Online courses directory (113)
Do you have an interest in biology and quantitative tools? Do you know computational methods but do not realize how they apply to biological problems? Do you know biology but do not understand how scientists really analyze complicated data? 7.QBWx: Quantitative Biology Workshop is designed to give learners exposure to the application of quantitative tools to analyze biological data at an introductory level. For the last few years, the Biology Department of MIT has run this workshop-style course as part of a one-week outreach program for students from other universities. With 7.QBWx, we can give more learners from around the world the chance to discover quantitative biology. We hope that this series of workshops encourages learners to explore new interests and take more biology and computational courses.
We expect that learners from 7.00x Introduction to Biology – The Secret of Life or an equivalent course can complete this workshop-based course without a background in programming. The course content will introduce programming languages but will not teach any one language in a comprehensive manner. The content of each week varies. We want learners to have an introduction to multiple languages and tools to find a topic that they would want to explore more. Participants with programming experience will find some weeks easier than students with only biology experience, while those with a biology background should find the week on genetics easier. We recommend that learners try to complete each week to find what interests them the most.
Workshop Content Creators and Residential Leaders
Gregory Hale, Michael Goard, Ph.D., Ben Stinson, Kunle Demuren, Sara Gosline, Ph.D., Glenna Foight, Leyla Isik, Samir El-Boustani, Ph.D., Gerald Pho, and Rajeev Rikhye
Residential Outreach Workshop Organizer and Creator
Mandana Sassanfar, Ph.D.
This workshop includes activities on the following biological topics: population biology, biochemical equilibrium and kinetics, molecular modeling of enzymes, visual neuroscience, genetics, gene expression and development, and genomics. The tools and programming languages include MATLAB, PyMOL, StarGenetics, Python, and R. This course does not require learners to download MATLAB. All MATLAB activities run and are graded within the edX platform. We do recommend that participants download a few other free tools for the activities so that they learn how to use the same tools and programs that scientists use.
The world is trending in real time! Learn from Twitter to scalably process tweets, or any big data stream, in real-time to drive d3 visualizations using Apache Storm, the “Hadoop of Real Time.” Storm is free, open source, and fun to use! Learn from Karthik Ramasamy, Technical Lead of Storm@Twitter, about the distributed, fault-tolerant, and flexible technology used to power Twitter’s real-time data flow pipeline. Twitter open sourced Storm in 2011, and it graduated to a top-level Apache project in September, 2014. Starting from basic distributed concepts presented during our first Udacity-Twitter Storm Hackathon, link Storm concepts to Storm syntax to scalably drive Word Cloud visualizations with Vagrant, Ubuntu, Maven, Flask, Redis, and d3. Link to the public Twitter gardenhose stream to process live tweets, parse embedded URLs, and calculate Top worldwide hashtags. Extend beyond Storm basics by exploring multi-language capabilities in Python, integrate open source components, and implement real-time streaming joins. In your final project, follow real-time trending topics by implementing the data pipeline to visualize only tweets that contain Top worldwide hashtags. Extend your project by exploring the Twitter API, or any data source, alongside Hackathon participants as they design their own ideas, receive feedback from Karthik, and open source a final project calculating real-time tweet sentiment and geolocation to drive a U.S. Map.
The job of a data scientist is to glean knowledge from complex and noisy datasets.
Reasoning about uncertainty is inherent in the analysis of noisy data. Probability and Statistics provide the mathematical foundation for such reasoning.
In this course, part of the Data Science MicroMasters program, you will learn the foundations of probability and statistics. You will learn both the mathematical theory, and get a hands-on experience of applying this theory to actual data using Jupyter notebooks.
Concepts covered included: random variables, dependence, correlation, regression, PCA, entropy and MDL.
The motion of falling leaves or small particles diffusing in a fluid is highly stochastic in nature. Therefore, such motions must be modeled as stochastic processes, for which exact predictions are no longer possible. This is in stark contrast to the deterministic motion of planets and stars, which can be perfectly predicted using celestial mechanics.
This course is an introduction to stochastic processes through numerical simulations, with a focus on the proper data analysis needed to interpret the results. We will use the Jupyter (iPython) notebook as our programming environment. It is freely available for Windows, Mac, and Linux through the Anaconda Python Distribution.
The students will first learn the basic theories of stochastic processes. Then, they will use these theories to develop their own python codes to perform numerical simulations of small particles diffusing in a fluid. Finally, they will analyze the simulation data according to the theories presented at the beginning of course.
At the end of the course, we will analyze the dynamical data of more complicated systems, such as financial markets or meteorological data, using the basic theory of stochastic processes.
The knowledge base of the world is rapidly expanding, and much of this information is being put online as textual data. Understanding how to parse and analyze this growing amount of data is essential for any organization that would like to extract valuable insights and gain competitive advantage. This course will demonstrate how text mining can answer business related questions, with a focus on technological innovation.
This is a highly modular course, based on data science principles and methodologies. We will look into technological innovation through mining articles and patents and implement natural language processing. We will also utilize other available sources of competitive intelligence, such as the gray literature and knowledge bases of companies, news databases, social media feeds and search engine outputs. Text mining will be carried out using Python, and could be easily followed by running the provided iPython notebooks that execute the code.
Who is this course for?
The course is intended for data scientists of all levels as well as domain experts on a managerial level. Data scientists will receive a variety of different toolsets, expanding knowledge and capability in the area of qualitative and semantic data analyses. Managers will receive hands-on oversight to a high-growth field filled with business promise, and will be able to spot opportunities for their own organization. You are encouraged to bring your data sources and business questions, and develop a professional portfolio of your work to share with others. The discussion forums of the course will be the place where professionals from around the world share insights and discuss data challenges.
How will the course be taught?
The first week of the course describes a range of business opportunities and solutions centered around the use of text. Subsequent weeks identify sources of competitive intelligence, in text, and provide solutions for parsing and storing incoming knowledge. Using real-world case studies, the course provides examples of the most useful statistical and machine learning techniques for handling text, semantic, and social data. We then describe how and what you can infer from the data, and discuss useful techniques for visualizing and communicating the results to decision-makers.
What types of certificates does DelftX offer?
Upon successful completion of this course, learners will be awarded a DelftX Professional Education Certificate.
Can I receive Continuing Education Units?
The TU Delft Extension School offers Continuing Education Units for this course. Participants of TXT1x who successfully complete the course requirements will earn a Certificate of Completion and are eligible to receive 2.0 Continuing Education Units (2.0 CEUs)
How do I receive my certificate and CEUs?
Upon successful completion of the course, your certificate can be printed from your dashboard. The CEUs are awarded separately by the TU Delft Extension School.
The course materials of this course are Copyright Delft University of Technology and are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike (CC-BY-NC-SA) 4.0 International License.
Welcome to The Quantum World!
This course is an introduction to quantum chemistry: the application of quantum theory to atoms, molecules, and materials. You’ll learn about wavefunctions, probability, special notations, and approximations that make quantum mechanics easier to apply. You’ll also learn how to use Python to program quantum-mechanical models of atoms and molecules.
HarvardX has partnered with DataCamp to create assignments in Python that allow students to program directly in a browser-based interface. You will not need to download any special software, but an up-to-date browser is recommended.
This course has serious prerequisites. You will need to be comfortable with college-level chemistry and calculus. Some prior programming experience is also encouraged.
The Quantum World is ideal for:
- Chemistry majors who want extra material alongside an on-campus course
- Chemistry majors at an institution that does not offer quantum chemistry
- Physics or CompSci majors who want to branch out to chemistry
- Graduate students refreshing on quantum mechanics before their qualifying exams
- Professional chemists who want to brush up on their skills
This course bridges the gap between introductory and advanced courses in Python. While there are many excellent introductory Python courses available, most typically do not go deep enough for you to apply your Python skills to research projects. In this course, after first reviewing the basics of Python 3, we learn about tools commonly used in research settings.
Using a combination of a guided introduction and more independent in-depth exploration, you will get to practice your new Python skills with various case studies chosen for their scientific breadth and their coverage of different Python features.