Dr. Kira Radinsky is a chairman and CTO of Diagnostic Robotics where the most advanced technologies in the field of artificial intelligence are harnessed to make healthcare better, cheaper, and more widely available. She also co-founded SalesPredict, acquired by eBay in 2016, that was the leader in the field of predictive marketing building solutions leveraging large-scale data mining to predict sales conversions.
One of the up-and-coming voices in the data science community, she is pioneering the field of medical data mining. Dr. Radinsky gained international recognition for her work at Microsoft Research, where she developed predictive algorithms that recognized the early warning signs of globally impactful events, including political riots and disease epidemics. In 2013, she was named to the MIT Technology Review’s 35 Young Innovators Under 35, in 2015 as Forbes 30 under 30 rising stars in enterprise technology, and in 2016 selected as "woman of the year" by Globes.
Dr. Radinsky also serves as visiting professor at the Technion, Israel’s leading science and technology institute, where she focuses on the application of predictive data mining in medicine.
Lecturer at Causal Inference course
Lecturer at ML foundations
Lecturer at Supervised Learning course
Lecturer at Generative AI course
Lecturer in Supervised Learning course
Noa is a Data Scientist, currently working as Director of Data Science at Fido. Formerly worked as a researcher at NASA, at Diagnostic Robotics, Amazon, Elbit and IAI. Her main topics of specialisation and focus are NLP, healthcare and space.
Volunteering as CTO at Tod’aers - combining AI and space research for sustainable technological developments.
Electrical Engineering Bachelor's degree, Technion (Summa Cum Laude).
Lecturer at ML foundations
Lecturer at Probability & Statistics
Lecturer at project workshops
Lecturer at Generative AI course
Project mentor
Project mentor
Lecturer at MLops course
Project mentor
Project mentor
Project mentor
Project mentor
Project mentor
Project mentor
Project mentor
Project mentor
The application process consists of three steps. First, candidates apply online through our website. Second, candidates take an online test. The test assesses analytical and basic programming skills and contains undergraduate level statistics and probability questions, data analysis questions, and short coding challenges (candidates can select from a few popular programming languages that our platform supports). The test must be completed within three and a half hours.
Y-DATA will offer two separate 4-day time windows to take the online test during July-August. Candidates who pass the online test will be invited to an in-person interview with Y-DATA team members. The interview is an opportunity for our team to learn more about candidates' background, experiences, and interests.
We assume our students have at least a bachelor's STEM degree or its equivalent.
We, therefore, expect all the candidates to have full knowledge of the first-year university-level material in math. In order to ensure a suitable level of pre-existing knowledge, we require all candidates to complete the Mathematics for Machine Learning specialization on Coursera prior to the beginning of their studies.
Candidates who are accepted to the program will have the cost of the course deducted from their tuition fee.
We assume our students have at least a bachelor's STEM degree or its equivalent.
We, therefore, expect all the candidates to have full knowledge of the first-year university-level material in probability and statistics. You may want to review basic topics in those subjects before taking our online test.
During the program, we won’t teach these topics from scratch, but we will provide a quick recap before diving into the more advanced topics required for later ML courses.
We require some experience in at least one of the common programming languages and an understanding of common data structures. Programming tasks are a large portion of the online test and assume existing programming background. During the program itself, the courses and home assignments will be in Python.
Candidates should have a basic knowledge of Python before taking the test. Some potential resources for people with no prior Python knowledge to get started include:
The weekly workload consists of 9 hours of frontal lessons (one mid-week evening + Friday morning) and approximately 20 hours of independent work on assignments and projects. We expect at least 80% attendance at lectures and seminars.
Due to the workload, in addition to the 9 hours of frontal lectures, we require our participants to have at least one full weekday available for work on classwork and industry project. Therefore, candidates are required to reduce full-time positions to 80% at most, and we strongly recommend reduction to 50% for the program's duration.
In some exceptional cases and based on individual assessment, we may allow select students to take the program without an industry project while maintaining a full-time job.
Participating in the program while working towards an academic degree is possible but depends greatly on the intensity of individual programs.
The cost of the Y-DATA program is NIS 29,000.
In order to make the program affordable, we also offer success-based model of tuition (SBE). Full cost of the program in SBE is NIS 45,000.
The program is taught in English in its entirety. We have international students and faculty, and thus expect all our students to be fluent in English.
Y-DATA classes take place in Tel Aviv, on TAU campus.