Data Science Graduate Program Student Handbook, Study notes of Database Management Systems (DBMS)

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Data Science Graduate Program Student Handbook Last Revised: August 16, 2021 1 Table of Contents Data Science Graduate Program 1 Student Handbook 1 Table of Contents 2 Welcome! 4 Program Governance 5 Covid-19 Impacts 6 Getting Started 8 International Programs & Services Office (IPS) 8 Housing Information 8 Identification Card (HuskyCard) 8 Office 8 After-hours Access 8 Parking & Transportation 8 Employment Information 9 Payroll 9 Degree Information 10 Core Courses 10 Electives 10 Foundational Courses 10 Domain Elective Courses 12 Home Departments / Advisors 12 Changing Advisors / Home Departments 12 Registration / Scheduling 13 Forms and Degree Completion Timelines 13 Tuition Payment 13 Continuous Enrollment 13 Career Placement 14 Internships / Co-ops 14 Enterprise 14 University Policies 15 External Transfer Credits 15 Accelerated MS 15 Senior Rule 15 Re-using Credits 16 2 Program Governance The Data Science program is managed by an inter-disciplinary community of scholars and professional staff working together to support this program. Some of the key personnel include: Dr. Laura E. Brown - Data Science Program Director Home Department, College: Computer Science, College of Computing Office: Rekhi 307, lebrown@mtu.edu Dr. Benjamin Ong - Data Science Executive Committee Home Department, College: Mathematical Sciences, College of Sciences and Arts Office: Fisher 217, ongbw@mtu.edu Dr. Guy Hembroff - Data Science Steering Committee Home Department, College: Applied Computing, College of Computing Director: Health Informatics Graduate Program Office: EERC 311, hembroff@mtu.edu Dr. Jeffrey Wall - Data Science Executive Committee Home Department, College: College of Business Office: AOB G010, jdwall@mtu.edu Dr. Hairong Wei - Data Science Executive Committee Home Department, College: College of Forest Resources and Environmental Science Office: Noblet 176, hairong@mtu.edu Dr. Jianhui Yue - Data Science Executive Committee Home Department, College: Computer Science, College of Computing Office: Rekhi 203, jyue@mtu.edu Most inquiries can be directed towards Dr. Brown. E-mail is the preferred mode of communication. Graduate Student Government (http://gsg.mtu.edu) Data Science Representative: Surya Ravula, sravula2@mtu.edu Mr. Ravula will communicate information regarding travel grants, social activities, opportunities and other important information. He can also be used as a contact to communicate suggestions, complaints, and help to answer questions. 5 Covid-19 Impacts The whole world has seen the terrible impact of the covid-19 pandemic on our friends, family, communities, countries, and world. Here at Michigan Tech we have had to adapt to the pandemic as well. The latest news, policies, and plans at Michigan Tech, are updated on the MTU Flex page. For those who were studying during Spring 2020 and Track A Summer 2020, the following policies impact the program rules governing the program. ● Proposal 54-20 - Good Academic Standing: The Graduate School recognizes the additional stressors that graduate students are under due to disruptions caused by COVID-19 are many and are strong contributors to their ability to maintain or return to good academic standing in spring 2020. ○ Graduate students who are on probation after fall 2019 or who have been reinstated for spring 2020 following a suspension can return to good standing after spring 2020 if they satisfy the conditions of good standing as defined by Senate Policy 416.1. ○ Graduate students who are on probation after fall 2019 will not be suspended if they do not return to good standing in spring 2020 as defined by Senate Policy 416.1. These students will remain on probation for their next semester of registration. ○ Graduate students who are in good standing after fall 2019 will not be placed on probation if they do not meet the standards for good standing as defined by Senate Policy 416.1. These students will remain in good standing for their next semester of registration. ○ Graduate students who began at Michigan Tech in spring 2020 do not have an academic standing. These students will be in good standing after spring 2020 regardless of whether they meet the standards for good standing as defined by Senate Policy 416.1. ● Proposal 59-20 - Pass / Low Pass grades: Grades will be assigned using the normal grading scheme at the end of the semester. Graduate students will then have seven days after grades are posted to decide if they would like to switch any classes from spring 2020 to pass/fail grades (grades of SCV, LCV, or ECV). A pass grade (SCV) will be assigned for a grade of C or better. A low pass grade (LCV) will be assigned for grades of CD or D. A fail grade (ECV) will be assigned for a grade F. Once a graduate student elects the Pass/Fail option for a course and the final grade is changed, the decision is final and may not be reversed. ○ Considerations for SCV/LCV: The SCV and LCV grades will appear on your transcripts but not contribute to the cumulative GPA. SCV grades may satisfy graduation requirements (see below), but LCV grades can not. ● Proposal 60-20 - BC/C and SCV grades: The Graduate School and the Data Science program allows up to six credits of BC, C, or SCV grades to be used toward completion of a graduate degree. An additional three (3) credits of SCV may be allowed to count towards the degree requirements. Courses with an LCV grade will not meet degree requirements (e.g., completing a core or elective requirement), but will not negatively impact your cumulative GPA (like receiving a CD/D would). Recall, your degree completion requires a minimum cumulative GPA of 3.0. 6 For those who were studying during Fall 2020, the following policies impact the program. ● Proposal 30-21 - Extend Time for Completion of Incomplete: The time to complete an “I”, incomplete grade is extended one year past the end of the course. ● Proposal 33-21 - Change Date for Withdrawal with a “W”: Students are allowed to withdraw from a course with a “W” until Friday, December 11th (extended from Friday, Nov. 6th). ● Proposal 37-21 - Pass/Low Pass/ Fail for Fall 2020: Grades are assigned using the normal grading process. Students will then have seven days to decide if they would like to switch any classes to Pass/Low Pass/Fail grading. Students should talk to their advisors and others on campus about the impact of this change: GPA, course credit, academic standing, degree requirements, financial aid, transfer credit, visas, and acceptance into graduate or professional schools. Impacts of covid-19 on Michigan Tech, will continue into the Spring 2021, pay attention to emails, town halls, the MTU Flex webpage, to learn of the most recent changes. 7 Degree Information The Data Science Masters is a course-based program requiring successful completion of 30 approved credits within five years of starting the program. Specifically, ● 12 credits of core courses must be successfully completed ● at least 6 credits of approved electives must be successfully completed ● at most 6 credits of foundational courses may be taken ● 6 - 12 credits of domain electives may be taken. A passing grade (B or higher) must be obtained in 24 of the above 30 credits; a grade of BC or C may be accepted for the remaining 6 of the 30 credits. Additionally, at least 18 credits must be taken at the graduate level (5xxx and 6xxx). Core Courses The following four courses are required for the Data Science degree. These courses are often offered only once a year. You will need to plan accordingly. Fall* UN 5550 Introduction to Data Science MA 5790 Predictive Modeling BA 5200 Information Systems Management and Data Analytics Spring UN 5550** Introduction to Data Science MA 5790*** Predictive Modeling CS 5831 Advanced Data Mining *Note, many students do not need to take all three core courses in their first Fall semester. UN 5550, Introduction to Data Science is recommended for this first semester, but the choice of the other core course should be discussed with your advisor before the semester begins. **Due to covid-19, spring admissions have been opened up, therefore an additional offering of UN 5550 has been added to the Spring 2022 term. Note, students starting in Fall 2021 are highly encouraged to enroll in UN 5550 for the Fall semester - this course helps prepare you for other courses you may take. ***In Spring 2022, there are plans to also offer MA 5790. Note, the spring offering may not be available all years going forward. Electives At least 2 courses, 6 credits, must be taken from the list of approved elective courses in Table 1. Note, the options have changed over the years, be sure to select courses given the year you entered the program. ** Class offerings might change without notice. Please refer to the Registrar’s schedule of classes for actual class offerings. 10 Table 1. List of Elective Courses Academic Year student joined the program Course Course Offered 2018 - 2019 2019 - 2020 2020 - 2021 2021 - 2022 BA 5740 - Managing Innovation and Technology Fall X CS 5631 - Data Visualization Fall, Spring X X X CS 5841/EE 5841 - Machine Learning Spring X X X X CS 5471 - Computer Security Fall, Spring X X X X FW 5083 - Programming Skills for Bioinformatics Fall, alt. years X X X X MA 4710 - Regression Analysis Fall X X MA 5770 - Bayesian Statistics Fall, alt. years X X X MA 5781 - Time Series Analysis and Forecasting Spring X X X X MGT 4600 - Management of Technology and Innov. Fall, Spring X X X PH 4395 - Computer Simulation in Physics Spring X PSY 5210 - Advanced Stat. Analysis and Design I Fall, alt. years X X X X SAT 5114 - Introduction to AI and Health Fall X X X UN 5390 - Scientific Computing Fall, Spring X X X X Foundational Courses A maximum of six (6) credit hours of foundational skills course may be applied to the MS in Data Science. These courses will build skills necessary for successful completion of the MS in Data Science. Some students will not need to take these foundational courses and will instead use the domain electives to reach the credit requirements of this program. A list of foundational courses that can be taken towards the data science program is listed on the data science website and in Table 2. Domain Elective Courses Appendix A contains an extensive list of domain elective courses that can be taken towards the data science program. Your remaining credits of domain elective courses, 6-12 credits, can be taken towards the data science degree. Note: If there is a course not on the Domain Elective Course list, a student may petition the Graduate Program Director for its consideration. This petition must be submitted before the start of the semester for consideration. 11 Table 2. List of Foundational Courses Course Course Offered CS 3425 - Introduction to Database Systems Fall, Spring FIN 3000 - Principles of Finance Fall, Spring, Summer FW 3540 - Introduction to Geographic Information Systems for Natural Resource Management Spring MA 3710 - Engineering Statistics Fall, Spring, Summer MA 3715 - Biostatistics Spring MA 3740 - Statistical Programming and Analysis Fall, Spring MIS 3100 - Business Database Management Spring MKT 3600 - Marketing Data Analytics Spring SAT 3002 - Application Programming Introduction Fall SAT 3210 - Database Management Fall, Summer SAT 3611 - Infrastructure Service Administration and Security Fall, Summer Advisors The Data Science program is an interdisciplinary collaboration across the university; Data Science graduate students will have an opportunity to select an advisor that matches their interest/domain background. There will be an opportunity to meet some of the advisors during the data science program orientation and early fall activities. Program orientation for fall admissions typically occur the week before classes begin. Students will be asked to select and meet with an advisor by the second week of their first semester. Advisors should typically be selected based on which domain electives you intend to specialize in. For example, if you intend to mostly take courses offered by the Math department as your domain electives, you should consider requesting Dr. Ong as your advisor. Your advisor should provide academic and career advising. Academic advising entails ensuring that your courses are suitable for your career goals and you have the necessary background to succeed in the courses. Career advising entails providing you a glimpse of what the industry in your domain specialization might look for in potential hires, connecting you with other researchers in their department/college, and potentially connecting you with recruiters. All students will default to the Program Director (Dr. Brown) as their advisor. Changing Advisors Within the data science program, there should be little cause to request a change of advisors or home departments. The advisors work together as a team, communicating with each other regularly as part of the steering committee. 12 University Policies Here are some of the university policies that pertain to the data science program. A full listing of University policies is found online. External Transfer Credits To transfer credits from another university or college to the data science program, please be aware that : ● A maximum of ten transfer credits can be applied towards the Data Science degree unless special arrangements have been made between Michigan Tech and the second institution. The number of credits accepted depends on an evaluation by the Data Science program and the dean of the Graduate School. ● A grade equivalent of “B” or better must be earned in the course to be transferred. ● The proposed course to be transferred must be pre-approved by the Data Science Executive Committee before the end of the semester prior to the semester you intend to take the transfer course, and must be taken at an institution accredited by the Higher Learning Commission (HLC). The course cannot duplicate courses that have already been taken at Michigan Tech. The student will provide the name of the university, a course number, name, and description, and the most recent syllabus for the course. A Michigan Tech faculty member responsible for teaching the required course will be consulted as to whether the proposed course is of equivalent content. Accelerated MS The accelerated Masters in Data Science program is open to all high achieving undergraduate students at Michigan Tech. It allows students to double count up to six courses toward both the Bachelor’s and MS degree. Students with an overall GPA of 3.0 or higher can apply for admission to the accelerated MS in Data Science program any time upon attaining junior class standing, but must apply prior to being awarded their bachelor’s degree. Students should meet with the Data Science program director and their undergraduate advisor to plan what courses may be double-counted and allowed senior rule courses. All courses counted under the senior rule and all double-counted courses applied to the accelerated MS in Data Science degree must have a grade of B or higher. Senior Rule Michigan Tech undergraduates may take up to 10 Data-Science approved credits hours in their senior year, and use these credits towards a Data Science Masters degree. A grade of “B” or higher must be attained for these credits to count towards the graduate degree. Note, these credits do not count towards the undergraduate degree, and are independent of double-counted courses. 15 Re-using Credits Students may double count up to 10 credits from one other Michigan Tech graduate program toward a Data Science masters degree, with the approval of the Data Science Program Director. Graduate credits earned toward the completion of a graduate degree at an institution other than Michigan Tech cannot be applied toward this degree program (this is a Michigan Tech policy). Good Academic Standing and Dismissal The Data Science Graduate Program follows the Graduate School policies on Good Academic Standing and Grading Policy. Academic Grievances / Grade Appeals Students wishing to appeal a grade assigned by a faculty member at Michigan Tech should follow the procedure described in the Michigan Tech Policy Statement under Academic Grievances. Policies for Repeating Courses For the data science degree, up to six (6) credits may be accepted with a B/C or C grade. Overall a 3.0 GPA must always be maintained, failure to do so will result in academic probation. Required courses can only be repeated once. If a student fails to earn a B or above in a required course after taking the required course twice, the student will be recommended for dismissal from the program. This policy applies even when the course is repeated at another institution. Helpful Tips Formatting Papers and Citing Research Material All of your instructors expect you to properly cite and document sources of information in your work. Different instructors will prefer different formatting styles. Plagiarism is not tolerated and can result in dismissal from the graduate program. Be sure you are familiar with what constitutes a violation. When in doubt, please ASK your instructor or research advisor. A detailed booklet is available that describes Michigan Tech’s academic integrity policy and procedures. Skills and Research Methodology Although the Data Science Graduate Program is course-based, there will be numerous opportunities to work with professors on current research projects. Take the initiative to engage with faculty in your area of interest. Volunteer to assist with research tasks outside of class, above and beyond class assignments. Learn the methodology being used by the researcher. Be aware that: 16 ● Statistics and quantitative skills are critical for data scientists. Not only should you be able to use a variety of statistical tools, but you also need to be able to understand the theoretical meaning and be adept at interpreting the results in productive and insightful ways. ● Core courses will require familiarity with a number of advanced computer skills. Invest time developing a solid understanding of a computer programming language such as Python, R and SAS. This will allow you to carry out more complex data analyses. ● Writing/communication skills are essential to a successful career. Michigan Tech provides assistance to improve your professional writing/communication. You should treat each and every writing/communication assignment as an opportunity to improve your communication skills. Academic Integrity Academic integrity and honesty are central components of a student's education, and ethical conduct fostered in an academic context will be carried into a student's professional career. Academic integrity is essential in a community of scholars searching and learning to search for truth. Anything less than total commitment to integrity undermines the efforts of the academic community. Both students and faculty are responsible for upholding the academic integrity of the University. For more information about policies related to Academic Integrity, please visit the Office of Academic and Community Conduct. 17 Mathematical Sciences MA 4330 Linear Algebra MA 4720 Design and Analysis of Experiments MA 5201 Combinatorial Algorithms MA 5221 Graph Theory MA 5627 Numerical Linear Algebra MA 5630 Numerical Optimization MA 5701 Statistical Methods MA 5741 Multivariate Statistical Methods MA 5750 Statistical Genetics MA 5761 Computational Statistics MA 5791 Categorical Data Analysis Mechanical Engineering - Engineering Mechanics MEEM 5010 Professional Engineering Communication Physics PH 4390 Computational Methods in Physics Social Sciences SS 5005 Introduction to Computational Social Science SS 5315 Population and Environment Applied Computing EET 4496 Applied Machine Learning SAT 5001 Introduction to Medical Informatics SAT 5141 Clinical Decision Support Modeling SAT 5151 Application Integration and Interoperability SAT 5241 Designing Security Systems SAT 5283 Information Governance and Risk Management SAT 5424 Population Health Management and Monitoring SAT 5761 Introduction to Hadoop and Applications SU 5010 Geospatial Concepts, Technologies, and Data Co-op UN 5000 Graduate Cooperative Education I 20 Appendix B: Degree Schedule - Masters in Data Science (A) Required Coursework - 12 credits Semester Course Number Title Credits Grade UN 5550 Introduction to Data Science 3 MA 5790 Predictive Modeling 3 CS 5831 Data Mining 3 BA 5200 Information Systems Management 3 (B) Elective Coursework - Minimum 6 credits Semester Course Number Title Credits Grade (C) Foundational Coursework - Maximum 6 Credits Semester Course Number Title Credits Grade (D) Domain Electives Semester Course Number Title Credits Grade 21