Explore data science and analytics offerings at Ohio State

Students learn about information visualization techniques that help people analyze massive amounts of digital data to combat overload and aid sensemaking with applications in retail and financial decision making, logistics, information systems, manufacturing, healthcare, energy and smart grids, cybersecurity and social networks.

The place of the city in social organization; the emergence, nature, and problems of modern urbanism; projects based in census and field data.  

Advanced topics in management control, strategic interactions within the firm. Students may not repeat topics.

Note: 4893 is a temporary course number. The course will be assigned a permanent course number and “Data Science” title after spring 2018.

Level: Undergraduate

Offered by: Department of Political Science, College of Arts and Sciences

Taught by: Skyler Cranmer

A revolution in data science has changed the way business, government, and the academy learn and adjust to changing environments. Companies like Facebook, Amazon, Netflix, and Google; government agencies like the CIA, NSA, and Department of Defense; as well as election forecasters like FiveThirtyEight rely on advanced data science technology to understand the vast quantities of data being produced and filter fact from noise. This new data science is a combination of the social sciences, statistics, and machine learning, and this new class is meant to provide a first introduction to the topic (no experience or pre-requisites necessary) that will culminate in a “hackathon” data project. The course is software-application focused and will tour topics such as basic design, social network analysis, text as data, machine learning, and behavioral genetics.

Applications of modern marketing research procedures to variety of marketing problems. Emphasis placed on survey design and sampling, attitude measurement, perceptual mapping, market segmentation, conjoint analysis, and measurement of customer satisfaction.

Introduction to formulation, solution and analysis of continuous and discrete linear and nonlinear models to optimize systems using data-driven techniques.

Introduction to computer architecture at machine and assembly language level; pointers and addressing; C programming at machine level; computer organization.

Basic elements of syntactic description and an overview of syntactic structure across languages.

Survey of basic concepts and techniques in artificial intelligence, including problem solving, knowledge representation, and machine learning.

Statistical models for data analysis and discovery in big-data settings. The regression methods developed in Stat 3301 are extended to data settings with binary and multi-category outcomes. An introduction to some of the most commonly used statistical methods for exploring and analyzing multivariate data is provided. Interpretation and communication of the results of analyses is emphasized.

Statistical models for data analysis and discovery in big-data settings, with primary focus on linear regression models. The challenges of building meaningful models from vast data are explored, and emphasis is placed on model building and the use of numerical and graphical diagnostics for assessing model fit. Interpretation and communication of the results of analyses is emphasized.

Introduction to construction materials and techniques.

Fundamental modeling and simulation techniques in GIS, including cellular automata, diffusion models, and agent-based models, and their applications in social, environmental, and natural resources research.

An introduction to spatial data analysis in geography: the fundamental statistical and spatial analysis methods used in quantitative geographic research.

Data representation using hashing, search trees, and linked data structures; algorithms for sorting; using trees for language processing; component interface design

Intellectual foundations of software engineering; design-by-contract principles; mathematical modeling of software functionality; component-based software from client perspective; layered data representation.

This course is an introduction to analyzing social media data. Students will learn how to operate an established analytics platform and complete a novel research project using real world social media data. Students will learn how to tackle industry and academic research problems using social media logs and metrics.

Six Sigma Projects class.

Designed to familiarize students with the Six Sigma process improvement methodology and to provide them an opportunity to practice using Six Sigma Black Belt tools.

In this seminar, we will study both the theoretical and substantive development of the field of social networks as well as methodological tools necessary for conducting research. We will explore how our relationships significantly shape many aspects of our lives including cognition, motivations, identity, social mobility, group organization and resource distribution.

Introduction to sociological research techniques, methodological approaches, and relevant quantitative procedures.

An overview of issues, methods, and techniques of scientific psychological research. Students must be pursuing majors or minors in Psychology.

Level: Graduate

Offered by: Department of Anthropology, College of Arts & Sciences

Taught by: Mark Moritz

This course on research design and ethnographic methods is highly recommended for students who are considering doing social science research. Students will learn how to design a study and get hands-on training in a wide range of different research methods. In a collaborative research project we will design a study, collect data, analyze data, and write up the results. Students will also learn how to write a competitive grant proposal.

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Students learn to study anthropological problems through hands on experience with ethnographic methods, critical discussion of issues in ethnographic research and design of an ethnographic study.

Level: Undergraduate and Graduate

Offered by: College of Public Health

Taught by: Ayaz Hyder

In this course, students will gain a very general introduction to data analytics concepts and methods in the context of current and emerging public health issues. Students will be exposed to foundational concepts in data analytics that are relevant to the design, analysis, and interpretation of data-driven decision-making in public health and healthcare settings. After taking this course, students can opt to take PUBHLTH 7015 – Public Health Data Analytics II, which will allow guided application of methods.

An overview of theoretical and practical aspects of the assessment and prediction of human behavior; topics include achievement, intelligence, personality, attitudes, interests, and interpersonal relations.

Introduction to economic theory: supply and demand for goods, services, and factor inputs; market structure; international trade, the distribution of income. First required course for students planning to take 4000-level courses in Econ.

Presents the concepts, framework and tools of market-based management to deliver superior customer satisfaction, value and profitability. The focus of the course is on identifying, creating, delivering and measuring value creation.

Introduction to the theory of national income determination; economic fluctuations; money; government policy; international economics.

Introduction to phonological analysis and description, and an overview of phonological structure across languages.

Cross-linguistic survey of the sounds of the world’s languages.

Introduction to computer architecture and organization at machine and assembly level; pointers and addressing using C programming; introduction to operating system concepts: process, memory management, file system and storage, and multi-threaded programming.

Numerical methods for scientific computation: computer arithmetic, rounding errors, machine precision, machine representation, root-finding, interpolation, integration, linear systems, splines, smoothing, curve-fitting, linear programming.

Security threats and services, elements of cryptography, protocols for security services, network and internet security, advanced security issues and technologies.

Course examines the role of marketing research in the formulation and solution of marketing problems. Emphasis is placed on problem formulation, research design, data collection methods (instruments, sampling, operations) and analysis techniques.

Analysis of advanced capital budgeting problems through understanding the theories and applications of capital structure, leasing and real options. Theories and applications in corporate control, corporate governance and mergers and acquisitions.

Introduction to the power of maps, covering spatial representation, visual literacy, and geographic information technology in a global society.  

Introduction to basic concepts of machine learning and statistical pattern recognition; techniques for classification, clustering and data representation and their theoretical analysis.

Focus on the operational use of logistics software technologies such as transportation management systems, warehouse management systems, and inventory management systems. Course taught in a lab environment with tools for lectures, exercises and HW.

Concepts and methods used to plan and manage logistics activities in a business environment. Understanding of the components of logistics management and tradeoffs required to manage the integrated flow of goods through the supply chain.

This case-based course will prepare students to make data-driven decisions on key logistics issues and then make these decisions actionable through a focus on appropriate solution methods needed to achieve firm financial goals.  

Will equip student with the tools and skills necessary to recognize, analyze and solve significant problems in the operation of a logistics system through the application of techniques using spreadsheet analysis, optimization and simulation.

Introduction to linguistic meaning across languages, including word meaning, the contribution of syntactic structure, and the role of context in interpretation.

Matrix algebra, vector spaces and linear maps, bases and dimension, eigenvalues and eigenvectors, applications.

Level: Graduate

Offered by: College of Public Health

Taught by: Macarius Donneyong

The primary objective of this course is to introduce students to the practical issues in obtaining and analyzing healthcare databases and to become familiar with the basic principles of observational study designs regarding the use of these databases to study health outcomes and health policy.

The course provides an introduction to the principles of statistical learning and standard learning techniques for regression, classification, clustering, dimensionality reduction, and feature extraction.

Foundational inferential methods for learning about populations from samples, including point and interval estimation, and the formulation and testing of hypotheses. Statistical theory is introduced to justify the approaches. The course emphasizes challenges that arise when applying classical ideas to big data, partially through the use of computational and simulation techniques.

Calculus-based introduction to statistical data analysis. Includes sampling, experimental design, probability, binomial and normal distributions, sampling distributions, inference, regression, ANOVA, two-way tables.

Principles and methods for visualizing data from measurements and calculations in physical and life sciences, and transactional and social disciplines; information visualization; scientific visualization.

A survey of biomedical informatics theories and methods employed in the design, implementation and management of clinical and translational research programs. Recommended experience with computer science, statistics, anatomy, medical terminology, and molecular biology.

An introduction to probability and its role in statistical methods for data analytics. Equal emphasis is placed on analytical and simulation-based methods for quantifying uncertainty. Approaches to assessing the accuracy of simulation methods are discussed. Students should have some prior knowledge of basic programming. Applications of probability and sampling to big-data settings are discussed.

Parallel programming models; sequential and parallel performance issues; high-performance computer architecture; design, analysis, implementation and performance evaluation of parallel algorithms.

Introduction to operations and supply chain management to improve manufacturing and service organizations; analyzing, controlling and improving resources and processes to increase productivity, generate value-added output and meet business goals.

Prereq: Econ 2001.01 (200), 2002.01 (201); and Stat 1430 (133) or equivalent.

Survey of fundamental methods and techniques of neural networks; single- and multi-layer perceptrons; radial-basis function networks; support vector machines; recurrent networks; supervised and unsupervised learning.

Introduction to Network Science; Global and Local Network Measures; PageRank; Community Discovery  Algorithms; Network Models; Understanding the role of network analysis in Web and Social network applications

Teach students from different backgrounds the role of imaging in biomedical research, decision support and personalized medicine. Recommended familiarity with a programming language and operating system.

Introduction to experimental design, including selection and layout of plots, data analysis software, and data interpretation.

Database systems use, logical design, entity-relationship model, normalization, query languages and SQL, relational algebra and calculus, object relational databases, XML, active databases; database design project.

Knowledge discovery, data mining, data preprocessing, data transformations; clustering, classification, frequent pattern mining, anomaly detection, graph and network analysis; applications.

Exploration of current interdisciplinary best business practices in industry. Students will work across business specializations to learn strategy and implementation details from current industry professionals.

A survey of biomedical informatics theories and methods employed in the design, implementation and management of information systems supporting basic science, clinical and translational research, clinical care, and public health. Recommended course work in computer science, statistics, anatomy, physiology, and medical terminology.

Prereq: Not open to students with credit for 710.

Introduces students to basic topics of bioinformatics including sequence analyses, proteomics, microarrays, regulatory networks, sequence and protein databases. Recommended background in molecular biology and computer science.

The design of accounting information systems; focus is on business processes and internal control.

Overview of molecular and cellular biology of single cells, tissues, organisms and their interactions, including genetics and biochemistry of single cells.

Team-based projects: solve information security problems (mobile/static host/network hardening, intrusion detection and vulnerability scanning, forensics); results communicated through report writing and presentation.

Introduction to security of digital information; threats and attacks; regulations; risk management; attack detection and response; cryptography; forensics; technical training and certifications.

Health Analytics is the science of analyzing health data for knowledge discovery and decision making. The sheer diversity of data types in health care settings results in what scholars call a DRIP environment: Data Rich-Information Poor. Data has become ubiquitous in healthcare settings from clinical decision making to operational/business planning; health decisions are now being made similarly. No programming experience is required, but students are expected to vigorously engage in their own learning and parts of courses will require that students use and adapt to tools demonstrated during class.

The theory and algorithms underpinning today’s GIS technology and the development of custom GIS applications using the Python programming language.

Examination of issues, techniques and applications of analytic cartography, interactive maps and scientific visualization for exploring geographic data.

Focuses on designing, implementing, querying and managing spatial databases or persistent data stores where most entities have footprints in geographic space and time. This is critical for designing and implementing GIS for projects and organizations. It is also crucial for moving beyond GIS to the bigger world of geographic information services.

Introduction to the fundamental principles, methods, and geographic applications of remote sensing.

First course for science majors, covering dimensional analysis, atomic structure, the mole, stoichiometry, chemical reactions, thermochemistry, electron configuration, bonding, molecular structure, gases, liquids, and solids.

Prereq: One unit of high school chemistry, and Math Placement Level L or M; or a grade of C- or above in Math 1130 (130), 1131 (131), 1148 (148), 1150 (150), or above. Not open to students with credit for 1220 (123), 1620 (162), 1920H (202H), 1250 (125), 1610, or 1910H (202H). This course is available for EM credit. GE nat sci phys course. NS Admis Cond course.

Basic principles of geographic and land information systems and their use in spatial analysis and information management.

Fundamentals of natural language processing, automatic speech recognition and speech synthesis; lab projects concentrating on building systems to process written and/or spoken language.

Presents the concepts, framework and tools of market-based management. Topics include the marketing concept and the role that product, price, promotion and distribution play in marketing strategy and implementation.

Development of language skills using Italian media sources such as soap operas, internet resources, films, newspapers/magazines, etc. Not open to native speakers of Italian.

Management of financial risk inherent with international business; problems of implementing corporate finance principles; International Capital Markets, for non-Business majors.

Develops skills related to Time Value of Money, Risk and Return with applications in business and personal finance.

Prereq: AcctMIS 2000 (310), Math 1130 (130) or equiv, Econ 2001.01 (200), and CSE 100 or equiv.

A survey of accounting for non-business students; financial statements and analysis; accounting for assets, liabilities and owners’ equity; and elements of managerial accounting.

Design/analysis of algorithms and data structures; divide-and-conquer; sorting and selection, search trees, hashing, graph algorithms, string matching; probabilistic analysis; randomized algorithms; NP-completeness.

Propositional and first-order logic; basic proof techniques; graphs, trees; analysis of algorithms; asymptotic analysis; recurrence relations.

How to use relevant accounting information for various applications in finance such as estimating stock and bond prices and free cash flows.

Introduction to economics as an experimental social science. Students participate in and study results of economic experiments dealing with markets, individual decision making, and a broad array of game theoretic economic models.

Introductory chemistry for non-science majors, including dimensional analysis, atomic structure, bonding, chemical reactions, states of matter, solutions, chemical equilibrium, acids and bases, along with topics in organic and biological chemistry.

Prereq: Eligibility to enroll in Math 1116 (116). Not open to students with credit for 1210 (121), 1250, 1610 (161), 1910H (201H), or 102. This course is available for EM credit. GE nat sci phys course. NS Admis Cond course.

Discrete choice models, panel data, endogeneity, instrumental variables, and systems of equations; stationary and nonstationary time series; applications in various fields of economics. Continuation of 5410.

Practice-oriented development, design, implementation and evaluation of spatial databases, with an emphasis on local problems.

Hands-on experience in the execution of industry business practices to fill a need articulated by an operating public or private organization.

Study of computer-based systems designed to support decision-making within the organization.

Introduction to the tools of data analysis in political science, with an emphasis on data visualization.

Covers basic techniques for analyzing data in political science.

Designed to equip students with competencies in translating real-world problems into forms that such technologies can assist with, to portray/visualize these translations in ways that enhance the understanding of the dynamics of these problems, to structure mechanisms that derive suggested solutions, to clearly convey the justification and practicality of final solutions to others. 

Basic concepts of probability and statistics applied to the interpretation of quantitative data.

Examines the theories of methods used to identify profitable customers, understand their needs and wants, and how to build a bond with them by developing customer-centric products and services directed toward providing customer value.

Provides students with an understanding of how to convey complex information visually using software tools so as to facilitate, frame and inform debates around issues in public policy and public finance.  

Computer vision algorithms for use in human-computer interactive systems; image formation, image features, segmentation, shape analysis, object tracking, motion calculation, and applications.

Computer networks, communication protocols, Internet TCP/IP and applications, wireless communications and network security.

Computational models of language acquisition and application of machine learning techniques to language processing.

Symbolic and probabilistic computation applied to the structure of words and sentences, models of syntax, parsing algorithms.

Overview of major empirical methods in communication research.

Overview of major empirical methods in communication industry research.

A study of the cartographic techniques of map compilation and design including generalization, symbolization, reproduction, and GIS-based mapping with an emphasis on thematic mapping.

Introductory finance class which allows students develop the skills to understand how financial managers make value-maximizing decisions for their firms.

Examination of the use of business analytic models used in managerial decision making processes. Emphasis on formulation and interpretation of models; supported by spreadsheet based software.

Exploration of biology and biological principles; evolution and speciation, diversity in structure, function, behavior, and ecology among prokaryotes and eukaryotes. A broad introduction to biology comprises both Biology 1113 and 1114.

Prereq: Math 1130 (130), 1148, or 1150 or above, or Math Placement Level L or M.

Exploration of biology and biological principles; evolution and the origin of life, cellular structure and function, bioenergetics, and genetics. A broad introduction to biology comprises both Biology 1113 and 1114.

Introduction to concepts and methods for making decisions in the presence of uncertainty. Topics include: formulation of decision problems and quantification of their components; learning about unknown features of a decision problem based on data via Bayesian analysis; characterizing and finding optimal decisions. Techniques and computational methods for practical implementation are presented.

Course gives students experience analyzing language in social media. It covers theoretical issues arising in digital communication and provides hands-on practice at computational data analysis, applicable across fields. Students gain an understanding of the sociolinguistic dynamics of online communication and the technical skills to conduct research on them. Team-taught w/ faculty in Linguistics. 

Course gives students experience analyzing language in social media. It covers theoretical issues arising in digital communication and provides hands-on practice at computational data analysis, applicable across fields. Students gain an understanding of the sociolinguistic dynamics of online communication and the technical skills to conduct research on them. Team-taught w/ faculty in English. 

Application of quantitative methods to geographic problems; spatial statistics, area sampling, maps of residuals, regionalization methods, and simulation maps.

Applications of modern marketing research procedures to variety of marketing problems. Emphasis placed on survey design and sampling, attitude measurement, perceptual mapping, market segmentation, conjoint analysis, and measurement of customer satisfaction.

Level: Graduate

Offered by: Department of Economics, College of Arts and Sciences

Taught by: Mehmet Caner

This is a graduate-level course on high-dimensional econometrics, aka big data. This means we analyze two cases, where the regressors are growing with sample size. First, when the regressors are less than the sample size, where the main emphasis will be in asymptotics and selection consistency proofs. We will see a benchmark estimator: adaptive lasso. The second case is when the number of regressors are larger than sample size; there we focus on finite sample issues (oracle inequalities) and then we talk about building confidence intervals in high dimensional cases. In certain weeks, we will work on R programs that run high-dimensional regression. The important packages in R are lars and glmnet; we will learn how to implement them.

Transaction management; query processing and optimization; organization of database systems, advanced indexing, multi-dimensional data, similarity-based analysis, performance evaluation, new database applications.

An introduction to accounting and cost analysis for planning, decision-making, and control.