MS in Information Science: Machine Learning No GRE/ TOEFL required* Save USD 67,750 Upto 3 years STEM OPT Visa in the US *Applicable for selected countries only No GRE/ TOEFL required* Save USD 67,750 Upto 3 years STEM OPT Visa in the US *Applicable for selected countries only Online/Hybrid • 2 years Download Brochure Apply Now Application Closes 17th Oct 2023 Enquire: +1 520 341 2109 Program Delivered by: In Collaboration with: RANKINGS AND RECOGNITIONS TOP 50 PUBLIC UNIVERSITY U.S. News & World Report 2022 TOP 100 GLOBAL UNIVERSITY U.S. News & World Report 2021 TOP 0.47% WORLDWIDE UNIVERSITY Center for World University Rankings 2022 Why Choose this Program? Fully Online or Hybrid Format Choose between a fully online and hybrid mode of learning (start online, complete on-campus in the US). Save USD 67,750 as compared to full-time US master’s (applicable for Hybrid). Eligible for up to 3 years STEM OPT Visa in the US (applicable for Hybrid). Learn from World-Class Faculty Learn from University of Arizona faculty over live online classes and in-person classes (if on-campus). 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 Choose between a fully online and hybrid mode of learning (start online, complete on-campus in the US). Save USD 67,750 as compared to full-time US master’s (applicable for Hybrid). Eligible for up to 3 years STEM OPT Visa in the US (applicable for Hybrid). 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. Online/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 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 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 Online and hybrid formats with mentorship provided throughout the master's degree 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 Program Fees Online USD 11,000 Hybrid (Online+On-Campus)* USD 34,176 *Save USD 67,750 as compared to full-time US master’s Earn Master’s Degree from 2 years Program Choose between a fully online and hybrid mode of learning ( first year online, second year on-campus) 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 (for hybrid students). 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: 17th Oct 2023 Apply Now Batch Start Dates Online/Hybrid To be announced Frequently Asked Questions Program Details What is the Master’s in Information Science: Machine Learning Program from the University of Arizona and Great Learning? The MS in Information Science: Machine Learning is a 21-month online program offered by the University of Arizona in collaboration with Great Learning with the aim to empower one to leverage the opportunities created by the information age and the advent of machine learning. From the foundations of information management to complex applications of it with machine learning, the program provides a holistic overview of the data ecosystem. Through this program, students will receive career mentoring support from Great Learning, which includes creating an e-portfolio, resume review, interview preparation sessions, access to an exclusive job board, and career guidance. This career support platform assists students in paving the way for a successful career path in Information Science and Machine Learning. The online mode of learning will furthermore let the students continue working while upgrading their skills and save up on accommodation costs. Post completion of the program, they will receive a MS in Information Science from University of Arizona and join the global alumni network of the University of Arizona that gives them access to extensive career and alumni support to advance in their careers. Will the sessions be live or recorded? Both live sessions and pre-recorded video lectures will be used. Live sessions will also be recorded so that students who can't make it to a session or want to review it afterward can do so by watching the session recording. What will be the duration of the live sessions in this Information Science and Machine Learning Master’s Program? The typical duration of a live session would be 2.5 to 3 hours in a month. How many live online sessions are conducted in every course? There will be at least four live sessions in every course, while the exact number of sessions will depend on each faculty member. Is this course WES accredited? WES accreditation is only required for courses from universities outside of the US to prove that they are equivalent to one by an accredited US university. For example, WES may look at a course by an Indian engineering college that assigns points out of 100 and then converts it to an equivalent score for a similar course in the US out of a 4.0 GPA scale. As the Master’s in Information Science: 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. What happens if I am unable to attend a live session? While attendance to the live sessions is strongly encouraged it is not mandatory. You can view the session recording on the LMS (Learning Management System) at a later time of your convenience. Can I complete this Master’s in Machine Learning Program in an accelerated manner by taking multiple courses per term? This program follows a cohort-based approach. So, students are required to finish these courses in a specified order and time period. Will every course in this Master’s in Machine Learning Online Program have a final examination? Each faculty member will determine the grading system of their respective subjects and share the evaluation criteria with you at the beginning of each course. How much time do I need to dedicate to this degree every week? This program is a master’s degree and will be rigorous in nature. While the time needed will vary depending on prior knowledge, you should plan to spend around 8-10 hours every week. What distinguishes online master's degrees from traditional classroom-based learning and on campus programs? While in terms of curriculum design, degree and transcripts, and access to university resources, this online degree is the same as an on-campus program, the key difference comes in how the curriculum is delivered. Classroom based learning relies on more in-person interactions where students attend lectures in a classroom, interact with faculty over office hours, have study sessions with TAs etc. In an online program all this interaction shifts so that it happens over the internet. Thus an online program offers more convenience, flexibility and safety (by providing social distancing) compared to a classroom program. The online degree programs are much more affordable compared to an on-campus program and the student also saves on housing in the US and opportunity cost of not working for two years. Is programming knowledge necessary for me to successfully complete the course? Knowledge of a programming language is not a prerequisite for this Master's Degree in Machine Learning and Information Science. GL provides extensive pre-course materials as well as in-course support to help bridge the gap. How long is the entire degree program? 21 months, 5 semesters of 4 to 4.5 months each. Can I take this Masters degree in Information Science and Machine Learning from anywhere in the world? Yes, as this program is offered online, students from any part of the world (except the United States and China)can take up the MS in Information Science: Machine Learning. What should I expect from this Master's Degree from the University of Arizona? After completing this master's in machine learning program, you will be able to: Review and report on information generation and processing to comprehend data from a larger perspective. Assess and enhance information management systems to discover best practices, gaps, and areas for improvement. Utilize machine learning approaches to address current business challenges. Work on the entire information management pipeline. Deploy the latest techniques to get valuable insights for growth to become the ideal ML leader. Which topics will be covered as a part of the MS in Machine Learning Program? 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 Artificial intelligence and machine learning skills - starting from the fundamentals and progressing to more complex, hands-on applications. Throughout this course, you would learn about several concepts like Data Mining and Discovery, Data Visualization, Data Warehousing and Analytics in the Cloud, Advanced Natural language processing, Neural Networks & more. You would work on several hands-on projects and case studies to apply machine learning techniques to solve real-world business problems and a capstone project that incorporates all the tools and techniques learned throughout the course. Why should you choose the MS in Information Science by the University of Arizona? There are a lot of amazing benefits this course has got to offer you.Here are a few of them World-Class Faculty Upon taking up the Masters Program, you would learn from the world-renowned faculty of University of Arizona through Live Interactive Sessions. Hands-on Learning Learners work on several hands-on projects and case studies to apply machine learning techniques to solve real-world business problems and a capstone project that incorporates all the tools and techniques learned throughout the course. Comprehensive Curriculum 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. Learners master several in-demand tools such as MS Azure TensorFlow, HuggingFace and more. Masters Degree from University of Arizona Upon the successful completion of MS in Information Science: Machine Learning program, you would receive a Master’s Degree from The University of Arizona that adds value to your resumes. What are the highlights of this program? FlexibilityTaught in a blended format with live online classes and recorded lectures, the program features hands-on projects and mentorship sessions to enhance the learning experience and is structured to suit the schedules of working professionals. Committed Student SupportLearners are facilitated with constant support from several industry experts that assist them in every stage of their learning journey. Career AssistanceGreat Learning offers excellent placement prospects when learners successfully complete this course. Comprehensive career assistance is offered to every learner to help them land the jobs they desire. Learn the most in-demand toolsUpon taking up this program, learners gain an understanding of the most in-demand tools such as Python, TensorFlow and more. The UArizona Advantage Through this MS in Information Science: Machine Learning program, you can join the global alumni network of the University of Arizona and access the extensive career and alumni support they offer to advance your career Admission queries How do I share my required documents with the admissions team? Learners can get in touch with their Program Advisors on guidance on how to go about completing their application. They can also directly go ahead and apply here. Are there any advantages to applying early for admission? Although there is no unique benefit to applying early, the sooner you apply, the sooner you will receive a response. We suggest candidates submit the application form as early as possible to avoid missing the deadline or having to scramble at the last minute. Some phases in the application process will take time, and the admissions team will carefully review each application. Since there is a limited number of seats for every cohort, candidates applying in time are more likely to get into a cohort of their choice. Some early applications also get shortlisted for scholarships. Please check with your Program Advisor for more details. What should I write in my Statement of Purpose (SOP)? There are no predefined set of questions that an applicant needs to answer. The SOP is more of a means for the admissions staff to learn more about an applicant's background. Some of the topics a candidate could address to accomplish this include: What inspired you to apply for this MS in IS: Machine Learning Program? How can this master’s degree help you reach your professional objectives? What strengths do you have that will help you make the most of this master’s degree to further your career? What academic documents are required to submit in order to be considered for admission? Applicants are required to submit their bachelor’s degree certificate, academic transcripts, resume, Statement of Purpose and a Letter of Recommendation. When can a learner expect the Admission Offer from University of Arizona? A clean and complete application with all the necessary documents will be reviewed and reverted to in two weeks. Will one be eligible to get a student visa for the U.S. post enrolment? No, since this is a fully online course, enrolment will not entitle students to a U.S. student visa. Eligibility What is the eligibility for the MS in Information Science? Students must have completed a 4 year U.S. bachelor’s degree or equivalent. A bachelor’s degree along with Master’s, 1st year of Master’s or 1 year Diploma. If the 3 year bachelor’s is equivalent to 120 credits or the transcript of the same states it is equivalent to 4 years then it is acceptable. Will my three-year bachelor's degree qualify me to apply? OR am I eligible to apply although I don’t possess an engineering degree? Students who earned their bachelor's degree outside of the US: You may apply for this program if your transcript review indicates that your degree is equal to a four-year U.S. bachelor's degree. Students who earned their bachelor's degree in the US: You may apply if you have a 4-year bachelor's degree. Do I need to give the GRE or GMAT test to qualify for MS in IS: Machine Learning No. GRE or GMAT scores are not required to qualify for this master’s degree. Do I need to clear an English language proficiency test to apply? No, if the medium of instruction for your bachelor degree was English. If the medium of instruction for the bachelor’s degree was not English then you would need to give an English language proficiency test like IELTS or TOEFL. Is a bachelor's degree in science or mathematics required to pursue this Master’s Degree in Machine Learning Although it will be helpful, having a background in mathematics and statistics is not a requirement. Potential candidates are urged to brush up on their mathematical skills if they haven't used them recently because this program's particular disciplines will all have significant quantitative components. The students also have access to Program Support by Great Learning in case of any issues faced during the program. With a dedicated program manager to solve all queries, mentorship from Industry Experts & peer interaction to build a network, the students get the right guidance and structure needed to successfully complete the program. Do only working professionals qualify for pursuing this online MS in Information Science and Machine Learning Program? Are unemployed students eligible for this degree? While this master’s degree program is intended for working professionals, any student who fulfills the prerequisites can enroll in this program. Fee and Payment What is the fee to enroll for this MS in Machine Learning Program? The tuition fee for this MS degree is USD 10,500. Are there any additional charges that the learner should be aware of? No. All the learner needs to pay is the program fee. This includes cost of examinations, access to relevant university tools and resources as well as necessary student memberships. Post Program Details What are the benefits offered by the University of Arizona to the learners upon taking up this Master’s in Machine Learning Program? Through this MS in Information Science: Machine Learning program, you can join the global alumni network of the University of Arizona and access the extensive career and alumni support they offer to advance your career. Bear Down Network - Directory of The University of Arizona Alumni with features such as Job Board, Mentorship Opportunities and much more. Wildcat Mentor Society - The Transformative Program to build strong professional networks and get guidance from seasoned mentors. Graduate Center - Resource for graduate and postdoctoral learners to enhance their academic and professional success. Lifelab - Platform to help learners with Resume-building, Cover letters, LinkedIn Profiles and much more. Handshake - The University of Arizona's official job board and campus interview system for hiring opportunities, career preparation resources and peer networks. Furthermore, students have access to university ID, free software licenses, offline resources and multiple workshops. For career development, they can also submit their documentation (CVs, Resumes etc.) for review online and get feedback from the counselor within 24-48 hrs. Will the final degree indicate that this was an online course No, the degree will not mention the mode of learning. The degree certificate and the transcripts will be the same as the ones offered for the on-campus program. 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 msis.uoa@mygreatlearning.com or +1 520 341 2109. Application Closes 17th 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 Select Campus Preferred Mode of Learning Online Hybrid (1st year Online & 2nd year on campus) 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 > × Thank you for applying Your application is with our team for the next steps.