Other courses and community
01 Community courses
These stand-alone courses in advanced ML topics are aimed at DS professionals with extensive pre-existing knowledge and focus on state-of-the-art tools and developments often unavailable in academic courses and online learning platforms. These courses are offered free, for the enrichment and benefit of ML community, and provide in-depth understanding of high-level topics, combining theoretical foundations and practical knowledge taught from a professional point of view.
Causal InferenceLecturer: Daniel Nevo
It is well known that correlation does not imply causation. However, often the main interest lies in the impact of an intervention. Furthermore, with the increase in the amounts of data collected, issues concerning systematic bias become more and more important in modern data science. In this course, we will first define what causal effects are, and then present a reservoir of causal inference methods to estimate these effects accompanied by real-life examples. We will also learn about basic terms as confounding and selection bias, and how to identify their potential presence and adapt our analysis plan using directed acyclic graphs
Advanced Time SeriesLecturer: Gleb Ivashkevich
The course is dedicated to deep learning techniques in time series modelling. Deep learning models offer significant advantages when applied to time series problems, regardless of the task (regression, classification, etc). The course is centered around a set of seminal research papers, several datasets and known use cases. Since there exists no well-established curriculum in the domain of deep learning for time series, we'll use this unique structure to bring together academic innovation and industry experience.
Adversarial learningLecturer: Ziv Katzir
Adversarial machine learning (AL) is a relatively new and extremely active research domain, focused on understanding the susceptibility of machine learning algorithms to misleading inputs.This course is a journey through the evolution of adversarial machine learning in recent years. It starts with the early methods of attack and defense, and concludes with recent discoveries and outstanding research questions. As part of this journey we will review notable studies, and discuss their contribution to the understanding of this phenomenon.
Practical crowdsourcing for efficient MLLecturer: Artem Grigorev
Quality labeled data is critical for machine learning. Our online Y-DATA community course will show you how to take control of your data labeling.
02 Practicum by Yandex
Practicum is a fully online bootcamp for self-driven individuals who want to enter the tech market. This is an online education service that allows a wide range of people to learn a new and high-demand profession from scratch. Practicum comes with 24/7 community support and mentoring, and takes a practical and project-based approach including continuous hands-on use of the studied technologies.
Collect, analyze and visualize data. Over the course of this 7-month program, you will master the skills required to become a data analyst and build a portfolio of projects on topics such as these: User preferences for streaming on-demand media, the effect of weather on taxi services and boosting e-commerce revenue
Engage in front-end development as well as back-end basics over the course of this 10-month program. Set out over a series of two-week sprints and consisting of around 20 hours of work a week, and build a solid foundation of knowledge, skills, and portfolio items required to become an entry-level web developer.
03 Data Skills
Data skills courses are a short and intensive mini-bootcamp, aimed at tech professionals with diverse experience and some coding background, but no previous DS experience. These courses aim to provide an opening to the worlds of Data Science and Artificial Intelligence by offering an entry-level perspective on a wide range of DS and ML topics. The course provides hands-on experience with multiple common DS tools, offering practical experience and understanding of core ML tasks such as classification, regression, and clustering, as well as overview of the capabilities of Deep Learning and state-of-the-art developments
Intro to Data Science
This course aims to provide an opening to the world of Data Science by offering an entry-level perspective on a wide range of DS and ML topics. The course provides an introduction and hands-on experience with multiple common DS tools, as well as understanding of core concepts of modelling and working with data. Over the course of 6 weeks, Intro to DS course will provide practical experience and understanding of core ML tasks such as classification, regression, and clustering, as well as overview of the capabilities of Deep Learning and state-of-the-art developments. The course lays the groundwork for anyone interested in the field or looking to get started by introducing and exploring the fundamental concepts behind data science and the data industry.
04 Academic courses
These are introductory academic courses on ML topics as part of undergraduate studies offered by Y-DATA faculty and based on our expertise in teaching DS topics with applicable tools and skillset. The courses allow students of not computer/engineering degrees to gain basic understanding of DS tools and their potential, as well as providing understanding of key data manipulation skills for research and hypothesis testing. The courses combine practice of common DS tools with glimpses into the full potential of ML
Introduction to Data Science for Product ManagementLecturer: Adir Solomon
Data is quickly becoming our world’s most valuable commodity. As its importance grows, it’s never been more important to explore, understand and communicate data concepts as part of one’s daily work. This course offers the necessary tools to use data to its full potential and to obtain insights needed from the position of a product manager. The course covers 4 separate modules across 3 verticals: product, management, and data science.
Y-DATA leads an ongoing meetup series open for anybody who is interested in the technical aspects of Data Science and Machine Learning. The main goal of these events is to build stronger academia-industry relationships and to make our ecosystem richer by bringing together top names from the tech industry in Israel and abroad and building a community around the opportunity to meet in an informal setting and engage in exchange of ideas as well as build up professional networks.
Understanding the Junior ParadoxDate: Wednesday, May 25, 2022 6:30 PM
What is stopping juniors from getting in jobs and gain experience? Why do employers stuck without candidates? How does it look from a tech employer's perspective? What opportunities are within this situation? Welcome to the Junior Paradox.
Y-DATA#21: Training Data—The Overlooked Area of Modern AI
The era of modern AI started with the rise of big data. Once you have large amounts of logged structured data, be it clicks on the products in an online store, or time spent on a certain webpage in a browser, or percentage of paid credits in a bank, data science steps in. However, in reality, the data is often either not structured or, even worse, does not exist at all. For example, a voice assistant will only learn to correctly activate after the model analyses thousands of hours of speech recordings made by different voices, accents, amidst surrounding noises. Further, a search engine will only learn how to rank the most relevant sites on top after “seeing” millions of pairs matching user queries and web pages documents, judged by the relevance of the match. All the magic and power of artificial intelligence has a natural glass ceiling. And this ceiling is training data.
Y-DATA#19: DS career paths: advancing beyond your first roleDate: Wednesday, June 23, 2021 6:30 PM
As more and more data professionals are reaching this career milestone, with a few years of industry experience as data scientists and looking for the next step, we invite you to join this meetup to take a look at varied perspectives on the subject. We'll feature a panel discussion by a team of talented industry insiders, who come from different backgrounds and are taking different trajectories into the future.
Y-DATA#18: ML Methods for Video AnalysisDate: Wednesday, March 17, 2021 6:00 PM
Two great talks about video analysis in real life and recent ML methods used in this field. Our speakers will be Alex Rav-Acha (VP Engineering at Vimeo) and Sergey Ovcharenko (Yandex)