Data science is a set of fundamental principles that support the extraction of information and knowledge form data. It involves concepts for extraction of knowledge via the analysis of data using techniques from various fields such as statistics, machine learning and data mining. This two-day workshop concentrates on the fundamental concepts of data science and engineering. The goal for this course is 2-fold: (1) to be able to approach business problems using analytical data- approach and (2) to competently interact on the topic of data science for business analytics. Enterprises nowadays adopt a data-driven decision-making (DDD) methods which refer to the practice of basing decisions on the analysis of data, rather than purely on intuition. Moreover, data science is an essential component for companies in the high-tech domain which are engaged in the development of data products and data services.
The course will present basic concepts and algorithms require to communicate in a data-driven environment.
Additional information is provided in appendices to extend the learning experience after the course has been completed.
During the workshop there will be master classes:
1. Case study “How to organize a Data Science division in the company” will be shown on a personal example by Oleg Voloshko, Head of the Big Data analytical products department at Kyivstar.
2. Software engineer Boris Trofimov will come from Sigma Software and will reveal in more detail the direction of “Data Engineering”
3. Yaroslav Nedashkovsky, System Architect at SoftElegance, will tell how you should implement Big Data and Data Science in different areas on his own experience
4. Igor Uspenev, who has 10 years of experience in Data Science and is a Lead Software Engineer at GlobalLogic, and Igor Tanenko, who has been involved in Deep learning solutions and projects for ADAS systems in recent years, will tell about the SLAM method (simultaneous localization and construction maps)
5. Alexander Popovich will introduce the audience to the world of Deep learning: What’s under the hood for Computer Vision. Alexander specializes in spiking and deep neural networks, holds the position of Machine Learning Engineer at GlobalLogic.
This course is intended for IT developers, digital marketers, CTO and business analysts taking their first steps with data science, data mining and machine learning and provides them with the skills required for becoming a productive data scientist in that environment.
The curriculum includes topics such as data mining algorithms and techniques. The course is suitable for people planning to engage in data science and big data analytics projects.
This course is designed for people with engineering/scientific academic background and with soft skills in programming and statistics.
The course doesn’t not include programming tasks.
To attend Workshop requires a level of English proficiency not lower than Intermediate.
Dr. Elan Sasson
- holds a Ph.D. in information systems - titled ‘Modeling Technology Assessment via Knowledge Maps: Text Mining and Temporal Trend Detection on the Internet’.
- an adjacent Lecturer in Tel Aviv University Recanati School of Management (MBA) and in the Engineering faculty (M.Sc.). Teaching courses in big data analytics, data science, and machine learning.
- member of the steering committee at the Tel Aviv University – the laboratory of AI, machine learning, and analytics.
- Hi-tech entrepreneur and a well-rounded executive with 27 years of experience in development of IT/IS products, software projects as well and managing start-ups in Israel and abroad.
- currently serves on the boards of several Hi-Tech companies and he is the CEO of Data Science Group in Israel.
Has defended his Ph.D. on the physico-mathematical sciences, became interested in data analysis and machine learning.
As a Data Scientist had experience in the banking and telecom spheres, implemented the projects in the field of credit scoring, fraud detection, marketing, pricing.
At Kyivstar, leads the direction of data products development for internal and external customers.
- 16 years of experience in IT
- Expert Big Data and Java / Scala technologies
- co-organizer and speaker of Odessa Java and Mobile User Groups
- Lecturer in ONPU
- More than 5 years of experience in the organization and processing of large data, as well as the date of platforms for companies such as Verizon / AOL and Collective with incoming rates> 1M events per second
- 10 years of experience as a team leader on projects
- Permanent speaker at major Ukrainian conferences
- area of interest: clouds, processing large amounts of data, event sourcing
System Architect in SoftElegance
Got Master’s degree in Computer Science and Software Engineering in 2008 from National Technical University of Ukraine, however made the first steps in software development around 2004.
Since 2011 works in SoftElegance.
He has a profound experience in building various successful SaaS solutions, data lake, mostly specialized in distributed system, IoT, and Big Data.
From 2015 works as a System and Data Architect, and is a speaker at the following Data Science and Data Engineering conferences: Spark Summit Europe, AI & Big Data Day, AI Ukraine.
Alexander trained in France, where he studied Kohonen self-organizing maps and spyware neural networks.
An expert in deep convolutional neural networks and their application in the computer vision.
Works in GlobalLogic Computer vision at the position of Machine learning Engineer.
The sphere of interests includes computer vision, neuroinformatics, neurocomputers.
Alexander has 2 years of experience in the field deep learning.
Igor Uspeniev has over 10 years of experience in data science including data mining and computer vision.
He conducts mathematical research in the analysis of nonlinear dependencies.
Igor is a Lead Software Engineer, Consultant, GlobalLogic, and at his current position he develops approaches in SLAM in environmental recognition.
Ihor Tanenkov has over 7 years of experience IT, mostly with computer graphics and computer vision.
During the last years he has been working with deep learning solutions for a wide variety of tasks, including automotive development, navigation and robotics.
He developed several projects for ADAS systems with traffic signs recognition, license plates recognition and lane tracking.
- Basic methodology
- CRISP DM – the journey from business understanding to model deployment )
- Data Understanding and Engineering
- Data integration
- Data transformation
- Exploratory Data Analysis (EDA)
- Descriptive analytics and statistics
- Basic statistical inference and measures of uncertainty and irregular cardinality
- Data pre-processing
- Data normalization
- Data cleaning
- Data reduction (PCA, ICA, and more)
- Outlier detection and analysis
- Data imputation
- Binning and discretization methods
- Matrix decomposition algorithms – PCA and ICA
- Predictive analytics and classification: Supervised Learning
- Linear and logistic regression
- Decision trees
- Ensemble models (Random Forest, Bagging and Boosting)
- SVM (support vector machine)
- KNN (K-nearest neighbors)
- Neural networks
- Clustering (from k-means to hierarchical clustering) – Unsupervised Leaning
- Hierarchical clustering
- Density-based methods
- Introduction to Deep Learning and Reinforcement Learning