Scientists $130,000/year Machine Learning Engineer $130,000/year Information Security Analysts $110,000/year Data Scientist $100,000/year Experience learning at a world-class campus Living On-campus at Tucson, Arizona Tucson metro area is home to over 1 million people and has 300+days of sunshine in a year making it one of the most bikeable cities in the world. University of Arizona has 23 dorms, and dozens of fraternity and sorority houses. International Student Services The International Student Services office strives to support all international students through graduation. You will find immigration guidance, academic resources, cultural connections, and social events through the International Student Services office. 600+ Student Clubs and Organizations There are hundreds of student clubs and organizations at the University of Arizona for everything from niche hobbies to extracurriculars to career-oriented and professional groups. Complete Access to the UArizona Libraries A dozen state-of-the-art facilities are at your disposal. Hubs of technology, arts, sciences, and more make it easy to dive deep into knowledge. Top 10 Recreation Centers - Best Value Schools, 2021 University of Arizona’s Campus Recreation (Campus REC) is the health and well-being place to be. From individual and group fitness classes to outdoor adventures and aquatics, there is something for you no matter your level of ability or expertise. 24X7 Technical Support Access to technical support 24 hours a day, 7 days a week, will make you campus life easy.
Curriculum Curriculum designed by The University of Arizona Faculty and industry Experts 11 Projects including a Capstone Project to prepare you for real-world problems The curriculum is designed by leading experts at the University of Arizona and practicing industry professionals. It is specifically curated to cover a range of information science skills - starting from the fundamentals and progressing to more complex, hands-on applications. The program ensures that learners without prior expertise in data science and machine learning too can gain the latest knowledge thereby making it one of the best Information Science programs. Download Curriculum Curriculum Snapshot Foundations of Information Sensing the data Learn about types, sources and management of internal and external data including static vs streaming data, reports, databases and online data. Data collection Learn the process of extracting, transforming and making data available for further use. Data usability Learn the fundamentals of scoping the data by eliminating redundant elements and following logical, graphical, statistical analysis flow. Data Storage Get introduced to RDBMS, SQL, NoSQL, Data Marts, Data Lakes, ETL, and Data Pipelines as well as large information repositories and tools like Hadoop, Hive, Spark etc. Data Mining and Discovery Introduction to data mining Learn about data distributions and hypothesis testing. Use basic maths and common programing functions to handle data. Business problem identification and scoping Map data to business problems being solved and select appropriate elements of the data. Graphical Data Analysis Learn how to create basic graphs and analyze data visually. Use descriptive statistics to interpret data characteristics. Unsupervised learning techniques Use unsupervised learning like dimensionality reduction and clustering to discover elements of data. Introduction to Machine Learning Linear Modeling Learn how to do linear regression and linear classification with perceptrons. Extend linear model to various use cases. Learning Theory and Model Evaluation Understand bias-variance trade off and cross validation. Probabilistic methods Learn about maximum likelihood and bayesian approach including Priors, Marginal Likelihood & Hyperparameters Optimization and Approximation Methods Use Gradient methods (gradient descent, Newton’s method), sampling and Markov Chain Monte Carlo simulations (using Metropolis Hastings algorithm) Classification techniques Implement basic classification techniques like Logistic Regression, Bayesian Classification, Naive Bayes, Nearest Neighbors, Support Vector Machines. Domain specific techniques Understand Nonparametric Bayesian methods, Gaussian Processes, Topic Modeling, Ensembles, Boosting and Random Forests . Applied Natural Language Processing Textual Data Data Acquisition Manual Annotation Pre-Processing Text Representation Discrete Text Representations Word Embeddings Semantic Text Similarity Text Classification Text Classification with Linear Regression The FeedForward Neural Network for NLP Evaluation Metrics Sequence Processing Sequence Labeling Reading Comprehension Sequence to Sequence Pre-trained Language Models Language Modeling Pre-trained Language Models PLM Variants A Paradigm Shift Task Unification Instructed PLMs Data Warehousing and Analytics in the Cloud Introduction to Cloud Learn cloud computing basics for machine learning and data science. Explore Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Function as a Service(FaaS) in context of MS Azure. Setting up Cloud and parallel processing Learn about Cloud Storage Systems and virtualization. Use parallel programming tools and infrastructure in cloud like HDFS, Hive, SparkML. Data warehousing Develop data warehouses in the cloud. Develop and deploy machine learning models in cloud Data warehouse design Using SQL and NoSQL Data Warehouse,. Understand Designing and Querying Data Warehouses Data Ethics Ethical Foundations Learn about the birth of ethics, the role it plays in our daily lives and how to ensure ethical conduct in Data Science. Ethics in Data Learn how to ethically use and collect data, how to ensure privacy through masking and how to maintain anonymity and confidentiality of data and metadata. Learn about the various ethics & compliance initiatives undertaken to ensure privacy of information such as GDPR. Ethics in Modelling Learn the various problems faced due to algorithmic bias and how to take a data/model-driven approach to tackle this. Data Analysis and Visualization Theory of Visualization Learn about design, shapes and color theory behind visualizations. Single and Multiple dimension visualizations Use appropriate graphical design to depict the information. Integrate multiple elements of data to present compact and effective information. Visualization for audience Learn about selection and presentation of visual information according to audience requirements. Interactive visualizations Understand linking to databases, filtering and information highlighting for visualizations. Explore python integrations with D3.js Neural Networks Introduction to NN Learn about history, development and applications of Neural Networks Feed Forward NN Learn about NN architecture, Gradient based learning and Back propagation Regularization Understand Dropout, Parameter penalties and early stopping CNN and RNN Understand Convolution and Pooling. Apply Gated Recurrent Network along with Encoder-decoder Network. Understand transformer network Practical considerations and interpretability Understand Default baseline models and Debugging. Use Local surrogates and Saliency maps for Interpretability Advanced Machine Learning Applications Applying deep learning methods for computer vision (CV) including methods to train large CV models Generative modeling with industry applications (e.g., diffusion models, text-to-image models) Case studies showcasing business applications of deep learning Capstone Project Projects aligned to research prioroties of university. e.g, 1) Predicting climate change patterns using hydrological data from regions 2) Predicting climatic vulnerability based on historic data. 3) Using NLP in analysis of public health records and predicting population health parameters. 4) Using non-invasive imaging techniques to rapidly assess population health. Master's Degree from The University of Arizona Upon the successful completion of Information Science with Specialization in Machine Learning program, you would receive a Master’s Degree from The University of Arizona. Languages and Tools covered and more... Meet the Faculty Learn from the esteemed faculty at University of Arizona and practicing Industry Professionals with immense experience in the field of Information Science. Dr.Steven Bethard Associate Professor, School of Information Dr. Christian Roman-Palacios Assistant Professor of Practice, School of Information Dr. Hong Cui Professor, School of Information Dr. Bryan Heidorn Professor, School of Information Director, Center for Digital Society and Data Studies. Dr. Meaghan Wetherell Assistant Professor of Practice, School of Information Dr. Peter Jansen Assistant Professor, School of Information Find Answers with our Webinars Explore the webinars, a robust way of staying connected that allow you to collaborate and exchange information, ideas, and knowledge regarding studying abroad remotely. PAST Embark on the AI Journey: Explore and Forge a lucrative career 20 May 2023 | 08:30PM IST Catherine Brooks iSchool Director and Professor Founder, Center for Digital Society and Data Studies VIEW RECORDING Get The University of Arizona Advantage Bear Down Network Get access to the online directory of The University of Arizona alumni with features like job board, mentorship opportunities, and much more. Wildcat Mentor Society Join transformative program to build strong professional network and get guidance from mentors. Graduate Center A reliable resource for graduate and postdoctoral learners to enhance their academic and professional success. Lifelab Personalized career guidance platform to help learners with resume-building, cover letters, LinkedIn profiles, and more. Handshake Official job board and campus interviewing system for learners to access hiring opportunities, career prep resources, and communities. On-Campus Experience Interact with top-notch faculty and global peers at the University of Arizona campus in Tucson, Arizona. Great Learning Advantage Learn from a comprehensive curriculum taught by world-class faculty. Get guidance on your learning journey, and access dedicated career support. DESIGNED FOR PROFESSIONALS/FRESHERS Learn while pursuing your career Hybrid format with personalised mentorship Practical Insights from Industry Experts STRUCTURED PROGRAM WITH GUIDANCE Networking and Program support Dedicated program manager to solve your queries Mentorship from experts to gain industry insights Interact with peers to grow your professional network Learner Testimonials* David Phillips Master of Science in Information Science The program is delightful, and the professors taught me so much. I have absolutely enjoyed the program. Prashanth Shenoy L&TD Professional at a Fortune 500 Fintech Company My favourite part of the MSML program are industry expert sessions. Great Learning absolutely went that extra mile to bring in some of the best experts to demonstrate the concepts taught by the UoA professors. Loren Champlin PhD student in Information Science I found the research in AI, game design and ML really insightful. The faculty here finds time to talk to learners and has you best interests at heart. Mona Baid Member of Technical Staff My favourite part of the program has been the hands-on projects which are incredibly engaging and stimulating. I have been able to apply the concepts and skills that we have learned in class to real-world scenarios. Sarah Stueve PhD student in Information Science I was exposed to different projects in a breadth of areas. The University and Program caters to people from different backgrounds. *Testimonials from learners of School Of Information, University Of Arizona Fees and Application Details MS in Information Science: Machine Learning Program USD 34,176 *Save USD 67,750 as compared to full-time US master’s Earn Master’s Degree from 2 years Program Hybrid mode of learning with first year online and second year on-campus in US Comprehensive curriculum with hands-on learning and mentorship sessions Quick application with no additional tests or prerequisites Globally recognized Master’s degree from the University of Arizona Apply Now Application Process 1 Apply Online Complete filling a fast and easy online application form. No additional tests or prerequisites are needed to apply. 2 Pre-screening Our team will make contact with you by phone to confirm your eligibility for the program. 3 Application Assessment The Admissions team will assess your application and provide a timely response. 4 Join The Program If selected, you will receive an acceptance letter with instructions on how to pay and join the program. Great Learning provides end-to-end support in applying to the University of Arizona and also in the US visa application process during the first year. Who is this program for? Early Career Professionals Young, early-career professionals looking to go abroad and master information-driven innovation with knowledge of the latest information methods. Young, early-career professionals looking to go abroad and master information-driven innovation with knowledge of the latest information methods. Mid & Senior Level Professionals Mid and senior-level professionals who are looking to go abroad or learn online and stay up-to-date with the latest information management skills needed for success in the digital world. Mid and senior-level professionals who are looking to go abroad or learn online and stay up-to-date with the latest information management skills needed for success in the digital world. Working Professionals Professionals who wish to learn online without quitting their job and transition to an information management career with industry-ready skills and knowledge. Professionals who wish to learn online without quitting their job and transition to an information management career with industry-ready skills and knowledge. Upcoming Application Deadline Our admissions close once the requisite number of participants enroll for the upcoming batch . Apply early to secure your seats. Deadline: 20th Oct 2023 Apply Now Cohort Start Date Hybrid To be announced Frequently Asked Questions Program Details What is the MS in Information Science: Machine Learning Program from the University of Arizona and Great Learning? The Master’s in Information Science: Machine Learning is a 2-year hybrid program where students pursue their first year online and second year on-campus at the University of Arizona, USA. The program aims to empower them to leverage the opportunities created by the information age. It offers a comprehensive overview of the Data ecosystem, covering everything from the foundations of information management to its sophisticated applications in Machine Learning. The University of Arizona will provide career mentoring support to students in this program that helps students prepare for a successful Information Science and Machine Learning career. Upon successful completion of this MS Degree in the USA, students will receive an MS Degree in Information Science and Machine Learning from the University of Arizona and join the global alumni network of the University of Arizona, gaining access to extensive career and alumni support to advance in their careers. What are the rankings for the University of Arizona? Listed below are the global rankings for the University of Arizona: US News & World Report - The University of Arizona is ranked #105 out of 443 National Universities. QS - World University Ranking 268 THE (Times Higher Education) - University Ranking 150 US News & World Report - Global Universities 99 Is MS in the USA a good idea? Yes. It is an excellent choice to study MS in the USA. Here are a few reasons: Academic Excellence: The USA is home to many prestigious universities with excellent research facilities, innovative teaching methods, and diverse course offerings. If the university you're interested in is renowned for your chosen field of study, it might be a good choice. Career Opportunities: The USA is known for its vast job market, particularly in technology, engineering, and finance. Graduates from US institutions often find good job prospects both within the country and internationally. Networking: Studying Master’s in the USA can offer a vast network of alumni, faculty, and professionals in various industries, which can be beneficial for career growth and opportunities. Cultural Experience: Studying Master’s in the USA for international students provides them with a cultural experience that can enhance personal growth and understanding of different perspectives. What is the structure of this Master's Degree Program? MS in Information Science: Machine Learning is being offered in two modes for you to choose from: Completely online format: Study the entire program online with live and recorded sessions from the University of Arizona’s faculty and industry experts. Hybrid format: This format helps you gain the best of both worlds while saving an incredible amount of money. You can start with your first year online and move to the US to study your second year on campus at the University of Arizona, which also makes you eligible for up to 3 years of OPT in the US. How are the sessions conducted? They are conducted through live virtual sessions in the first year and in-person classes in the second year. What happens if I can’t attend a live online session? While participating in the sessions is not mandatory, attending them is highly recommended. The sessions are recorded in the LMS (Learning Management System) so that students who miss a session or want to review it later can always access them later. What is the duration of the live sessions in this Master of Science in Information Science and Machine Learning? A live session in this program typically lasts 2.5 to 3 hours a month. How many live virtual sessions are conducted in every course? Every course will have at least four live sessions, while the exact number of sessions will depend on each faculty member Is it possible to finish this Master’s in Machine Learning, USA, in an accelerated manner by pursuing multiple courses per term? The program follows a cohort-based approach. As a result, students must complete these courses in the designated order. However, in the second year (on campus), the learner can take more credits each semester to finish the program relatively faster. Will every subject in this MS in Machine Learning Program have a final examination? At the start of each course, each faculty member will decide on the grading policy for their specific subjects and share the evaluation standards with the students. We have observed that students are often assessed based on projects, assignments, and quizzes for various courses in the program. How much time must I devote to this MS in Machine Learning, USA, every week? This is a master's degree program that will be rigorous in nature. Although the time needed will vary depending on prior knowledge, students should set aside 15 to 18 hours per week. Is this course WES accredited? WES accreditation is only required for courses offered by universities outside the United States to demonstrate that they are equivalent to those offered by an accredited US university. For instance, WES might examine a course offered by an engineering college in India that awards points out of 100 and translate the results into an equivalent score for a program of a similar nature in the US using a 4.0 GPA scale. As the Master’s in Information Science and Machine Learning Program is from the University of Arizona, a US-accredited university, there is no question of having WES review and recognize the course for credits. How many and when are the intakes offered in a year? It is the same as the online program. There are two annual intakes, i.e., Spring (Jan) and Fall (August). The students can come to campus only in the subsequent Spring or Fall semesters, NOT the Summer semester. What are the benefits of a hybrid learning format? By combining the best aspects of in-person and online learning, the hybrid format offers an enriching and flexible educational experience. Here are some of the key advantages: Cost-effectiveness: The hybrid format can be more cost-effective than traditional full-time programs, as students can save on housing, transportation, and other on-campus costs while still benefiting from in-person interactions and experiences. Flexibility: The hybrid learning format allows students to balance their personal, professional, and academic commitments more effectively. With access to synchronous and asynchronous online sessions, students can choose when and where they want to engage with the course materials, making it easier to fit their studies around their daily routines. Make the most of your time: While studying the first year online, you can choose to work full time as the classes are mostly scheduled over weekends or after usual working hours. This helps you implement your learnings in the industry as you go and also gain salary and experience to assist you further settling down in the US faster. Personalized learning experience: The hybrid model accommodates various learning styles and preferences by offering a combination of in-person and online resources. Students can utilize online video lectures, discussion boards, and interactive exercises at their own pace while also benefiting from face-to-face interactions with the faculty and peers during on-campus sessions. Enhanced collaboration: It fosters collaboration and networking opportunities among students from diverse backgrounds. By engaging in online and in-person discussions, group projects, and workshops, students can share ideas and perspectives, forge professional connections, and enhance their understanding of the course material. Access to a global community: It enables students to interact with a broader network of students, faculties, and industry professionals worldwide. This diversity in perspectives and experiences enriches the learning environment and helps students develop a more comprehensive understanding of global trends and challenges in machine learning. OPT: As you would be studying 2nd year on-campus, you become eligible for a 3-year OPT in the USA to explore jobs, internships, and a rewarding career. The program lets you settle down with the curriculum first, and you handle the visa and the movement to the US process post that. Immediate application of skills: The MS in Information Science: Machine Learning program emphasizes hands-on learning through practical assignments and real-world case studies. The hybrid format allows students to apply their newly-acquired knowledge and skills in their current professional settings, providing valuable experience and enhancing their career prospects. Access to top-notch resources: Students in the hybrid program have access to both the University of Arizona and Great Learning's extensive resources, including cutting-edge research, industry partnerships, and experienced faculty. These resources enable students to stay abreast of the latest advancements in Machine Learning and Information Science. Can I take a break between the 1st year (online) and the 2nd year (on campus)? Yes. Students have a maximum of 6 years to complete their Masters. Students are allowed to take up to one year of break by applying for 'Leave of Absence' in between the program. Mostly, the university approves petition form requests like these. If the student takes a break between 1st year and 2nd year, they might have to apply for a VISA accordingly so that the dates mentioned on the VISA are according to the plan. If I have applied for a completely online format and later if I want to change my decision and pursue the program in a hybrid format instead, when can I inform the team and avail this opportunity? Students wishing to come to the main campus should begin the process six (6) months before they want to arrive in the US to allow enough time for the I-20/visa application process. How will the exams be conducted in the first year? It is the same as the online program. Exam structures are defined by the respective faculty at the start of the year. Students are generally assessed via quizzes, assignments, and projects. What happens if I fail any course in my first year? The student will have the opportunity to reappear for the exam with the next cohort. Students who fail an exam need to have a grade posted for the course they are enrolled in. They can retake a class with the next cohort. But they must maintain a 3.0 GPA or be placed on probation for one semester. The entire graduate probation policy is here: https://grad.arizona.edu/policies/academic-policies/academic-probation Is programming knowledge necessary for me to complete the course successfully? Programming language proficiency is not required for this Master’s Degree in Information Science and Machine Learning program. Great Learning provides extensive pre-course materials and in-course support to bridge the gap. How long is this MS in Machine Learning and Information Science Program? For the Hybrid program, the duration on campus would be 2 semesters (Fall and Spring) or 3 semesters (Spring, Summer, and Fall), depending on when they start. The online program component will depend on how soon the student can finish the program. Students pursuing OPT will need to be enrolled in 1 fall and 1 spring semester. Can I take this MS in Information Science and Machine Learning Program from anywhere in the world? Yes. As this program is offered in a hybrid format, students from around the world can take up this MS Program in Information Science and Machine Learning. What can I expect from this MS in Information Science in the USA? After completing this MS Degree in Information Science and Machine Learning, students will be able to: Examine and report on information generation and processing to gain a broader understanding of data. Assess and improve information management systems to identify best practices, gaps, and opportunities for improvement. Utilize Machine Learning techniques to address current business challenges. Work on the entire information management pipeline. Develop cutting-edge strategies to gain insightful information for growth to become the ideal ML leader. Which topics will be covered as a part of the MS in Machine Learning and Information Science Program? The curriculum is curated by leading experts at the University of Arizona and industry professionals. It is specifically designed to cover various Artificial Intelligence and Machine Learning skills - starting from the fundamentals and advancing to more complex, hands-on applications. Throughout this program, students will learn Data Mining and Discovery, Data Visualization, Data Warehousing and Analytics in the Cloud, Applied Natural language Processing, Neural Networks, Artificial Intelligence, and Machine Learning. Students will work on 11 hands-on projects and case studies to apply Machine Learning techniques to solve real-world business problems and a capstone project incorporating all the tools and techniques acquired throughout the program. Why should you choose the MS in Information Science and Machine Learning from the University of Arizona? There are a ton of excellent benefits, which are as follows: University Ranking The University of Arizona, a world-renowned public university, is ranked among the world’s best universities. Here are its rankings: US News & World Report - The University of Arizona is ranked #105 out of 443 National Universities QS - World University Ranking 268 THE (Times Higher Education) - University Ranking 150 US News & World Report - Global Universities 99 Comprehensive Curriculum Leading experts at the University of Arizona and industry professionals have curated the curriculum. It is specifically designed to cover various Artificial Intelligence and Machine Learning skills - starting from the fundamentals and advancing to more complex, hands-on applications. Students master in-demand languages and tools like Python, MS Azure, TensorFlow, Keras, HuggingFace, and many more. World-Class Faculty Upon taking up the Master’s Program, students will learn from the world-renowned faculty members of the University of Arizona through Live Interactive Sessions. Hands-on Learning Learners implement 11 hands-on projects and case studies to apply Machine Learning techniques to solve real-world business problems and a capstone project incorporating all the tools and techniques acquired throughout the program. Dedicated Career Assistance The University of Arizona will provide dedicated career support to students in this program via various job portals and counselling services, helping students prepare for a successful Information Science and Machine Learning career. Master's Degree from the University of Arizona Upon successful completion of the Master’s in Information Science: Machine Learning Program, students will receive an MS Degree from the University of Arizona that adds value to their resumes. What are the highlights of this program? Here are the key highlights of this program: Cost-effectiveness: The hybrid format can be more cost-effective than traditional full-time programs, as students can save on housing, transportation, and other on-campus costs while still benefiting from in-person interactions and experiences. Flexibility: The program is taught in a blended format with live online sessions and in-person classes, and it includes hands-on projects and mentorship sessions to enhance the learning experience. It is designed to accommodate the schedules of working professionals. Committed Student Support: Learners are provided continuous support from several industry experts who assist them at every stage of their learning journey. Career Assistance: After successfully completing this course, The University of Arizona provides excellent placement opportunities. Every student receives dedicated career assistance to help them find their desired jobs. Learn the most in-demand tools: Upon taking up this program, learners gain an understanding of the most in-demand tools, such as Python, Keras, TensorFlow, Microsoft Azure, HuggingFace, and more. Ease of Access: GRE/GMAT/IELTS scores are not required to study for this master's degree in the USA. Furthermore, there are no application fee charges, and the process is free of cost. OPT: As you would be studying 2nd year on-campus, you become eligible for a 3-year OPT in the USA to explore jobs, internships, and a rewarding career. The program lets you settle down with the curriculum first, and you handle the visa and the movement to the US process post that. The UArizona Advantage: Through this program, students can join the global alumni network of the University of Arizona and access the extensive career and alumni support they offer to advance their careers. What is the student-to-faculty ratio on-campus? About 25 students in a class on the Arizona campus. Can students explore assistantships remotely from the first year (online)? Yes. If any remote positions are available for RA/TA, hybrid students can explore them from the first year itself. They can also explore such on-campus positions in their final online semester, letting the faculty know of their arrival date on campus. Application Process Is there any application fee? No, the application fee is waived off for students applying for the Hybrid program in collaboration with Great Learning. Do I need to apply for a transcript evaluation to apply for the program? No, you do not need to apply for a transcript evaluation to apply for the program. How do I apply for the program? Application Process: Fill out the application form (https://apply.grad.arizona.edu/users/login) Submit the required documents.<transcripts, etc.> The university takes 2-3 weeks for the application review and results. If selected, an offer letter will be released. How do I share my required documents with the admissions team? Students can get in touch with their Program Advisors for guidance on completing their application, or they can share their documents (transcripts, SOP, degree certificate, CV) via email (msml.hybrid@greatlearning.in). Are there any advantages to applying early for admission? We recommend that candidates submit the application form as quickly as possible to avoid missing the deadline or scrambling at the last minute. Some stages of the application process will take time, and the admissions team will carefully review each application. Candidates who apply early are more likely to be accepted into the cohort of their choice because there are only a certain number of seats available for each cohort. Some early applications are also shortlisted for scholarships. Please check with your Program Advisor for more details. What should I include in my Statement of Purpose (SOP)? The program advisor shares the SOP guidelines with the learners, and the format is around their background and motivations, long-term career goals, etc. The admissions staff primarily uses the SOP to learn more about a candidate’s background. Some of the pointers a candidate could address include the following: What inspired you to choose this MS in Information Science: Machine Learning Program? How will this master's degree help you achieve your professional goals? What strengths will enable you to make the most of this master's degree to advance your career? What academic documents must be submitted to be considered for admission? Candidates must submit their bachelor’s degree certificate, academic transcripts, resume, Statement of Purpose (SOP), and a Letter of Recommendation (LOR). When can a candidate expect the Admission Offer from the University of Arizona? A precise, complete application that includes all required paperwork will be examined and responded to in two weeks. Will I be eligible to get a student visa for the United States after enrolling in the program? Yes. Students are eligible for up to 3 years of Post-Study Work/STEM OPT Visa in the United States. When do I apply for a VISA for the hybrid format? Here is the process: The student informs Great Learning (GL). GL will inform I-School and submit the necessary documents on behalf of the student. I-School informs ISS. ISS will issue I-120 to the student. Students get the visa from the embassy. It is recommended that students begin the process of applying for an I-20 and subsequent visa appointment 6 months in advance of the target date of coming to the US. Students will work with the University of Arizona’s International Student Center to apply for an I-20, which takes an average of 10 business days to process. After the I-20 is issued, students can apply at their local consulate for a visa appointment. How soon can the I20 get issued to the students? Students can apply for I20 right after the first semester. But she will speak to ISS to explore if one can start the I20 process even earlier, i.e., right after getting the offer letter from the university. What if I don't get the VISA to continue with the second year on-campus at the University of Arizona? The student can choose to pursue the program online. OR students can reapply for the visa and come in subsequent semesters/years after taking a Leave of Absence. Will I get assistance in applying for VISA? Great Learning will help students compile all the documents they must submit to school for I-20. The University of Arizona will issue the I-20 and supporting documents around the admission process, including letters of recommendation and any aid. Students have to go to the Embassy and then get a visa. Will I get assistance in finding suitable accommodation in Tucson? Yes, the Grad College and International Student Services will assist in helping students find accommodation. International Student Services provides referrals to the Office of Residence Life and Off-Campus Housing for assistance with finding accommodations. Eligibility What is the eligibility for the MS in Information Science and Machine Learning? Here are the eligibility requirements for the MS in Information Science and Machine Learning: 4-year accredited bachelor, OR 3-year accredited bachelor + master, OR 3-year accredited bachelor + post-grad diploma, OR 3-year bachelor by itself provided the degree is at least 120 credits, AND The degree has been earned in Division I, and the institution is accredited by the National Assessment and Accreditation Council (NAAC) with an institutional score of 'A' or better. Note: Graduate applicants from India are not required to provide an English proficiency test (IELTS/TOEFL) for admission to the Graduate College; however, some degree programs may require proof of English Proficiency. Please see the course for specific requirements. Also, students from India must demonstrate English proficiency to work as graduate teaching assistants/associates. To find out the English proficiency requirements for other nationalities, please refer to: https://grad.arizona.edu/catalog/intladmissionsreqs/ Will my 3-year bachelor's degree qualify me to apply? OR am I eligible to register even though I do not have a degree in engineering? Students who secured their bachelor's degree outside of the US: You can apply for this program if your transcript review indicates that your degree is equivalent to 120 credit points. Students who secured their bachelor's degree in the US: You're eligible to apply if you have a 4-year bachelor's degree. Can I apply for this Master’s in the USA without GRE or GMAT? Yes. GRE or GMAT scores are not required to study for this master's degree in the USA. Do I need an English proficiency test score (IELTS, PTE, TOEFL) to apply for the program? Not for India. But yes, for the rest of the world. Same as Online programs. To find out the English proficiency requirements for other nationalities, please refer to: https://grad.arizona.edu/catalog/intladmissionsreqs/ Note: TOEFL scores or similar may be required if the student is hired as a Graduate Assistant, especially in Teaching Assistant roles (https://grad.arizona.edu/funding/ga/english-speaking-proficiency-evaluation). Is it necessary to have a bachelor's degree in science or mathematics to pursue this Master's in Information Science in the USA? While a background in mathematics and statistics is advantageous, it is not required. Required concepts will be covered for you in the pre-course work. In case of any problems during the program, students can contact Great Learning's Program Support. Students receive the necessary guidance and structure to successfully complete the program, including a dedicated program manager to answer all questions, mentorship from industry experts, and peer interaction to build a network. Are only working professionals eligible to pursue this MS in Information Science and Machine Learning Program? Can unemployed students pursue this degree? Working professionals, as well as freshers who meet the prerequisites, can apply for the program. Fee and Payment What is the enrollment fee for this Master’s Degree in Information Science and Machine Learning Program? The online format of MS in IS: ML costs around USD 10,500. The tuition fee for the hybrid structure (Start online, finish in the US) is around USD 34,000. Candidates can save INR 55+ Lakhs compared to a full-time US Master’s Degree. Will Great Learning help me with financial assistance? Yes. Great Learning is associated with Gyandhan (One of the largest marketplaces for education loans). Our counsellors will help you get in touch with the Gyandhan team and explore the best education loan suiting your needs. Please note that this service is available for Indian students only. Can I work in the US on an F1 visa to support my studies or living expenses? If yes, can I work on the university campus? Yes. International students can work on campus for the University of Arizona for up to 20 hours per week. On-campus employment does not require authorization from International Student Services. https://international.arizona.edu/international-students/on-campus-employment How do I apply for scholarships or financial aid from the university? For scholarships, students can apply here: https://financialaid.arizona.edu/types-of-aid/scholarships RA/TA positions: Students can apply for these positions in iSchool as well as other departments. They could use the Handshake portal for this. Students can also go to employment and career services. The ISS can help students look for these on-campus jobs. What is the fee payment schedule on-campus? Tuition for a specific term is due on the first day of the semester. Before the semester starts, students may enroll in a Payment Plan that splits their fees into three equal sums. You can learn more about Payment Plans and on-campus tuition due dates here: Tuition Payment Plan | Bursar (arizona.edu) Post Program Details Will I be eligible for OPT in the US? The program pursued in a hybrid format makes you eligible for up to 3 years of STEM OPT in the USA. (OPT 12 months and extension of 24 months after the first year for STEM courses). The OPT 3-year extension applies to companies approved by eVerify. Link: https://international.arizona.edu/international-students/opt What are the advantages of pursuing this Master's in Machine Learning and Information Science Program at the University of Arizona? Through this Master's in Machine Learning and Information Science Program, students can join the global alumni network of the University of Arizona and access the extensive career and alumni support they offer to advance their careers. Bear Down Network - The University of Arizona alumni directory offers features like access to the Job Board, Mentorship Opportunities, and more. Wildcat Mentor Society - The Transformative Program to establish solid professional connections and receive guidance from seasoned mentors. Graduate Center - Resource for graduate and postdoctoral students to improve their academic and professional success. Graduate Center offers career advising, support programs, workshops, networking groups, and more to current graduate students (https://gradcenter.arizona.edu/career-support). Lifelab - Platform to help students with resume-building, cover letters, LinkedIn profiles, and many more. Handshake - The University of Arizona's official job board and campus interview system for hiring opportunities, career preparation resources, and peer networks. Office of Student Engagement & Career Development - It offers general career support, job fairs, workshops, and resources to all University of Arizona Students (https://career.arizona.edu/). Students can also access a university ID, free software licenses, offline resources, and various workshops. For career development, they can also submit their documentation (CVs, Resumes, etc.) for review online and receive feedback from the counsellor within 24-48 hours. Will the final degree indicate this was an online/hybrid course? No. The mode of learning will not be mentioned in the degree, and the degree certificate and transcripts will be the same as those offered for the on-campus program. Who are the major recruitment partners at the University of Arizona? Tesla, NASA, Boeing, Enterprise Rent-a-car, GEICO, Altria, American Express, Goldman Sachs, Vanguard, Charles Schwab, and many more- https://career.arizona.edu/ What could be the salary range post hybrid program in the US? Job Roles Salary Range Computer and Information Research Scientists $131,490 per year Data Scientist >$100,000 Information Security Analysts >$110,000 Machine Learning Engineer $1,36,511 What kind of job roles can I apply for post MS in IS: ML degree program? This degree program prepares for job roles like Information Architect, Data Engineer, Data Analyst, Database administrator, Data Scientist, AI/ML engineer, Digital Artist, Software Engineer, and a wide variety of leadership roles in various kinds of companies. Still have queries? Contact Us Please fill in the form and a Program Advisor will reach out to you. You can also reach out to us at msml.hybrid@greatlearning.in or +1 520 341 2109. Application Closes 20th Oct 2023 Download Brochure Check out the program and fee details in our brochure Oops!! Something went wrong, Please try again. Name Email Mobile Number Work experience in years Work Experience in years Currently a college student 0 Years Less than 1 Year 1-2 Years 2-3 Years 3-5 Years 5-8 Years 8-12 Years 12-15 Years More than 15 Years By submitting the form, you agree to our Terms and Conditions and our Privacy Policy. Submit Form submitted successfully Thank you for reaching out to us. You can expect to hear from us in 1 working day. Not able to view the brochure? View Brochure Browse Related Blogs 20 Deep Learning Applications in 2021 Across Industries Learn More > What is Artificial Intelligence? How Does AI Work, Applications and Future? Learn More > What is Artificial Intelligence Learn More > Artificial Intelligence Books Learn More > Machine Learning Interview Questions Learn More > What is Machine Learning Learn More > Prerequisites for Machine Learning Learn More > Deep Learning vs Machine Learning Learn More > Deep Learning Applications Learn More > AI Technology Learn More >