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Emmanuel Sitnikov
Emmanuel Sitnikov

Statistics And Analysis Of Scientific Data (Gra...


Our one-year MPS program in Applied Statistics readies students for careers in fields where modern data analysis skills are highly coveted. Built on the three curriculum components of core courses, an in-depth statistical analysis project, and elective coursework, our program offers students the option to focus their graduate education in one of two areas: techniques of applied statistical analysis or data science, which includes high performance computing, databases and scripting.




Statistics and Analysis of Scientific Data (Gra...


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For more than twenty years, our Master of Professional Studies (MPS) program has offered world-class training in applied statistics and preparation for the 21st-century workplace. The MPS provides a solid foundation in theoretical statistics, a broad education in many applied statistics topics, certification in SAS and a semester-long, real-world data analysis project. Our graduates are employed all over the world, in industries ranging from finance to survey analysis to biomedical research.


The Master of Professional Studies (MPS) in Applied Statistics is for those who are interested in professional careers in business, industry, government or scientific research. Our MPS program provides rigorous training in modern data analytical skills that are sought after in almost any field. Currently, Cornell is the only Ivy League University that provides such a program. This program is recognized as a Professional Science Master's (PSM) program, approved by the Council of Graduate Schools.


CTSI Biostatistics, Epidemiology and Research Design Center offers one-stop service for researchers who need study design, biostatistical and data management expertise. We provide Penn State researchers with a full range of biostatistical expertise and service.


The Center serves as a crossroads where researchers at the interfaces between statistics, data analysis, astronomy, space and observational physics collaborate, develop and share methodologies, and together prepare the next generation of researchers.


Figures in scientific publications are critically important because they often show the data supporting key findings. Our systematic review of research articles published in top physiology journals (n = 703) suggests that, as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies. Papers rarely included scatterplots, box plots, and histograms that allow readers to critically evaluate continuous data. Most papers presented continuous data in bar and line graphs. This is problematic, as many different data distributions can lead to the same bar or line graph. The full data may suggest different conclusions from the summary statistics. We recommend training investigators in data presentation, encouraging a more complete presentation of data, and changing journal editorial policies. Investigators can quickly make univariate scatterplots for small sample size studies using our Excel templates.


We conducted a systematic review of standard practices for data presentation in scientific papers, contrasting the use of bar graphs versus figures that provide detailed information about the distribution of the data (scatterplots, box plots, and histograms). We focused on physiology because physiologists perform a wide range of studies, including human studies, animal studies, and in vitro laboratory experiments. We systematically reviewed all full-length, original research articles published in the top 25% of physiology journals between January 1 and March 31, 2014 (n = 703) to assess the types of figures that were used to present continuous outcome data (S1 Fig and Table A in S1 Text). We also abstracted information on sample size and statistical analysis procedures, as these factors may influence figure selection. Detailed methods and results are presented in the data supplement. Based on our findings, we recommend major changes to standard practices for presenting continuous data in small sample size studies. We hope that these recommendations will promote scientific discourse by giving readers the information needed to fully examine published data.


Statistical analysis means investigating trends, patterns, and relationships using quantitative data. It is an important research tool used by scientists, governments, businesses, and other organizations.


After collecting data from your sample, you can organize and summarize the data using descriptive statistics. Then, you can use inferential statistics to formally test hypotheses and make estimates about the population. Finally, you can interpret and generalize your findings.


Identifying the measurement level is important for choosing appropriate statistics and hypothesis tests. For example, you can calculate a mean score with quantitative data, but not with categorical data.


Using your table, you should check whether the units of the descriptive statistics are comparable for pretest and posttest scores. For example, are the variance levels similar across the groups? Are there any extreme values? If there are, you may need to identify and remove extreme outliers in your data set or transform your data before performing a statistical test.


From this table, we can see that the mean score increased after the meditation exercise, and the variances of the two scores are comparable. Next, we can perform a statistical test to find out if this improvement in test scores is statistically significant in the population.Example: Descriptive statistics (correlational study)After collecting data from 653 students, you tabulate descriptive statistics for annual parental income and GPA.


The Master of Science Concentration in Analytics combines the mathematical and statistical training of the traditional MS in Statistics with enhanced computational and data analytic training for those planning careers in information intensive industries or research. The program includes fundamental training in mathematical and applied statistics as well as specialized training in data management, analysis, and model building with large datasets and databases. The specialized courses have an emphasis on statistical computing, data management, and statistical learning, which encompasses the more statistical topics that fall under the broader title of data mining. Students are encouraged to gain experience in a business or consulting environment as part of the program.


The choice of STAT 425 or STAT 527 provides thorough coverage of linear regression and data analysis that is fundamental for further study in analytics. STAT 527 is the more advanced course required for PhD students. The second course is a selection of one of several traditional courses in foundational areas of statistics.


STAT 542 is an advanced course in statistical learning that covers stat-of-the-art and proven methods for classification, clustering, model selection, and predictive modeling in the context of large data sets. The second advanced analytics course is a choice of advanced statistical computing theory, multivariate analysis, data mining, and machine learning courses.


Practical and transferable skills are developed by the provision of opportunities for hands-on experience through regular workshops and projects. Data analysis demonstrations and exercises are an essential component of the core modules and much of the tuition for statistical computing takes place in computer workshops, which will allow you to learn through active participation. Additional workshops running during the teaching terms provide preparation for the summer research project and cover the communication of statistics, for example, the presentation of statistical graphs and tables. Project supervisors will provide guidance on how to manage an extended task effectively and you are encouraged to monitor your own working practice using a self-assessment questionnaire, as well as to monitor your own progress by self-marking of non-assessed coursework.


Introduction to mathematical concepts and methods essential for multivariate statistical analysis. Topics include basic matrix algebra, eigenvalues and eigenvector, quadratic forms, vector and matrix differentiation, unconstrained optimization, constrained optimization, and applications in multivariate statistical analysis. Prerequisite: Graduate standing and one course in statistics.


Concepts of probability and mathematical statistics with applications in data analysis and research. May be repeated for credit when the topics vary. Prerequisite: Graduate standing, and Statistics and Data Sciences 382, Mathematics 362K and 378K, or consent of instructor.


Theories, models and methods for the analysis of quantitative data. With consent of the graduate advisor, may be repeated for credit when the topics vary. Prerequisite: Graduate standing, and Statistics and Data Sciences 380 or 382 or consent of instructor.


An introduction to statistical learning methods, exploring both the computational and statistical aspects of data analysis. Topics include numerical linear algebra, convex optimization techniques, basics of stochastic simulation, nonparametric methods, kernel methods, graphical models, decision tress and data re-sampling. Prerequisites: Graduate standing.


Focuses on various mathematical and statistical aspects of data mining. Topics covered include supervised learning (regression, classification, support vector machines) and unsupervised learning (clustering, principal components analysis, dimensionality reduction). The technical tools used in the course draw from linear algebra, multivariate statistics and optimization. Prerequisites: Graduate standing and Mathematics 341 or equivalent.


Introduction to programming using both the C and Fortran (95/2003) languages, with applications to basic scientific problems. Covers common data types and structures, control structures, algorithms, performance measurement, and interoperability. Statistics and Data Sciences 322 and 392 may not both be counted. Prerequisite: Graduate standing and credit or registration for Mathematics 408C or 408K. 041b061a72


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