Astronomy Research Data Analysis with Python

Kursus ini dirancang untuk memberikan pengetahuan dasar tentang metode numerik dan pembahasan berbagai metode numerik yang bisa diterapkan saat menyelesaikan permasalahan di bidang teknik.Kursus ini akan bermanfaat bagi:Mahasiswa yang ingin berkarir di bidang teknik sebagai engineer.Engineer saat melamar pekerjaan di bidangnya. Penguasaan pengetahuan tentang metode numerik akan memberikan keunggulan saat wawancara pekerjaan.Engineer saat menjalani pekerjaannya yang akan berhadapan dengan berbagai permasalahan yang bisa diselesaikan dengan memakai pengetahuan tentang metode numerik.Topik-topik di dalam kursus ini disampaikan melalui berbagai gambar, tabel, diagram, dan skema; sehingga akan lebih mudah diserap peserta.Kursus ini dikembangkan dari pengalaman instruktur mengajarkan matakuliah Metode Numerik di fakultas Teknik selama bertahun-tahun.Kursus ini membahas tentang:Pengantar metode numerik, model matematika, dan error.Berbagai area permasalahan dimana metode numerik bisa dipakai, seperti: akar fungsi satu variabel bebas, integrasi numerik, interpolasi, sistem persamaan linear, dan regresi.Pembahasan berbagai teknik dalam metode numerik, seperti: metode biseksi dan metode Newton-Raphson untuk mencari akar fungsi satu variabel bebas; aturan trapesium, aturan Simpson 1/3, dan aturan Simpson 3/8 untuk menghitung integral secara numerik; metode Gregory-Newton beda hingga maju dan beda hingga mundur untuk melakukan interpolasi; iterasi Jacobi dan iterasi Gauss-Seidel untuk menyelesaikan sistem persamaan linear; regresi linear polinomial order 1 dan regresi kuadratik polinomial order 2 untuk mencari fungsi regresi dari data tersebar. Hendri Yanto - MathemaNesos

In this course, you will learn Statistical DownScaling Model (SDSM) software. In this modern world as a researcher, we design analysis and explore climate by using software to enhance the best quality analysis. Because of accurate results and analysis by Statistical DownScaling Model (SDSM) software, we are able taking decisions properly to predict the climatic change of the world of different parts.To enhance the proper analysis of rainfall, temperature, humidity, etc, of the different parts of the world, this Statistical DownScaling Model (SDSM) climatic Data Analysis Course will help you perfectly with proper guidelines.With my professional experience, I have created this course with a proper sequence of steps of topics so that you can understand and learn the proper way. Although the course is focused on Statistical DownScaling Model (SDSM), the climatic concepts and steps are used with references to parameters of different climatic data. The course focuses on the analysis and knowledge utilized on a professional level worldwide.After completing this course you will get the following outcomes:Will be able to process data perfectly.Will be able to use SDSM Software confidently.Will be able to analyze rainfall data.Will be able to analyze temperature data.Will be able to analyze humidity data.Will be able to analyze streamflow data.Will be able to take the critical decision on climate change.And Many more...

Artificial intelligence is rapidly transforming education, offering new possibilities for teaching, learning, and productivity. This course equips educators with the knowledge, strategies, and confidence to integrate AI tools responsibly and effectively into their classrooms. Participants will begin by exploring the foundations of AI—demystifying common misconceptions and identifying how AI already supports daily teaching practices. From there, the course examines practical classroom applications such as lesson planning, content creation, differentiation, and accessibility support for diverse learners. Educators will also learn how AI can streamline tasks like grading, rubrics, and feedback, freeing up time to focus on student engagement.Equally important, the course addresses the risks and ethical considerations of AI, including bias, misinformation, privacy, and academic integrity. Participants will critically analyze these issues and develop strategies for teaching students responsible use of AI. Through hands-on practice with popular tools like ChatGPT, Diffit, Curipod, MagicSchool, and Grammarly, educators will build their own AI toolkit. The course culminates in the creation of an AI-infused lesson or unit plan that promotes creativity, equity, and digital citizenship.By the end, educators will be empowered to leverage AI as a supportive partner in instruction—maximizing its benefits while modeling ethical, transparent, and student-centered practices. Educators will understand how to integrate AI into their own classroom environments to help with mundane tasks while providing engaging activities and feedback for students.

The course on foundation engineering includes bearing capacity calculation based on laboratory and field test results, settlement analysis of shallow foundation and load carrying capacity of deep foundation (i.e. Pile Foundation). The course content is clipped in small clips of 15-20 minutes for quick reference and ease for understanding. It covers majority of the topics included in UG level curriculum, and at the same time it can be a handy tool for PG students and working professionals.The course is short and precise for quick understanding and learning. Suitable for learners willing to study Foundation Engineering (or Soil Mechanics-II) from scratch. The course combined with the "Complete Course on Geotechnical Engineering" by the same instructor is wholesome to study soil mechanics. Further, quizzes are added to make learners more competent and review self-progress. Quizzes do include question which may not be covered in the course, so as to expand learner's quest and horizon.Conclusively, this course is being developed in quite simple and easy way to study erratic topics (like bearing capacity equations and their relevant application, varied terminologies, settlement calculation) involved in foundation engineering. I am sure this course would greatly benefit learners. Also, do provide your valuable feedback.

Say goodbye to endless nights of researching and writing!We, Florian and Sanne, both wrote two master theses (8.5 / 10 average) and created an online course to show you not only what to write in each chapter, but also provide highly actionable advice on how to manage the whole thesis process.When writing our own theses (at Erasmus University Rotterdam) we often found each other struggling with similar problems: 1) finding a fun yet relevant topic, 2) issues with finding a suitable methodology to test hypotheses and 3) requiring a lot of time to understand what to write exactly in each chapter. Our university did provide some preparatory courses, but that was not enough to know exactly how to perform the research ourselves.We decided to build an online course to help students from all backgrounds write a better thesis in less time. Through our experiences of writing theses ourselves and working as consultants, we aim to teach you our best learnings in project management, problem solving and how to write persuasively.The highlights of the course include:Thorough discussion of how we managed the process of writing the thesis (including 'managing' our supervisor')Screenshots from our theses to show you how which illustrations you can use to improve your gradeActionable advice on how to learn from mistakes we madeStep-by-step explanation of how to solve common research problemsSay hello to higher grades and faster delivery. Enrol now - you won't regret it!

Are you a statistician or researcher? Then you might be interested in Jamovi. Jamovi is a free statistical software built on R. It allows you to prepare data, create visualizations, and run all kinds of statistical analyses. Jamovi has an easy-to-use interface that can be navigated using its large menus. Variables can be dragged and dropped in the appropriate fields to perform any task, such as creating a box plot or running a t-test.These features make Jamovi a more effective option than SPSS, STATA, or R, as these tools often come with steep learning curves, paid subscriptions, or both. With this statistical software, you can get started in a brief period of time, as there are no fees, and you don’t have to spend hours memorizing or debugging lines of code.This course provides guidance on several areas related to data preparation and statistical analyses. You'll learn how to:Navigate Jamovi using its menus and editing featuresPrepare data by renaming variables, changing variable types, and setting filtersCreate visualizations such as scatterplots, histograms, box plots, and bar plotsRun statistical analyses, including t-tests, ANOVAs, and linear regression modelsI hope you'll join me in exploring this free and easy-to-use statistical software.