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In these comprehensive video courses, created by Santiago Basulto, you will learn the whole process of data analysis. You'll be reading data from multiple sources (CSV, SQL, Excel), process that data using NumPy and Pandas, and visualize it using Matplotlib and Seaborn, Additionally, we've included a thorough Jupyter Notebook course, and a quick Python reference to refresh your programming skills.
- Course related:
- AMA1600 Fundamentals of AI and Data Analytics and AMA1751 Linear Algebra
- Subjects:
- Computing, Data Science and Artificial Intelligence and Mathematics and Statistics
- Keywords:
- Computer programming Computer science Python (Computer program language)
- Resource Type:
- Others
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Others
Kaggle offers a no-setup, customizable, Jupyter Notebooks environment. Access GPUs at no cost to you and a huge repository of community published data & code. Inside Kaggle you’ll find all the code & data you need to do your data science work. Use over 50,000 public datasets and 400,000 public notebooks to conquer any analysis in no time.
- Subjects:
- Computing, Data Science and Artificial Intelligence
- Keywords:
- Artificial intelligence Big data Machine learning
- Resource Type:
- Others
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MOOC
Meeting growing global energy demand, while mitigating climate change and environmental impacts, requires a large-scale transition to clean, sustainable energy systems. Students and professionals around the world must prepare for careers in this future energy landscape, gaining relevant skills and knowledge to expedite the transformation in industry, government and nongovernmental organizations, academia, and nonprofits. The building sector represents a large percentage of overall energy consumption, and contributes 40% of the carbon emissions driving climate change. Yet buildings also offer opportunities for substantial, economical energy efficiency gains. From retrofit projects to new construction, buildings require a context-specific design process that integrates efficiency strategies and technologies. In this course, you'll be introduced to a range of technologies and analysis techniques for designing comfortable, resource-efficient buildings. The primary focus of this course is the study of the thermal and luminous behavior of buildings. You'll examine the basic scientific principles underlying these phenomena, and use computer-aided design software and climate data to explore the role light and energy can play in shaping architecture. These efficiency design elements are critical to the larger challenge of producing energy for a growing population while reducing carbon emissions.
- Subjects:
- Environmental Engineering, Building Services Engineering, and Building and Real Estate
- Keywords:
- Buildings -- Energy conservation Sustainable architecture Sustainable buildings -- Design construction
- Resource Type:
- MOOC
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Others
Create interactive maps, and discover patterns in geospatial data.
- Subjects:
- Computing, Data Science and Artificial Intelligence and Land Surveying and Geo-Informatics
- Keywords:
- Python (Computer program language) Geospatial data
- Resource Type:
- Others
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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
- Resource Type:
- 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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