Massachusetts Institute of Technology (MITx) is proud to declare free online course named as “Introduction to Probability – The Science of Uncertainty.” This “Introduction to Probability – The Science of Uncertainty” free online course gives you an introduction to probabilistic models, including random processes and the basic elements of statistical inference.
The course covers basic structure and elements of probabilistic modeling and the related field of statistical inference are the keys to analyzing data and making scientifically sound predictions and multiple discrete or continuous random variables, expectations, and conditional distributions. The course will start on January 17, 2017.
Course At A Glance
- Length: 18 Week
- Effort:12 hours per week
- Subject: Data Analysis & Statistics
- Institution: Massachusetts Institute of Technology (MITx)
- Languages: English
- Price: Free
- Certificate Available: Yes
- Session: Starts on January 17, 2017
Providers Details
Massachusetts Institute of Technology — a coeducational, privately endowed research university founded in 1861. The University is dedicated to advancing knowledge and educating students in science, technology, and other areas of scholarship that will best serve the nation and the world in the 21st century. Through MITx, the Institute furthers its commitment to improving education worldwide.
Requirements
- College-level calculus (single-variable and multivariable). Although this is not a mathematics course, it does rely on the language and some tools from mathematics.
- It requires a level of comfort with mathematical reasoning, familiarity with sequences, limits, infinite series, the chain rule, as well as the ability to work with ordinary or multiple integrals.
About This Course
- The course emphasizes the basic concepts and methodologies that are universally applicable. It is a challenging class, but it will enable you to apply the tools of probability theory to real-world applications or your research.The language of the course is English.
- The analytics Industry is one of the fastest growing in modern times so it is important in different business, science, and social science domains. In organizations, analytics enables professionals to convert extensive data and statistical and quantitative analysis into powerful insights that can drive efficient decisions.
How to Join This Course
You should register yourself through the given link to join this free on-line: https://courses.edx.org/register?course_id=course-v1%3AMITx%2B6.041x_4%2B1T2017&enrollment_action=enroll&email_opt_in=true
Course Format
The syllabus of the course given below, in the course you will cover these topics
- Multiple discrete or continuous random variables, expectations, and conditional distributions
- Laws of large numbers
- The main tools of Bayesian inference methods
- An introduction to random processes (Poisson processes and Markov chains)
Why Take This Course
- Advantage: The contents of this course are essentially the same as those of the corresponding MIT class (Probabilistic Systems Analysis and Applied Probability) — a course that has been offered and continuously refined over more than 50 years. It is a challenging class, but it will enable you to apply the tools of probability theory to real-world applications or your research.
- Certificate: After completing the course you will pursuing a Verified Certificate to highlight the knowledge and skills you gain ($99 ) with the help of this certificate you can give yourself an additional incentive to complete the course.
Learning Outcomes
After completing this free online course you will learn
- The basic structure and elements of probabilistic models
- Random variables, their distributions, means, and variances
- Probabilistic calculations
- Inference methods
- Laws of large numbers and their applications
- Random processes
Instructors
- John Tsitsiklis: He is a Professor with the Department of Electrical Engineering and Computer Science, and a member of the National Academy of Engineering.
- Patrick Jaillet: He is a Professor of Electrical Engineering and Computer Science and Co-Director of the MIT Operations Research Center.
- Zied Ben Chaouch: He is a TA and 6.041x Instructor, graduate student in MIT’s Department of Electrical Engineering & Computer Science.
- Dimitri Bertsekas: He is a Professor with the Department of Electrical Engineering and Computer Science, and a member of the National Academy of Engineering.
- Qing He: She is a Teaching Assistant and graduate student in the MIT Department of Electrical Engineering & Computer Science.
- Jimmy Li: He is a Teaching Assistant in the MIT. His research focused on applying the tools taught in this and related courses to problems in marketing.
- Jagdish Ramakrishnan: He is a Teaching Assistant in the MIT. His dissertation focused on optimizing the delivery of radiation therapy cancer treatments dynamically over time.
- Katie Szeto: She is a Teaching Assistant and her Master’s thesis explored applications of probabilistic rank aggregation algorithms.
- Kuang Xu: He is a Teaching Assistant and his research focused on the design and performance analysis of large-scale networks, such as data centers and the Internet, which involve a significant amount of uncertainties and randomness.
Suggested Reading
Basic knowledge of college-level calculus (single-variable and multivariable)
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“Analyzing and Visualizing Data with Excel”
Conclusion
- Once you will complete this course you will enable to apply the tools of probability theory to real-world applications or your research and you also learn the basic structure and elements of probabilistic models, probabilistic calculations.
- After completing the course you can buy an instructor-signed certificate with the institution’s logo to verify your achievement and increase your job prospects, give yourself an additional incentive to complete the course
Detailed Information
For more information about the course, you can check the given link: https://www.edx.org/course/introduction-probability-science-mitx-6-041x-2#!
