Hello! I’m Britney Scott, an aspiring data scientist. I’m currently a Masters Candidate at the University of Illinois at Chicago pursuing an MS in Business Analytics. My background includes a BS in Statistics and a BS in Marketing.
I love programming in R, so I built this website using R Markdown and the R Blogdown package to demonstrate my interest and to display some of my qualifications and projects.
Please have a look around and feel free to contact me if you have any questions. I’m actively seeking full-time opportunities in data science and machine learning.
MS in Business Analytics, 2020
University of Illinois at Chicago
BS in Statistics, 2019
University of Illinois at Chicago
BS in Marketing, 2019
University of Illinois at Chicago
These are recent projects from some of my courses.
All course descriptions copied directly from the UIC catalog
IDS 400: Programming for Data Science in Business
Aims to provide students the knowledge and skills for designing and developing data science applications in various business areas, using a language such as Python. Focuses on programming constructs and use of functions and packages.
IDS 521: Advanced Database Management
Data analysis for database design; logical data modeling, transaction modeling; implementation models; physical database design; database tuning and performance evaluation; database decomposition; distributed database; database security.
IDS 558: Revenue Management
Uses mathematical models and analytics to solve for profit-maximizing business strategies for companies. Topics covered include price optimization, price differentiation, market segmentation, capacity allocation, and network management.
IDS 560: Analytics Strategy and Practice
Projects and case studies on how to apply analytic skills developed in the MS Business Analytics curriculum to practical problems. Analytics related issues in the context of organizational strategy.
IDS 564: Social Media and Network Analysis
Analytic approaches to help organizations utilize massive social media data for making informed business decisions; sentiment identification; social network analysis; customer behavior analysis, social advertising using machine learning methods.
IDS 566: Advanced Text Analytics for Business
Techniques for mining and analyses of textual information. Natural language processing and machine learning approaches for sentiment and opinion analyses, topics extraction, document clustering, and their application for business decisions.
IDS 567: Business Data Visualization
Introduction to principles of data visualization for business and the optimal presentation of analytics results.
IDS 572: Data Mining for Business
Machine learning, statistics in data mining for business insights. Prediction, classification, trees, random forests, boosting, clustering, regularization, SVM, recommender systems, neural nets, text mining.
IDS 575: Statistical Models and Methods for Business Analytics
Generalized Linear Models; Factor Analysis; Time Series Analysis; Maximum Likelihood and Expectation Maximization; Sampling; Optimization; Support Vector Machines and Structured Prediction.
IDS 576: Advanced Predictive Models and Applications for Business Analytics
Generalized linear models, hierarchical models, neural networks, support vector machines, Bayesian networks.
STAT 381: Applied Statistical Methods I
Graphical and tabular representation of data; Introduction to probability, random variables, sampling distributions, estimation, confidence intervals, and tests of hypotheses.
STAT 382: Statistical Methods and Computing
Statistical computation with the SAS and R software packages: data structure, entry, and manipulation; numerical and graphical summaries; basic statistical methods; select advanced methods.
STAT 401: Introduction to Probability
Probability spaces, random variables and their distributions, conditional distribution and stochastic independence, special distributions, sampling distributions, limit theorems.
STAT 411: Statistical Theory
Estimation, tests of statistical hypotheses, best tests, sufficient statistics, Rao-Cramer inequality, sequential probability ratio tests, the multivariate normal distribution, nonparametric methods.
STAT 461: Applied Probability Models I
Computing probabilities and expectations by conditioning, Markov chains, Chapman-Kolmogorov equations, branching processes, Poisson processes and exponential distribution, continuous-time Markov chains, reversibility, uniformization.
STAT 481: Applied Statistical Methods II
Linear regression, introduction to model building, analysis of variance, analysis of enumerative data, nonparametric statistics, product and system reliability, quality control.
MATH 310: Applied Linear Algebra
Matrices, row reduction algorithm, vector spaces, LU-decomposition, orthogonality, Gram-Schmidt process, determinants, inner products, eigenvalue problems, applications to differential equations and Markov processes.
ECON 300: Econometrics
Specification of economic models; measurement of variables; estimation of economic relationships and testing of economic hypotheses; ordinary least squares regression and extensions.
MKTG 468: Advanced Marketing Research
Advanced knowledge of critical concepts and tools in marketing research related to problem identification, data collection, and analysis in conventional and digital media.