This course provides a hands-on introduction to widely-used tools for data science. Topics include Linux; languages and packages for statistical analysis and visualization; cluster and parallel computing including GPUs; Hadoop and Spark; libraries for machine learning; NoSQL databases; and cloud services.

PREREQUISITES: CSC 161, CSC 171 or some equivalent programming experience strongly recommended.

Location

Wegmans Room 1400 (MW 9:00AM - 10:15AM)

The goal of this course is to learn how to model, analyze and simulate stochastic systems, found at the core of a number of disciplines in engineering, for example communication systems, stock options pricing and machine learning. This course is divided into five thematic blocks: Introduction, Probability review, Markov chains, Continuous-time Markov chains, and Gaussian, Markov and stationary random processes. Prerequisites: ECE 242 or equivalent

Location

Gavett Hall Room 202 (MW 4:50PM - 6:05PM)

Fundamental concepts and techniques of data mining, including data attributes, data visualization, data pre-processing, mining frequent patterns, association and correlation, classification methods, and cluster analysis. Advanced topics include outlier detection, stream mining, and social media data mining. CSC 440, a graduate-level course, requires additional readings and a course project.

Location

Gavett Hall Room 202 (TR 3:25PM - 4:40PM)

This course presents the fundamental concepts of database design and use. It provides a study of data models, data description languages, and query facilities including relational algebra and SQL, data normalization, transactions and their properties, physical data organization and indexing, security issues and object databases. It also looks at the new trends in databases. The knowledge of the above topics will be applied in the design and implementation of a database application using a target database management system as part of a semester-long group project.

Location

Harkness Room 115 (MW 12:30PM - 1:45PM)

This course will cover foundational concepts in descriptive analyses, probability, and statistical inference. 

Topics to be covered include data exploration through descriptive statistics (with a heavy emphasis on using R for such analyses), elementary probability, diagnostic testing, combinatorics, random variables,  elementary distribution theory, statistical inference, and statistical modeling. The inference portion of the course will focus on building and applying hypothesis tests and confidence intervals for population means, proportions, variances, and correlations. Non-parametric alternatives will also be introduced. The modeling portion of the course will include ANOVA, and simple and multiple regression and their respective computational methods. Students will be introduced to the R statistical computing environment. 

PREREQUISITES: MTH 150 or MTH 150A; AND MTH 142 or MTH 161 or MTH 171

Location

Strong Auditorium Room 011 (TR 2:00PM - 3:15PM)

Description:  Time series analysis is a valuable data analysis technique in a variety of industrial (e.g., prognostics and health management), business (e.g., financial data analysis) and healthcare (e.g., disease progression modeling) applications. Moreover, forecasting in time series is an essential component of predictive analytics. The course will begin with an introduction to practical aspects relevant to time series data analysis such as data collection, characterization, and preprocessing. Topics covered will include smoothing methods (moving average, exponential smoothing), trend and seasonality in regression models, autocorrelation, AR and ARIMA models applied to time series data. Deep learning models including feedforward, recurrent, gated and convolutional architectures will also be studied. Students shall work on projects with time-series data sets using modeling tools in Python.

PREREQUISITES: Introductory Statistics (DSC 262/STT212/STT213 or equivalent), Linear Algebra and Differential equations (MTH 165 or equivalent), and applied Python programming (CSC161 or equivalent)

Location

Dewey Room 2162 (TR 11:05AM - 12:20PM)

The capstone/practicum provides an experience for data science majors/MS candidates to apply the core knowledge and skills attained during their program to a tangible data science focused project. Students will work in small teams on a project that applies data science methods to the analysis of a real-world problem. The instructor will guide each team in developing a topic that makes use of the knowledge the team members gained through their application area courses. The identified projects or problems and data sets will cover a range of application areas and reflect real-world needs from industry, medicine and government. Each student will be required to write a paper about their project, which satisfies one upper-level writing requirement for majors and Plan B for master's.

PREREQUISITES: DSC 240/440 (Data Mining) AND an introductory statistics course such as DSCC 262/462, STT212 or STT213 or equivalent. DSC 261/461 (Database Systems) strongly recommended prior but may be taken concurrently. FOR DSC GRADUATING SENIORS and MS CANDIDATES. Seniors should request eligibility.

Location

Gavett Hall Room 206 (MW 10:25AM - 11:40AM)

In this independent study, we will explore fundamental statistical models for dealing with astronomical data. This will involve reading seminal texts on astrostatistics, with a focus on Bayesian approaches to such methods, implementing data analyses in appropriate software (R, JAGS, Python), and determining possible areas for future research. 

Grading and assessment will be based on completion of readings, discussions of topics from readings, implementation of data analyses, and write-ups of such analyses.

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In this research course, we will apply AI/ML techniques to identify strong lensing systems in galaxy spectra measured with DESI. Strong lenses are foreground galaxies which amplify the light signal from much more distant background galaxies via General Relativistic gravitational lensing. They are a particularly valuable class of objects for cosmology, because they can be used to measure time-delayed signals from supernovae and other bright transients, which in turn provides a sensitive measurement of the expansion of the Universe. We will apply a multi scale convolutional neural network to attempt to classify strong lensing systems from the much larger background of ordinary galaxies.

Evaluation:
- Student meet 2x weekly with instructor to discuss progress in the analysis and relevant papers from the literature.
- Student will give a short (5-10 minute) presentation on progress every two weeks during group research meetings.
- At the end of the semester, the student will make a longer (~15 minute) overview of the analysis.
- Student will work with the instructor to write a technical document about the work and results.

Discussion of "Long Haul Syndrome" of COVID-19 on Twitter. The purpose of the project is to examine the longitudinal trend of the “long haul syndrome” of COVID-19 mentioned on Twitter from the start of the pandemic in the US and explore the major topics discussed about the “long haul syndrome”. We have streamed COVID-19 related tweets since the beginning of the pandemic in March 2020. Using the streamed COVID-19 data, the specific aims of the proposed project are as follows. Specific Aim 1: To examine the longitudinal trend of the “long haul syndrome” of COVID-19 mentioned on Twitter. Trend analysis will be conducted to examine the longitudinal variations of number of tweets mentioning the “long haul syndrome” of COVID-19 using related keywords. Specific Aim 2: To explore the major topics discussed about the “long haul syndrome” of COVID-19 on Twitter. Latent Dirichlet Allocation (LDA) model will be conducted on COVID-19 related Twitter dataset to extract the most frequently discussed topics about the “long haul syndrome” of COVID-19.

The student will be required to come to the PEAK Human Performance Clinical Research Lab to perform actigraphy data management and meet with the supervisor regularly. Generate actigraphy data report and conduct data review, trimming and auditing. Perform actigraphy data merging and troubleshoot and discrepancies in datasets. Keep a detailed programming log/codebook to document programming progress and codes. Finalize Insomnia Prediction Project by summarizing results, preparing tables and figures and contributing to manuscript writing.

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