Introduction to Computational Thinking and Data Science at MIT
You will learn how to utilize computation to achieve a range of objectives in 6.00.2x, which also gives you a quick introduction to a number of computational problem-solving subjects. Learners with some previous Python progair jordan 4 retro military black yeezy 350 nike air jordan 1 banchero orlando jersey aguilas cibaeñas jersey yara ellen wille yara ellen wille nike ispa 270 wigs online soccer jerseys for sale custom sublimated hockey jerseys jersey mls yeezy shoes under 1000 wig shop cheap jerseysramming expertise and a basic understanding of computational complexity are the target audience for this course. To further put the course’s principles into practice, you will spend a lot of time creating programs. For instance, you may create software to replicate a robot vacuuming a room or to model the dynamics of pharmacological treatments and viral population dynamics in a patient’s body.
This course will cover the following topics:
- Python 3 advanced programming
- Knapsack issue graphs and optimizing graphs
- Interactive programming
- Using the pylab package for plotting
- Random strolls
- Distributions and Probability
- Monte Carlo experiments
- fitting curves
- Statistical errors
A quick glimpse
Institution: MIT
Subject: Computer Science
Level: Intermediate
Prerequisites: 6.00.1x or equivalent (some prior programming experience in Python and a rudimentary knowledge of computational complexity)
Language: English
Video Transcript: English
About the instructors
1. John Guttag
The Dugald C. Jackson Professor of Computing Sciences and Electrical Engineering at MIT is Professor Guttag. He is also the group leader of the Data Driven Medical Studies Group at the Computer Science and Artificial Intelligence Laboratory. The team focuses on the use of cutting-edge computational methods in medicine. Prediction of negative medical occurrences, patient-specific therapy response prediction, non-invasive monitoring and evaluation methods, and telemedicine are all current efforts. In the fields of data socializing, sports analytics, program defined radios, software development, and mechanical theorem proving, he has also conducted research, published papers, and given lectures.
Brown University is where Professor Guttag earned both his applied mathematics master’s degree and his English bachelor’s degree. The University of Toronto awarded him a PhD.
Professor Guttag led the Electrical Engineering and Computer Science Department at MIT from January 1999 until August 2004. He belongs to the American Academy of Arts and Sciences and is a Member of the ACM.
2. Eric L. Grimson
Professor of computer engineering and science and Bernard M. Gordon Professor of Medical Engineering, Eric L. Grimson holds both positions. He served as MIT’s chancellor from 2011 until 2014. Professor Grimson has been a member of the MIT faculty since 1984 and has previously held the positions of education officer, assistant department head, and chair of the department of electrical computer science and engineering. Professor Grimson is well known around the world for his work in computer vision, particularly when it comes to applications in the study of medical images. He and his students have further created methods for site modeling, object and person identification, picture database indexing, image assisted surgery, activity and behavioral detection, and many more computer vision-related fields. Throughout his career, Professor Grimson has maintained a constant dialogue with his pupils. Currently, he is a lecturer for subjects 6.00, Introduction to Computer Science and Programming, and 6.01, Introduction to EECS.
