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Britney Scott

Masters Candidate

University of Illinois at Chicago

Biography

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.

Interests

  • Statistics
  • Predictive Modeling
  • Stochastic Processes
  • Data Science & Programming
  • Economics
  • Risk Management
  • Databases

Education

  • 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

Skills

R

Predictive Analysis

Econometrics

Probability Theory

Python

Database Management/SQL

Tableau

Market Research

Machine Learning

Experience

 
 
 
 
 

Marketing Intern

Datalogics

May 2019 – Dec 2019 Chicago, IL
  • Researched new vertical markets for EdTech product line.
  • Implemented new marketing messaging to communicate tailored value to each vertical market through in-depth market research.
  • Designed ad campaign concepts for vertical and horizontal markets.
  • Optimized AdWords budget through ad group, bid strategy and key word recommendations.
  • Analyzed competitor SEO strategies.
  • Developed lead nurturing strategies for cold leads using past Pardot conversion data.
  • Collaborated with Product Management team to complete a comprehensive competitive analysis.
  • Expanded creative marketing ideas into content for Pardot drip campaigns.
 
 
 
 
 

Research Assistant III

University of Illinois at Chicago

Jun 2016 – May 2018 Chicago, IL
  • Managed Undergraduate Research Assistants.
  • Collected data from UIC students to improve the College’s Professional Development Program and to provide specific feedback to students about their leadership qualities.
  • Generated new data using Likert Scales based in research in the management and psychology fields.
  • Designed weekly and monthly reports encompassing all aspects of the organization.
  • Communicated clearly and directly with professors, deans and other faculty and staff in the College of Business Administration.
  • Improved, wrote and implemented new procedures, such as the use of new technology during data collection, and trained new and ongoing employees.
  • Spearheaded the development of a detailed training manual.
  • Assisted as needed with additional detail-oriented tasks.
 
 
 
 
 

Marketing Assistant

Swedish American Museum

Jan 2016 – Jan 2018 Chicago, IL
  • Generated consistent templates for the Museum’s printed marketing materials in Adobe InDesign.
  • Managed spreadsheets with Microsoft Excel, and used other Microsoft Office software as needed.
  • Improved the Museum’s marketing strategy by utilizing online marketing platforms.
  • Increased traffic on the Museum’s social media sites through online marketing.
  • Worked closely with Museum staff to meet the marketing needs of specific events and exhibits.
  • Researched and wrote communications to potential Museum sponsors.
  • Served on the organization’s Marketing Committee, participating in projects such as a website revamp and the planning of annual fundraisers.

Recent Projects

These are recent projects from some of my courses.

IDS 572: Clustering Project

Comparing K-Means, K-Medoids, and Hierarchical clustering on a business dataset.

Stat 382: Final Project

The final project for my undergraduate R programming class.

Stat 481: Project 2

An analysis of variance, a Bonferroni test for differences in means, and a random effects model to test for significant variability.

Stat 481: Project 1

A regression analysis featuring a data transformation and step-wise regression.

Coursework

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.

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