Probability for Data Science

PROBABILITY FOR DATA SCIENCE

  • Instructor: Anand Seetharam
  • Open year round
  • Delivery: Self-paced online, about 3 hours of video lectures in addition to self-assessment quizzes (not graded) and final exam (graded).
  • Credentials: The students who successfully complete the course by passing the final exam will receive the Probability for Data Science digital badge and will be able to print a °®¶¹´«Ã½ issued course completion certificate.
  • Who can take this course: This course is open to all engineers, professionals, faculty and students.

ABOUT THE COURSE

Probability is a key mathematical concept that is essential for modeling and understanding computer system performance and real-world data generated from day-today activities and interactions. Data science, machine learning, natural language processing and computer vision rely heavily on probabilistic models.

This introductory course in probability is designed to provide the necessary background for learning and understanding machine learning and data science concepts. It will introduce the concept of probability, provide an overview of discrete and continuous random variables and describe how to compute expectation and variance. The course will also discuss specific distributions such as geometric, binomial, Poisson, uniform, exponential and normal distributions.

LEARNING OUTCOMES

At the end of the course, course participants will: 

  • Be able to describe the basic probability concepts such as mean, variance, conditional probability, Bayes rule and statistical independence.
  • Be able to compute the mean and variance of random variables.
  • Be able to describe discrete and continuous distributions such as geometric, binomial, Poisson, uniform, exponential and normal.
  • Be able to compute the derive properties of functions of random variables.
  • Be able to understand how real-world phenomena can be modeled using probability distributions.

ABOUT THE INSTRUCTOR

Anand Seetharam is an assistant professor in computer science in the Thomas J. Watson College of Engineering and Applied Science at °®¶¹´«Ã½. Dr. Seetharam is broadly interested in the field of computer networking. His research interests encompasses wireless networks, information-centric networks, ubiquitous computing, Internet of Things (IoT) and smart grids. 

COURSE FEES

  • $250: Standard/Industry rate (Group rates available, see below)
  • $150: BU and SUNY faculty/Staff and BU Alumni graduated May 2020/Non-SUNY students
  • $105: Non-BU and SUNY students (must give evidence of matriculation at other University/College)
  • $95: BU and SUNY Students and recent BU Alumni graduated Dec. 2020 or after/High School students
  • $35: Retake fee Students (requires proof of previous registration)
  • $50: Retake fee Non-Students (requires proof of previous registration)
Industry Group rate: 3-5 people from the same organization: $225 per person. Contact wtsnindy@binghamton.edu or 607-777-6251 for promo code to use when you register.

PAYMENTS

Payment is made at the time of registration. For questions, contact the Office of Industrial Outreach at 607-777-6251 or email wtsnindy@binghamton.edu.

CANCELLATIONS AND REFUNDS

Please note our cancellation and refund policy: All cancellations must be received in writing (email) to the Office of Industrial Outreach. All refunds will be assessed a 10% administrative fee. No refunds for cancellations or non-attendance will be given after you have started the course.  Submit your cancellation request to EMAIL: wtsnindy@binghamton.edu.

If the course is canceled, enrollees will be advised and receive a full refund.