BUS 5500 - Statistics and Data Analytics3 lecture hours 0 lab hours 3 credits
Course Description
This course reviews and applies the foundational concepts of statistics and analytics to common problems and scenarios found in the business domain. The focus on descriptive and diagnostic statistical techniques helps prepare students for more advanced study of prescriptive and predictive analytics. Topics include interval estimation, hypothesis testing, parametric and nonparametric tests, and an introduction to advanced analytics. The use of statistical programming languages such as R and Python will be emphasized to expose the student to contemporary statistical processing environments on computing clusters. (prereq: admission to graduate program)
Course Learning Outcomes
Upon successful completion of this course, the student will be able to:
- Apply fundamental concepts in the use and interpretation of statistical methods
- Prepare data for use in statistical and analytical processes
- Generate and interpret descriptive statistics related to business scenarios
- Use diagnostic analytics to inform solutions to business problems
- Select an appropriate statistical or analytic method to address a given business problem
- Communicate the meaning of statistical or analytic results to stakeholders
- Demonstrate the ability to analyze and write statistical program code in R and/or Python that automates the processing of statistical data sets that are not conducive to manual processing due to their size and/or scale
Prerequisites by Topic
Course Topics
- Introduction to statistics
- Populations, population samples, descriptive and inferential statistics
- Qualitative vs. quantitative data, variables
- Means, medians, percentiles, distributions, summations, arithmetic and geometric series, progressions
- Logarithms, linear and non-linear transforms
- Charting and graphing data
- Histograms, stem and leaf diagrams, frequency plots, box plots, bar charts, line graphs, dot plots, scatter diagrams, etc.
- Summarizing distributions
- Central tendency, mean, median, mode, etc.
- Measuring variability (sum of squared deviations, variance, standard deviation) and the Variance Sum Law
- Shapes of distributions (uniform, normal, binomial, etc.)
- Effects of linear transformations on variability
- Describing univariate and bivariate data
- Pearson’s correlation
- Variance Sum Law II
- Probability
- Basic concepts, independent probability, conditional probability, gambler’s fallacy
- Permutations and combinations
- Binomial distribution, multinomial distribution, Poisson distribution, hypergeometric distribution
- Bayes theorem
- Research and experimental design
- Scientific method, null hypothesis, measurement problems, sampling bias, causation vs. correlation
- Hypothesis testing, significance testing, one and two-tailed testing
- Steps in hypothesis testing, confidence intervals, common misconceptions
- Estimation
- Characteristics of estimators, degrees of freedom, bias and variability, confidence intervals for mean, T-distribution, correlation, and proportion
- Statistical Programming in Python
- Python environments, integrated development environments, and Jupyter Notebooks
- Python expressions, data types, operators, statement execution
- Python programming logic for branching, iteration, nesting and stacking logic structures
- Python data structures (lists, tuples, dictionaries, sets)
- Python functions and lambda expressions
- Python libraries and collections (arrays, nd-arrays, data frames, etc.)
- Object-oriented programming in Python
Coordinator
Dr. Jeff Blessing
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