Showing posts with label Science. Show all posts
Showing posts with label Science. Show all posts
Advance Diploma in Bioinformatics with Internship Project
Monday, August 4, 2025
Complete Bioinformatics, with internship project, Tools and Techniques, Computational Biology, Data Analysis and ML
Preview this Course
Description
Bioinformatics
Description
Take the next step in your scientific journey! Whether you're an aspiring researcher, a budding bioinformatician, a healthcare professional, or simply passionate about exploring the intersection of biology and data science, this course is your gateway to mastering the principles of bioinformatics. Dive into the world of genomic data analysis, computational biology, and algorithm-driven research. Strengthen your knowledge of DNA sequencing, gene expression analysis, and genome annotation. Enhance your analytical skills with molecular data interpretation, programming techniques, and bioinformatics tools. Build a solid foundation for advancements in personalized medicine, biotechnology, and genetic research. This is your opportunity to elevate your expertise, drive scientific innovation, and make a meaningful impact in the ever-evolving fields of bioinformatics and computational genomics!
With this course as your guide, you'll learn how to:
Understand the fundamental concepts and principles of bioinformatics and computational biology.
Gain insights into key bioinformatics techniques such as sequence alignment, next-generation sequencing (NGS) data analysis, and genome annotation.
Learn about the applications of bioinformatics in fields like CRISPR and Gene Editing, personalized medicine, drug discovery, evolutionary biology, and systems biology.
Invest in your knowledge today and build a strong foundation for advanced studies and innovative research in bioinformatics, genomics, and computational biology.
The Frameworks of the Course
Engaging video lectures, case studies, assessments, downloadable resources, and interactive exercises form the foundation of this course. This course is designed to provide an in-depth understanding of bioinformatics, its principles, tools, and real-world applications through comprehensive chapters and units.
This course introduces crucial bioinformatics tools and programming techniques, including Python, R, BLAST, and data visualization libraries, equipping you with practical skills for genomic data interpretation and biological research.
You will explore key concepts on genomics, proteomics and transcriptomics along with fundamental concept on RNA-Seq Data Analysis, Omics Data analysis. The course will cover topics including Computational Biology, Data Analysis and Machine Learning. This course will also introduce an understanding about Literature Review in Bioinformatics, Research Proposal Writing, Data Visualization, Report Preparation and Publishing Research Papers.
This course also helps you to strengthen your knowledge and application of advanced research, data-driven discovery, and innovation in the fields of genomics, computational biology, and personalized medicine.
In the first part of the course, you’ll learn about introduction, scopes and applications of Bioinformatics. You will learn about Tools and Techniques in Bioinformatics. You will learn the details about genomics, proteomics and transcriptomics. You will also understand about Computational Biology. You will also know about Phylogenetics-Tree Construction and Visualization.
In the middle part of the course, you’ll be able to learn about Data Analysis and Machine Learning in Bioinformatics, basis of Machine Learning in Bioinformatics, Tools and Platforms for Bioinformatics Data Analysis. You will also learn about Machine Learning Techniques in Bioinformatics. Gain knowledge about Omics Data Analysis. You will understand about applications of Artificial intelligence (AI), data Analysis and Machine Learning in Bioinformatics. You will also know about Drug Discovery and Development. Learn about CRISPR and Gene Editing Tools. You will also have the knowledge on Personalized Medicine and Precision Healthcare.
In the final part of the course, you’ll know about Research Methodology and Scientific Writing,Literature Review in Bioinformatics. Gain knowledge on Research Proposal Writing, Data Visualization and Report Preparation and Publishing Research Papers.
Course Content:
Part 1
Introduction and Study Plan
Ø Module 1: Fundamentals of Bioinformatics
Ø Module 2: Tools and Techniques in Bioinformatics.
Ø Module 3: Genomics, Proteomics and Transcriptomics
Ø Module 4: Computational Biology
Ø Module 5: Data Analysis and Machine Learning in Bioinformatics.
Ø Module 6: Practical Applications
Ø Module 7: Research Methodology and Scientific Writing
Part 2
Projects
· Predict the Function of Non-Annotated Genes Using Supervised Learning Techniques.
· Analyze Genomic Mutations Associated with Specific Cancers Using Bioinformatics.
Internship in Bioinformatics
This course is designed to provide students with a foundation in bioinformatics, integrating biology, computer science, and data analysis. The course will cover essential concepts, tools, and techniques used in bioinformatics to analyze and interpret biological data. By the end of the course, students will have hands-on experience with bioinformatics tools and databases and will be able to apply computational approaches to solve biological problems.
Assignment Title: Bioinformatics Analysis of Gene Sequences and Protein Structures
Project Title: Comprehensive Bioinformatics Analysis of a Biological Dataset
Who this course is for:
- This course is designed for students and researchers in life sciences, computer science, or related fields who want to integrate computational methods into their biological research.
- Computer Scientists and Data Analysts interested in applying their programming and data analysis skills to solve biological problems.
- Healthcare Professionals and Biotech Enthusiasts looking to understand how bioinformatics drives innovations in drug discovery, personalized medicine, and genomics.
Spatial Data Analysis with Earth Engine Python and Colab
Wednesday, June 9, 2021
Spatial Data Analysis with Earth Engine Python and Colab - Learn big spatial data, machine learning, GIS and remote sensing with Earth Engine Python API and Google Colab
- New
- Created by Dr. Alemayehu Midekisa, Spatial eLearning
- English [Auto]
What you'll learn
- Students will access and sign up the Google Earth Engine Python API
- Access and visualize satellite data in Earth Engine Python API
- Export geospatial data including raster and vector data formats
- Access images and image collections from the Earth Engine API
- Apply various machine learning algorithms on the Earth Engine Python API and Colab
Description
Do you want to access satellite sensors using Earth Engine Python API and Google Colab?
Do you want to learn the spatial data science on the cloud?
Do you want to become a geospatial data scientist?
Enroll in my new course to Spatial Data Analysis with Earth Engine Python API and Colab.
I will provide you with hands-on training with example data, sample scripts, and real-world applications. By taking this course, you be able to install Anaconda and Jupyter Notebook. Then, you will have access to satellite data using the Earth Engine Python API and Google Colab.
What makes me qualified to teach you?
I am Dr. Alemayehu Midekisa, PhD. I am a geospatial data scientist, instructor and author. I have over 15 plus years of experience in processing and analyzing real big Earth observation data from various sources including Landsat, MODIS, Sentinel-2, SRTM and other remote sensing products. I am also the recipient of one the prestigious NASA Earth and Space Science Fellowship. I teach over 10,000 students on Udemy.
In this Spatial Data Analysis with Earth Engine Python API and Colab course, I will help you get up and running on the Earth Engine Python API and Google Colab. By the end of this course, you will have access to all example script and data such that you will be able to accessing, downloading, visualizing big data, and extracting information.
In this course we will cover the following topics:
Introduction to Earth Engine Python API and Colab
Set Up a Google Colab Environment
Raster Data Visualization
Vector Data Visualization
Load Landsat Satellite Data
Cloud Masking Algorithm
Calculate NDVI
Export images and videos
Process image collections
Machine Learning Algorithms
One of the common problems with learning image processing is the high cost of software. In this course, I entirely use open source software including the Google Earth Engine Python API and Colab. All sample data and script will be provided to you as an added bonus throughout the course.
Jump in right now and enroll.
Best,
Dr. Alemayehu Midekisa, PhD
Who this course is for:
This course is meant for professionals who want to manipulate big spatial data on the cloud using Earth Engine and Google Colab
Anyone who wants to learn accessing and extracting information from satellite data
Anyone who wants to apply for a spatial data scientist job position
100% Off Udemy Coupon . Free Udemy Courses . Online Classes
Quantum Physics: an overview of a weird world (Basics)
Thursday, October 17, 2019
Online Courses Udemy - Quantum Physics: an overview of a weird world (Basics), A primer on the conceptual foundations of Quantum Physics
BESTSELLER, 4.5 (378 ratings), Created by Marco Masi, English [Auto-generated]
PREVIEW THIS COURSE - GET COUPON CODE
What you'll learn
- The conceptual foundations of Quantum Physics.
- A comprehensive A-Z guide that will save you a ton of time in searching elsewhere trying to piece all the different information together.
- Quantum Theory without falling into oversimplifications or hyped versions and yet conceived for an audience of non-physicists.
- The double silt experiment, the wave particle duality, entanglement, quantum superposition, the uncertainty principle of Heisenberg, Schrödinger's cat paradox, quantum tunneling and much more.
- A course that fills the gap between a too popularized version of Quantum Mechanics and too high level university courses.
- You will learn all the basics, enabling you to distinguish between mere speculative interpretations in fashion and the real experimental facts.
Requirements
- Some lectures resort to high school math and some calculus.
- Example: Pythagorean theorem, square root, exponential, sin/cos functions, Cartesian coordinates, vectors, intuitive concept of a derivative, basic notion of a complex number.
- However, no university level required. In case you need a mathematical refresh an appendix will help you to recall some elementary mathematical concepts.
- Please take your time. The concepts you are going to learn may need some effort to be 'digested' and some material may need to be viewed more than once.
Description
Note: Take a look at the free lectures! Scroll down to the curriculum and click on 'Basics I'. The 'preview' lectures are free. That might help you to get a better feeling on what's about.
Why this course? This is an introductory course (Basics) that originates from my desire to share my knowledge of the mysterious as fascinating world of Quantum Physics. Considering how the media (sometimes also physicists) present Quantum Theory focusing only on highly dubious ideas and speculations backed by no evidence or, worse, promote pseudo-scientific hypes that fall regularly into and out of fashion, I felt it necessary to create a serious introduction to the conceptual foundations of Quantum Physics. The second part (Supplemental), which focuses further on some selected topics, can be found on the Udemy portal as well.
Who is it for? For the autodidact who is looking for a serious and rigorous introduction to the foundations of quantum physics and some of its philosophical implications. This course does not need a technical background except for some basics of algebra, trigonometry and calculus. It is easier than a university course but needs more effort than a popular science lecture. It might be easier for those having already some math background but a mathematical appendix is furnished for those who need a reminder.
Even though these lectures are not a replacement for college courses they could complement it. University or college classes do not address the foundations and the philosophical aspects of Quantum Physics, teaching Quantum Mechanics mostly from the formal and mathematical perspective, which is something we will restrict only to the essential basics here. While in schools, colleges and universities, Quantum Physics is taught with a dry and almost exclusively technical approach which furnishes only a superficial insight on its foundations, this course is recommended also to school, undergraduate and graduate students who would like to look further. Not only physicists could (re-)discover some topics but philosophers, engineers, IT students or historians of science could acquire with this course a basic preparation which is unlikely to be offered in most departments. This online course proposes itself also to become part of a faculty curriculum in departments or other institutions which would like to expand their interests towards the foundations of Quantum Physics (contact the instructor for details).
What is it about? A course on the conceptual foundations of Quantum Physics on topics that you won't find elsewhere explained at introductory level. It will lead you by hand as clearly as possible from the abc of Quantum Mechanics to the most recent experiments and its implications.
We review the standard concepts like the wave-particle duality, Heisenberg`s uncertainty principle, Schrödinger`s cat, the vacuum zero-point energy and virtual particles, among several others. Then we deepen the subject analysing quantum entanglement, the so called "EPR paradox" which question our naive understanding of the meaning of reality and locality (for more details on the content look up the curriculum page).
My aim is to deliver the material necessary so that you will be able by yourself to distinguish between mere speculative (and more or less extravagant) interpretations in fashion, and the real Quantum Theory and its experimental facts as it is.
Note I: School, college or university students will get a free coupon. Send an e-mail from your school/department account (or the scan of an id) to marco.masi@gmail.com and you will get the coupon.
Note II: A personal tutorship and training support is also available for individuals who would like to submit their doubts in a Q&A session or for students of institutions who would like to include this course as part of their curriculum. In the latter case the assignments will be reviewed individually and, at the end of the course, the student will have to develop a short topical paper. A certificate of attendance and a student proficiency assessment will be provided. Please contact me for more details at: marco.masi@gmail.com
Note II: I will be grateful if you post a review with text. It is an essential resource that helps me to understand if and how the course can be optimized.
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