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Bioinformatics and Data Analysis
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Courseware
Introduction to seismic theory, measurements and processing of seismic data to final focussed image for geological and/or physical interpretation.This course deals with the most important aspects of reflection seismics. Theory of seismic waves, aspects of data acquisition (seismic sources, receivers and recorders), and of data processing (CMP processing, velocity analysis, stacking, migration) will be dealt with. The course will be supplemented by a practical of 6 afternoons where the students will see the most important data-processing steps via exercises (in Matlab).
- Subjects:
- Land Surveying and Geo-Informatics and Disaster Control and Management
- Keywords:
- Seismic prospecting Seismometry Earthquakes Seismic reflection method
- Resource Type:
- Courseware
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MOOC
This course teaches the R programming language in the context of statistical data and statistical analysis in the life sciences. We will learn the basics of statistical inference in order to understand and compute p-values and confidence intervals, all while analyzing data with R code. We provide R programming examples in a way that will help make the connection between concepts and implementation. Problem sets requiring R programming will be used to test understanding and ability to implement basic data analyses. We will use visualization techniques to explore new data sets and determine the most appropriate approach. We will describe robust statistical techniques as alternatives when data do not fit assumptions required by the standard approaches. By using R scripts to analyze data, you will learn the basics of conducting reproducible research. Given the diversity in educational background of our students we have divided the course materials into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. We start with simple calculations and descriptive statistics. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.
- Subjects:
- Mathematics and Statistics
- Keywords:
- Life sciences -- Statistical methods Mathematical statistics -- Data processing R (Computer program language)
- Resource Type:
- MOOC
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MOOC
In the past few decades, China's cities have experienced a period of rapid development. Great changes have taken place in both urban space and urban life. With the booming of information and communications technology (ICT), ‘Big data’ such as mobile phone signaling, public transportation smart card records and ‘open data’ from commercial websites and government websites jointly promote the formation of the ‘new data environment’, thus providing a novel perspective for a better understanding of what changes have happened or are happening in China’s cities. This course combines both the new data generated for urban analysis and its research applications. The content ranges from big data acquisition, analysis, visualization and applications in the context of China’s urbanization and its city planning, to urban modeling methods and typical models, as well as the emerging trend and potential revolution of big data in urban planning. We have categorized the overall content of this online course into five sections, namely, overview, data, data processing, application, and perspective. The section of overview introduces cities in transition and describe the changing of urban space and urban life in China. The second section lists some commonly used open data and big data in the ‘new data environment’. Then, methods for data acquisition, cleaning and analysis are illustrated in data processing section. To better explain the data analysis method, the fourth part introduces several Chinese research cases to illustrate the application of these methods in urban research. Last but not least, the last section is the most future-oriented one, which is composed of some methodologies and proposals such as Data Augmented Design (DAD) and Big Model. This course, which shares experiences on big data analysis and its research application, will suit those concerning contemporary urbanizing China and its urban planning in the context of information and communication technologies.
- Subjects:
- Building Services Engineering and Building and Real Estate
- Keywords:
- China Cities towns -- Data processing City planning Big data
- Resource Type:
- MOOC
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Courseware
The course treats the following topics: - Relevant physical oceanography - Elements of marine geology (seafloor topography, acoustical properties of sediments and rocks) - Underwater sound propagation (ray acoustics, ocean noise) - Interaction of sound with the seafloor (reflection, scattering) - Principles of sonar (beamforming) - Underwater acoustic mapping systems (single beam echo sounding, multi-beam echo sounding, sidescan sonar) - Data analysis (refraction corrections, digital terrain modelling) - Applications (hydrographic survey planning and navigation, coastal engineering) - Current and future developments.
- Subjects:
- Land Surveying and Geo-Informatics
- Keywords:
- Underwater acoustics -- Remote sensing Ocean bottom Ocean bottom -- Remote sensing
- Resource Type:
- Courseware
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Courseware
This course discusses the requirement, interpretation, methods and design of hydrological measurements. Following topics are covered: Accuracy requirements of measurements and error propagation: Related to a problem the required accuracy of measurements and the consequences for accuracy in the final result are discussed. Different types of errors are handled. Propagation of errors; for dependent and independent measurements, from mathematical relations and regression is demonstrated. Recapitulated is the theory of regression and correlation. Interpretation of measurements, data completion: By standard statistical methods screening of measured data is performed; double mass analysis, residual mass, simple rainfall-runoff modelling. Detection of trends; split record tests, Spearman rank tests. Methods to fill data gaps and do filtering on data series for noise reduction. Methods of hydrological measurements and measuring equipment: To determine quantitatively the most important elements in the hydrological cycle an overview is presented of most common hydrological measurements, measuring equipment and indirect determination methods i.e. for precipitation, evaporation, transpiration, river discharge and groundwater tables. Use, purpose and measurement techniques for tracers in hydrology is discussed. Advantages and disadvantages and specific condition/application of methods are discussed. Equipment is demonstrated and discussed. Areal distributed observation: Areal interpolation techniques of point observations; inverse distance, Thiessen, contouring, Kriging. Comparison of interpolation techniques and estimation of errors. Correlation analysis of areal distributed observation of rainfall. Design of measuring networks: Based on correlation characteristics from point measurements (e.g. rainfall stations) and accuracy requirements the design of a network of stations is demonstrated.
- Subjects:
- Hydraulic Engineering
- Keywords:
- Hydrology -- Measurement Hydrology
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- Courseware
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MOOC
Engineers in the automotive industry are required to understand basic safety concepts. With increasing worldwide efforts to develop connected and self-driving vehicles, traffic safety is facing huge new challenges. This course is for students or professionals who have a bachelor's degree in mechanical engineering or similar and who are interested in a future in the vehicle industry or in road design and traffic engineering. It's also of value for people already working in these areas who wantbetter insight into safety issues. This course teaches the fundamentals of active safety (systems for avoiding crashes or reducing crash consequences) as well as passive safety (systems for avoiding or reducing injuries). Key concepts include in-crash protective systems, collision avoidance, and safe automated driving. The course will introduce scientific and engineering methodologies that are used in the development and assessment of traffic safety and vehicle safety. This includes methods to study the different components of real-world traffic systems with the goal to identify and understand safety problems and hazards. It includes methods to investigate the attitudes and behavior of drivers and other road users as well as recent solutions to improve active safety. Italso includes methods to study human body tolerance to impact and solutions to minimize the injury risk in crashes. Study topics include crash data analysis and in-situ observational studies of drivers and other road users by the use of instrumented vehicles and roadside camera systems. Solutions in active safety, such as driver alertness monitoring, driver information as well as collision avoidance and collision mitigation systems, will be described. Examples of in-crash protective systems are combinations of traditional restraints such as seat belts and airbags but with advanced functions such as automatic adaption to the individual occupant as well as pre-collision activation based on advanced integrated sensor systems and communication systems. The course will be based on recorded lectures that use videos and animations to enhance the experience. Online tutorials that access simulation models will give the participants an experience of influencing parameters in active safety and passive safety systems. As a result of support from MathWorks, students will be granted access to MATLAB/Simulink for the duration of the course.
- Subjects:
- Transportation
- Keywords:
- Traffic safety Roads -- Design construction Motor vehicles -- Safety measures Automobile industry trade
- Resource Type:
- MOOC
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Courseware
This course discusses the evolution and role of urban public transportation modes, systems, and services, focusing on bus and rail. It covers various topics, including current practice and new methods for data collection and analysis, performance monitoring, route design, frequency determination, vehicle and crew scheduling, effect of pricing policy and service quality on ridership.
- Subjects:
- Transportation
- Keywords:
- Urban transportation -- Management Local transit Urban transportation -- Planning
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- Courseware
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Others
We provide advice and resources to enable you to develop and/or extend your statistical computing skills, helping you to independently use common statistical packages (R, Stata, SAS, SPSS) for the analysis of research data.
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MOOC
This short course is adapted from a semester length graduate level coursetaught at MIT covering Qualitative Research Methods. This online course will focus specifically on teaching how to prepare for and conduct a conversational interview for data gathering purposes. We will also discuss the nature of qualitative research as a methodology, how it compares and differs from other forms of research, and how qualitative and quantitative research complement each other in a research project. This isthe first in a multi-part series which will be released over the coming year, which will focus on Conversational Interviewing, Data Analysis, and Constructing Theory. You might have encountered other forms of interview techniques in your studies and training. The form that we are teaching is the preferred method of Professor Silbey's, one that she has used extensively throughout her career. The goal is to construct an interview protocol such that you will be able to guide your interviewee through topics of interest to your study without bringing them up explicitly, in order to explore experiences and accounts without pointing respondents in particular directions. Not sure what an interview protocol is? No problem! You will by the end of the course.
- Subjects:
- Statistics and Research Methods
- Keywords:
- Conversation analysis Qualitative research -- Methodology Social sciences -- Research -- Methodology Interviewing
- Resource Type:
- MOOC
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MOOC
In this course, you will obtain some insights about marketing to help determine whether there is an opportunity that actually exists in the marketplace and whether it is valuable and actionable for your organization or client. Week 1: Assess methods available for creating quantitative surveys, along with their advantages and disadvantages. Identify the type of questions that should be asked and avoid unambiguous survey questions. Week 2: Design, test, and implement a survey by identifying the target audience and maximizing response rates. You will have an opportunity to use Qualtrics, a survey software tool, to launch your own survey. Week 3: Analyze statistical models that can be applied to your marketing data, so that you can make data-driven decisions about your marketing mix. Week 4: Predict most likely outcomes from the marketing decisions and match the type of analysis needed for your business problem. Take Quantitative Research as a standalone course or as part of the Market Research Specialization. You should have equivalent experience to completing the second course in this specialization, Qualitative Research, before taking this course. By completing the third class in the Specialization, you will gain the skills needed to succeed in the full program.
- Subjects:
- Marketing and Statistics and Research Methods
- Keywords:
- Quantitative research Marketing research
- Resource Type:
- MOOC