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1 - 9 of 9 results for: engr 245

CME 245: Topics in Mathematical and Computational Finance

Description: Current topics for enrolled students in the MCF program: This course is an introduction to computational, statistical, and optimizations methods and their application to financial markets. Class will consist of lectures and real-time problem solving. Topics: Python & R programming, interest rates, Black-Scholes model, financial time series, capital asset pricing model (CAPM), options, optimization methods, and machine learning algorithms. Appropriate for anyone with a technical and solid applied math background interested in honing skills in quantitative finance. Prerequisite: basic statistics and exposure to programming.Can be repeated up to three times.
Last offered: Summer 2017 | Repeatable for credit

CS 245: Principles of Data-Intensive Systems

Most important computer applications have to reliably manage and manipulate datasets. This course covers the architecture of modern data storage and processing systems, including relational databases, cluster computing frameworks, streaming systems and machine learning systems. Topics include storage management, query optimization, transactions, concurrency, fault recovery, and parallel processing, with a focus on the key design ideas shared across many types of data-intensive systems. Prerequisites: CS 145, 161.
Terms: Win | Units: 3

CS 345S: Data-intensive Systems for the Next 1000x

The last decade saw enormous shifts in the design of large-scale data-intensive systems due to the rise of Internet services, cloud computing, and Big Data processing. Where will we see the next 1000x increases in scale and data volume, and how should data-intensive systems accordingly evolve? This course will critically examine a range of trends, including the Internet of Things, drones, smart cities, and emerging hardware capabilities, through the lens of software systems research and design. Students will perform a comparative analysis by reading and discussing cutting-edge research while performing their own original research. Prerequisites: Strong background in software systems, especially databases ( CS 245) and distributed systems ( CS 244B), and/or machine learning ( CS 229). Undergraduates who have completed CS 245 are strongly encouraged to attend.
Last offered: Autumn 2016

CS 349D: Cloud Computing Technology

The largest change in the computer industry over the past five years has arguably been the emergence of cloud computing: organizations are increasingly moving their workloads to managed public clouds and using new, global-scale services that were simply not possible in private datacenters. However, both building and using cloud systems remains a black art with many difficult research challenges. This research seminar will cover industry and academic work on cloud computing and survey challenges including programming interfaces, cloud native applications, resource management, pricing, availability and reliability, privacy and security. Students will also propose and develop an original research project.n nPrerequisites: For graduate students, background in computer systems ( CS 240, 244, 244B or 245) is strongly recommended. Undergrads will need instructor's approval.
Last offered: Autumn 2018

DESINST 245: Redesigning Post-Disaster Finance

Unfortunately, natural disaster scenarios are becoming annual and severe due to climate change, urbanization and legacy building practices and standards. When disaster responders leave affected communities, banks, insurance companies and government agencies are challenged to fund the rebuilding.nnHow might we bring human-centered design to the post-disaster loan and insurance processes?nnIn this class, you will interview bankers, insurers and their bank regulators, borrowers, past disaster victims, emergency responders and others to visually map post-disaster process from multiple points-of-view, with the goal of revealing simpler and more adaptive design opportunities.Then you will work together to produce an immersive storytelling experience for all stakeholders to see how they might take a more human-centered approach to the post-disaster banking and insurance processes, where the stories of rebuilt community and household can be better told, shared and funded faster.nnAdmission by application. Find more info at dschool.stanford.edu/classes.
Terms: Spr | Units: 3-4

ENGR 245: The Lean LaunchPad: Getting Your Lean Startup Off the Ground

Apply the Lean Startup principles including the Business Model Canvas, Customer Development, and Agile Engineering to prototype, test, and iterate on your idea while discovering if you have a profitable business model. This is the class adopted by the National Science Foundation and National Institutes of Health as the Innovation Corps. Team applications required in December. Proposals can be software, hardware, or service of any kind. Projects are experiential and require incrementally building the product while talking to 10-15 customers/partners each week. See course website http://leanlaunchpad.stanford.edu/. Prerequisite: Interest in and passion for exploring whether your technology idea can become a real company. Limited enrollment.
Terms: Win | Units: 3-4

ME 245: From Maps to Meaning

One of the oldest visual tools created by humans to make sense of the complexities of our world, maps are unique in their ability to synthesize data, convey meaning through spatial logic, and deliver information at high resolution. They are also incredible tools for communication, data sorting and insight finding.n n This is an intensive, hands-on course that uses mapping techniques to navigate the intersection of data and design. Students will tackle three main projects and several shorter assignments over 10 weeks. Perfect attendance and completion of projects is absolutely mandatory. You will:n -collect, sort and organize quantitative and qualitative datan -create maps to synthesize complex informationn -use mapping as a tool to work on design problemsn -explore biases in map-makingn -create design interventions based on data and mapsn n While no specific prior experience is necessary, this class is for you if you are comfortable with the ambiguity of learning new skills on and off the computer, if you geek out about design and data, and if you are not intimidated by the idea of creating analog and digital maps. Admission by application. See dschool.stanford.edu/classes for more information.

MS&E 245A: Investment Science

Basic concepts of modern quantitative finance and investments. Focus is on the financial theory and empirical evidence that are useful for investment decisions. Topics: basic interest rates; evaluating investments: present value and internal rate of return; fixed-income markets: bonds, yield, duration, portfolio immunization; term structure of interest rates; measuring risk: volatility and value at risk; designing optimal portfolios; risk-return tradeoff: capital asset pricing model and extensions. No prior knowledge of finance is required. Concepts are applied in a stock market simulation with real data. Prerequisite: basic preparation in probability, statistics, and optimization.
Terms: Win | Units: 3-4

MS&E 245B: Advanced Investment Science

Formerly MS&E 342. Topics: forwards and futures contracts, continuous and discrete time models of stock price behavior, geometric Brownian motion, Ito's lemma, basic options theory, Black-Scholes equation, advanced options techniques, models and applications of stochastic interest rate processes, and optimal portfolio growth. Computational issues and general theory. Teams work on independent projects. Prerequisite: 245A.
Terms: Spr | Units: 3
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