Workshop:Data Science Essentials - methodologies and practical issues

Workshop:Data Science Essentials – methodologies and practical issues

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12 480 UAH

About workshop

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.

For whom:

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.

About speakers:


  • 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
    • K-Means
    • K-Medoids
    • Hierarchical clustering
    • Density-based methods
  • Introduction to Deep Learning and Reinforcement Learning


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