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Inga Maslova

  • Associate Professor of Clinical Data Sciences and Operations

Inga Maslova is an applied statistician specializing in time series analysis, machine learning, wavelet techniques, functional data analysis, big data, and applications to finance, business analytics, economics, hydrology, remote sensing, precision agriculture, and climate change problems. She has taught and developed courses in statistical learning, business statistics, data analytics and visualization. To her classroom she brings her own experience from consulting for Cisco, working with the World Bank on business cycle synchronization; analyzing drone data to help farmers make better crop management decisions at Utah Water Research Lab; using statistical learning models to forecast commodity prices. In 2018 she received a Golden Apple Teaching Award for teaching excellence.Before joining USC, Inga was a faculty at American University, Washington DC.She received her BS (Summa Cum Laude) in Mathematical Statistics from Vilnius University, Lithuania; MS and PhD in Statistics from Utah State University.

Inga Maslova
Press Inquiries: COMMUNICATIONS@MARSHALL.USC.EDU

Departments

Data Sciences + Operations

RESEARCH + PUBLICATIONS

COURSES

Spring 2024
  • BUAD-425
    Introduction to Business Analytics
    Leveraging large corporate datasets; slice and dice data; dash boards; data mining and statistical tools; neural network; multiple and logistic regression; decision tree; gain inference and decision making; clustering.
  • DSO-424
    Business Forecasting
    A variety of forecasting techniques used by a variety of businesses. Emphasis on learning to apply these techniques to real data.
  • DSO-590
    Directed Research
    Research leading to the master's degree. Maximum units which may be applied to the degree to be determined by the department. Graded CR/NC. Open only to master's students. Duplicates credit in former IOM-590.
Fall 2023
  • GSBA-545
    Data Driven Decision Making
    Data analysis technologies for business decision making; principles and techniques of statistical inference for business problem solving; foundations of data-driven regression and time series analytics. Duplicates credit in GSBA 506ab and GSBA 524.
  • DSO-522
    Applied Time Series Analysis for Forecasting
    Survey of forecasting and time series methods. Models for stationary and nonstationary time series; ARIMA model identification, estimation, and forecast development. Seasonal and dynamic models. Web registration open only to graduate business and accounting students. Duplicates credit in former IOM 522. Recommended preparation: (GSBA 506a and GSBA 506b) or GSBA 524 or (GSBA 516 and GSBA 545).