public health
1. Demonstrate mastery of competencies in statistical concepts from performing calculations to interpreting results using the
following methods: simple and multiple linear regression; logistic and polynomial regression; odds ratios; relative risks;
hazard ratios; Bayesian analysis including the use of posterior probabilities and credible intervals to inform decision making;
Bayes factors; etc.
2. Understand and apply data confidentiality and governance issues to big data.
3. Apply standard data management techniques to extract, process, and clean data.
4. Demonstrate effective use of programming languages (e.g. R, S-plus, SAS, STATA) by both understanding and creating
algorithms for data analytics.
5. Translate a health-related question by practitioners into the appropriate data analytics question by identifying appropriate
statistical, computational, and analytical tools to solve the question.
6. Apply algorithms learned to existing large datasets in public health, beginning with large nationwide datasets such as the
Youth Risk Behavior Surveillance System (YRBSS) or the National Health and Nutrition Examination Survey (NHANES),
collected through the Centers for Disease Control and Prevention (CDC).
7. Describe more advanced algorithms to even larger 'big data' in public health, such as collecting multiple biometric data
measures using sensors at regular intervals throughout the day among thousands of participants over time (I don't know if
it's reasonable to expect students in an undergraduate minor to already be applying the more advanced algorithms realistically, I certainly don't see it happening initially - though perhaps later when the program is more established?).
8. Communicate analyses and methods to both technical audiences and public health practitioners who may have nontechnical backgrounds.
economics
1. display competence in organizing, manipulating and summarizing data
2. be able to use hypothesis testing and basic regression analysis of economic data and clearly and concisely explain the
theoretical models that underpin such analysis
3. be able to use statistical software to carry out such analysis (either STATA or R)
4. complete an empirical senior research project using data analysis
5. be well prepared for research assistance positions that require only a B.S.
6. be well prepared for Master's programs in economics, applied economics and other related areas
7. be well prepared for Ph.D. level econometrics
bsan
1. Analyze and elicit organizational requirements for managing data-intensive projects.
2. Understand and apply statistical and analytical techniques to solving a wide variety of business problems that involve the
use of data.
3. Integrate data analytics and databases with systems-based solutions to addressing organizational needs.
4. Understand and use a variety of predictive analytics techniques and business intelligence tools to enhance organizational
effectiveness.
5. Use and analyze social media and other emerging data types in support of organizational goals.
6. Develop research questions, quantify research goals, manipulate data in various formats, and measure and present
research outcomes in a business environment.
7. Use contemporary software tools to perform data preparation and to implement business research tasks.
8. Understand the social and ethical dimensions of data analytics.
computer science
1. understand core algorithms in data analytics and machine learning, such as: regression, clustering, classification, timeseries, bayes probabilistic reasoning, k-nearest neighbors, decision trees, neural networks, kernel methods such as support
vector machines, possibly non-negative matrix factorization
2. understand the order of complexity of these algorithms and how they scale for use with big data
3. understand more than just how to use a tool to solve a problem, for example how to train algorithms when using
supervised learning, how to prevent overfitting, anomaly detection to handle outliers and missing data
4. understand the basics of data mining
5. understand how to use both relational databases (e.g. MySQL) and NoSQL databases and when to use each
6. familiarity with Hadoop and MapReduce and how to distribute big data across servers
7. be able to use at least one cloud platform (e.g. Amazon, Microsoft, Rackspace, etc.) as well as create and work with
virtual machines both in cloud and on department servers
8. know some languages useful for data analytics (e.g. R, python, SAS, SPSS) and be able to competently program
algorithms learned
9. understand the importance of data privacy and security and how to secure data on servers or in the cloud
10. use the above methods, techniques, tools, languages, and platforms with actual data from on-line datasets or those
collected through the CBIA
11. utilize the above in senior research
12. be able to communicate algorithms, use of tools, and application of knowledge both to technical as well as less technical
audiences
mathematics
1. Able to get an entry-level job that requires using mathematical and statistical techniques to answer questions and solve
problems that involve the analysis of "large" data sets.
2. Able to enter a graduate program related to data analytics.
3. Thorough understanding of the statistical techniques used in data analytics.
3. Able to locate, extract, transform and load large data sets needed for doing analytical work.
4. Able to employ professional software tools used in data analytics, including those used for databases, data mining and
statistical analysis.
5. Able to effectively present and communicate to a range of audiences problems, their solutions, and conclusions related to
data analytics.
6. Able to program basic analytics algorithms, and script data retrieval.
7. Knowledgeable of the ethical issues involved in the use of data.
8. Knowledgeable of the role of assumptions involved in constructing mathematical models, and the limitations of
mathematical models.
chemistry
1. A chemistry or biochemistry major should have competency with data analysis.
2. understand general principles behind linear/non-linear regression analysis, application of correlation (time-series)
analysis/ waveform transforms, etc,
3. understand the purpose/ theory/limits of data smoothing
4. gain functional ability writing basic code using a language such as ( python, R, etc)
5. foundational knowledge of database mining
6. complete a senior research project that requires the application of skills acquired from the proposed minor .
Finance
1. Be able to develop research questions and appropriate analytical models (e.g. univariate analysis, ANOVA,
multivariate regression, etc.) to test theories based on the available data.
2, Be able to collect data and manipulate it for analysis with software such as Excel, SAS or SPSS.
3, Be able to summarize existing literature, theory and models and to develop methodologies using hypothesis
testing and regression analysis to examine the research question.
4. To understand and be able to test for the various problems associated with data that require adjusting the analytic
techniques used or manipulating the data (e.g. heteroscedasticity, endogeneity, multicollinearity, unit root, etc.)
5. To understand and apply statistical and analytical techniques appropriate for the data and its various
aforementioned quirks.
6. To understand and use a variety of predictive analytics and data mining techniques (e.g. principle components
analysis, factor analysis, stepwise regression, etc.)
7. Be able to present the data and results in a professional manner with tables, significance levels, etc. that support
the findings.
8. Be able to undertake senior-level empirical research projects demonstrating analytic and writing competencies
required for entry-level research positions or admission to graduate programs in finance or economics.
9. Knowledge of ethical issues related to data analysis.