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Students develop and document code to read, process, and analyze datasets of varying complexity, interpret results, and communicate findings in written and oral formats. The syllabus covers programming fundamentals, geospatial data and map projections, time-series statistical analysis, outlier handling, uncertainty quantification, and fitting simple models, with weekly readings and hands-on lecture-tutorial delivery.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":20,"@type":76,"position":81},"https://docshare.wps.com/document/exam/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/computational-earth-systems-science-course-es2001-learning-syllabus/496636/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/computational-earth-systems-science-course-es2001-learning-syllabus/496636.png","ImageObject",300,407,{"name":92,"@type":93},"dabgoudw","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-05","2026-10-02",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What programming and analysis skills will students learn in ES2001?","Question",{"text":112,"@type":113},"Students learn how to choose appropriate visualization and analysis techniques, write simple functions, read and plot external datasets, and perform statistical analyses such as best fitting, correlation/cross-correlation, and least squares.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which geospatial and time-series topics are included?",{"text":117,"@type":113},"The course covers geospatial representation, benefits and pitfalls of different map projections, spatial interpolation techniques, and time-series methods to study long-term rates and seasonal cycles.",{"name":119,"@type":110,"acceptedAnswer":120},"How is student performance assessed in this course?",{"text":121,"@type":113},"Assessment includes Continuous Assessment: weekly quizzes (open book), weekly lab assignments, and mid-term tests, with specified weightings for each component.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},496636,1791251350,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":19,"category_name":20,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":39,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":46,"preview_page_count":4},3985747860484,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","| Expected Implementation in Academic Year (New format) | AY2026-2027 |\n| --- | --- |\n| Semester/Trimester/Others (specify approx. Start/End date) | Semester 1 |\n| Course Author\u003Cbr>* Faculty proposing/revising the course | Assoc Prof Benoit Taisne |\n| Course Author Email | [btaisne@ntu.edu.sg](btaisne@ntu.edu.sg) |\n| Course Title | COMPUTATIONAL EARTH SYSTEMS SCIENCE |\n| Course Code | ES2001 |\n| Academic Units | 4 |\n| Contact Hours | 78 |\n| Research Experience Components | Not Applicable |\n\n| Pre-requisites | MH1801 and MH1800; OR\u003Cbr>MH1100 and MH1101; OR\u003Cbr>CY1201 and CY1202; OR\u003Cbr>CY1602 and CY1601; OR\u003Cbr>MH1802 |\n| --- | --- |\n| Co-requisites | NIL |\n| Pre-requisite to | ES0002, ES2802, ES3006, ES3102, ES4302, ES4902, ES4911 |\n| Mutually exclusive to | NIL |\n| Replacement course to | NIL |\n| Remarks (if any) |  |\n\nIn this course, you will learn how to choose data visualization and analysis techniques that are relevant to thescientiØc challenge you are facing, and to create their own functions for performing these analyses. You will also learn to master a programming language that could serve as a basis to learning other languages. The goal is to give you a solid grounding in the basic statistics and programming techniques that will allow you to continue to build on these skills throughout the rest of your undergraduate training and careers, with a strong emphasis on critical thinking and developing skills that can be adapted in the future to suit a changing world, rapidly changing technologies, and increasingly varied sources of environmental and geoscience data.  \nUpon the successful completion of this course, you (student) would be able to:  \n\n| ILO 1 | Visualise and statistically analyse Earth system and environmental datasets using appropriate computational tools. |\n| --- | --- |\n| ILO 2 | Develop and document computer code to read, process, and analyse datasets of varying structure and complexity. |\n| ILO 3 | Interpret computational results and communicate Øndings clearly in written and oral forms. |\n| ILO 4 | Demonstrate conØdence in computational thinking, problem-solving, and independent learning. |\n\nThe course will cover aspects of programming style and funcƟonality, starƟng from the basics of variables, loops, reading and ploƫng external data sets, and wriƟng simple funcƟons. The course will use widely used programming language. GeospaƟal data and maps will be covered, including the benefits and piƞalls of different map projecƟons and techniques for spaƟal interpolaƟon of data. We will use Ɵme series from environmental data to teach techniques instaƟsƟcal analysis of Ɵme series, such as invesƟgaƟng long-term rates and seasonal cycles. Other classes will ensure that students gain the knowledge and pracƟce to assess the significance of their results, to apply best pracƟces to assess and remove outliers, and to understand and quanƟfy uncertainƟes in their data and results, as well as to fit simple models to the data.  \nWeekly reading of documentaƟon on new concept and use of funcƟon.  \nThe students are also encourage to read introductory books on environmental data analysis.  \nThe course itself is not following any specific text-book.  \n| \u003Cbr>| | | \u003Cbr>| | |\n| --- | --- | --- | --- | --- | --- |\n| 1 | Introduction to the class and programming skills | 4 | In\u003Cbr>person | Assignment 1 | Varies |\n| 2 | Reading external Øle and plotting time series and histograms | 1,2,3,4 | In\u003Cbr>person | Assignment 2 | Varies |\n| 3 | Conditional\u003Cbr>statements and loops | 2; 4 | In\u003Cbr>person | Assignment 3 | Varies |\n| 4 | Map projection and plotting maps | 1,2,4 | In\u003Cbr>person | Assignment 4 | Varies |\n| 5 | Continue on geospatial representation and tools | 1,2,4 | In\u003Cbr>person | Assignment 5 | Varies |\n| 6 | Catch up week | 3,4 | In\u003Cbr>person | Mid-term 1 / group discussion | Varies |\n| 7 | Introduction to linear algebra | 2,4 | In\u003Cbr>person | Assignment 6 | Varies |\n| 8 | Data analysis: best Øtting | 1,2,3,4 | In\u003Cbr>person | Assignment 7 ","cbCaivxwycZood5j","https://ap.wps.com/l/cbCaivxwycZood5j","pdf",1189747,"English","# Course Overview\n## Learning Outcomes (ILOs)\n## Course Topics and Weekly Schedule\n## Hands-on Activities and Semester Project\n## Report and Presentation\n## Assessment Components (CA)","[{\"question\":\"What programming and analysis skills will students learn in ES2001?\",\"answer\":\"Students learn how to choose appropriate visualization and analysis techniques, write simple functions, read and plot external datasets, and perform statistical analyses such as best fitting, correlation/cross-correlation, and least squares.\"},{\"question\":\"Which geospatial and time-series topics are included?\",\"answer\":\"The course covers geospatial representation, benefits and pitfalls of different map projections, spatial interpolation techniques, and time-series methods to study long-term rates and seasonal cycles.\"},{\"question\":\"How is student performance assessed in this course?\",\"answer\":\"Assessment includes Continuous Assessment: weekly quizzes (open book), weekly lab assignments, and mid-term tests, with specified weightings for each component.\"}]","COMPUTATIONAL EARTH SYSTEMS SCIENCE - Course ES2001 - Learning syllabus | PDF",1790937112]