The objective of this program is to prepare students to enter the workforce in the rapidly advancing field of data science, an interdisciplinary domain that cuts across computer science and statistics, by providing a solid, comprehensive background in the related topics of theory and their applications. Data science is often seen as a very applied topic, and students are trained on specific computational tools for domain-driven tasks. On the contrary, the goal of this program is to provide all skills necessary that will enable students to be flexible and competitive in today’s job market by gaining deep understanding of theory, implementation (e.g., algorithms and appropriate computing languages), as well as the inherent “nature” of different data modalities, such as classification and prediction challenges on specific data (e.g., sparse and/or incomplete data).
Data science is broadly defined as a cross-disciplinary field, on the border between Computer Science and Statistics, and involves data-driven knowledge discovery in terms of pattern analysis and prediction. Being an applied science field, data science includes not only the foundational topics from Statistics, such as regression and sequence analysis, Machine Learning, and Data Mining, but also related topics targeting the applied component of data analysis, such as database systems, information visualization, and “big data” analytics.
What completes data science training is the computational/algorithmic thinking, which primarily involves the implementation aspect of methodologies, namely the ability to translate the mathematical description of a method into computational terms, to design the appropriate algorithms, and to study/improve implementation performance in terms of accuracy and computational efficiency (time).
The applicant must be admitted as a graduate student without provisions and complete 30 semester-credit hours of study in CSCI and STAT courses numbered 500 or above
Core Course Requirement (9 credits in Computer Science, 6 credits in Statistics)
Computer Science - CSCI 59000, Introduction to Data Science; CSCI 57300, Data Mining; CSCI 57800, Statistical Machine Learning.
Statistics - STAT 51200, Applied Regression Analysis; STAT 52900, Applied Decision Theory and Bayesian Analysis.
Statistics - STAT 51400 Design of Experiments; STAT 52000 Time Series and Applications; STAT 52300 Categorical Data Analysis; STAT 52400, Applied Multivariate Analysis; STAT 52501, Generalized Linear Models; STAT 53600, Introduction to Survival Analysis.
Capstone - CSCI 69500, MS Capstone Project, or STAT 59800, Topics in Statistical Methods
Resident Study Requirements
The total number of hours of academic credit used to satisfy degree requirements consist of all course credit hours that paper on the plan of study, other graduate course credit hours with grades of C or better that paper on the IUPUI/Purdue transcript and research hours that appear on the IUPUI/Purdue transcript.
For a Master of Science in Computer Science at IUPUI, at least one-half of the total credit hours sued to satisfy degree requirements must be earned in residence n the IUPUI campus. Transfer credits used to satisfy degree must also appear on the plan of study. Course credits obtained via televised instruction from a campus shall be considered to have been obtained in residence on that campus. At least 30 total credits hors of 500 level or above courses are required. In fulfilling these requirements, a maximum of 15 credit hour will be allowed from any one semester (total 15 for summer I and II combined).
Credit earned for graduate study at other universities may be applied toward the Master of Science in Computer Science with approval of the Advisory Committee, the Graduate Committee and the Graduate School. Such credits may not have been used to meet other degree requirements.
Transfer credits are normally limited to six semester hours. Application for the transfer of credit is made when the plan of study is presented for approval. This should be done as soon as possible. Only credit hours associated with graduate course for which grades of a B or better were obtained will be eligible for transfer.
Overall Student Performance
The objective of this type of assessment is to determine whether or not a given student is satisfactorily progressing towards, and finally achieves, the performance objectives that the Graduate Faculty has set for the Program.
The instructor in each class will evaluate the progress of each student through the course and the final achievement by using the mechanisms and objectives stated in the course syllabus. These vary by course. The mechanisms are typically evaluations of exercises, written and oral examinations, and projects, collaboratively or individually executed. The general outcomes are that the student will understand the theoretical concepts and be proficient in applying them within the context of the course's subject.
The student must accumulate individual and cumulative performance ratings for all courses taken that satisfy the minimum acceptable standards the department establishes. The outcome here is that the graduate will have a uniformly high technical capability across a broad spectrum of subjects in computational data science.
Each student must demonstrate satisfactory accomplishment in a fundamental domain of knowledge, which the group of Core Courses provides. The outcome of this requirement is that the student will possess solid knowledge of the theoretical bases of computational data science.
Every student must achieve sufficiently deep command of a specialization area to successfully complete a thesis or project. The evaluations from the specialization courses combined with the evaluation by the student's thesis or project supervisors measure this. The outcome of the student's preparation for this will be that she or he will possess expertise in a specific research or application area for future use in the profession.
Finally, each student must make a written and public presentation of the thesis or project work, which the student's Examination Committee evaluates. This measures and sets a minimum standard on the student's capability to:
Integrate appropriately new knowledge with he knowledge and skills presented in the taken courses in ways sufficient to engage in research or the solution of problems arising in practice.
Communicate effectively, orally and in writing, with colleagues or teammates while solving problems and in presenting the solution.
Think analytically and critically and apply a variety of logical and computational tools as aids in this process.
Articulate the relationships between the area expertise and other discipline area and society in general.