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  • DATA SCIENCE VISUALIZATION COURSE

    DMITRY GUZENKO

    Data Analyst, Luxoft

    03.11-04.11
    business

    edition
    Software & Computer Museum
    Kyiv, Saksaganskogo str., 40/85

    Speaker

    Dmitry Guzenko

    Data Analyst, Luxoft

    More than 20 years of experience in business process automation & ERP systems implementation, 10 years of experience in system analysis and business models architect, 3 years expertise in Data Management approach to improve company efficiency.

    Experience in Solution Architecting, Business Analysis best practice implementation to improve valuable changes for stakeholders.

    Data analysts, top managers, executives, and Data Scientists – these categories of professionals understand the value of data visualization skills like no one else.

     


    Course content:


    At the course you will find the answer to the following questions:

     

    The course is designed to provide practical skills for all participants in the course and teach you to work with three systems: Microsoft Power BI, QlikView, and Tableau.
    These are recognized leaders in visualization. Ability to use them is a necessary skill for a modern specialist.

    The course is based on the study of officially free products available to everyone:

    Also, for Data Science and machine learning projects, a deeper analysis of data is required, which is implemented with the help of R and Python tools. Part of the course is devoted to the study of some basic visualization capabilities using popular R and Python libraries.


    This course will be interesting for:

     


    Requirements for participants:


    Course program

    November 3

    Block 1. Introduction to data visualization

    • History of visualization, examples of historically significant visualization work
    • Common architectures for data processing, analysis and visualization systems
    • Forecasting and visualization in Data Science projects
    • General requirements for visualization systems
    • Comparison of the best visualization systems
    • Typical mistakes
    • Best practices and guidelines for developing high-quality visualizations

    Block 2. Planning and implementation of data visualization projects

    • Approaches to the organization of visualization projects
    • Practice: working in groups, developing a visualization prototype according to business requirements

    Block 3. Working with Microsoft Power BI for data visualization

    • About Power BI products
    • Installation, system setup
    • Import data from various sources
    • Pre-processing of data
    • Data aggregation and model development
    • Practice: Import and pre-processing

    Block 4

    • Use of additional data functionality
    • Reporting using standard items
    • Developing reports using advanced visual capabilities
    • Publication of reports for employees of the organization
    • Publication of reports on the Internet for public access
    • Practice: publishing reports

    November 4

    Block 5. Work with the QlikView Personal Edition system for data visualization

    • About QlikView products
    • Installation, system setup
    • Import data from various sources
    • Pre-processing of data
    • Data aggregation and model development
    • Practice: Import and pre-processing
    • Use of additional data functionality
    • Reporting
    • Publication of reports on the Internet for public access
    • Practice: Developing and publishing reports

    Block 6. Working with the Tableau Desktop Public Edition system for data visualization

    • About Tableau Products
    • Installation, system setup
    • Import data from various sources
    • Pre-processing of data
    • Data aggregation and model development
    • Practice: Import and pre-processing
    • Use of additional data functionality
    • Reporting
    • Publication of reports
    • Practice: Developing and publishing reports

    Block 7. Use of R language for visual data analysis

    • Setting Power BI to use R
    • Opportunities for research using R
    • Important functions for analyzing data from ggplot libraries, ggplot2, rgl, plotly
    • Practice: build Power BI reports using language features R

    Block 8: Use Python to visualize data

    • Setting up Power BI to use Python
    • Python research capabilities
    • Important functions for data analysis from libraries matplotlib, plotly, bokeh
    • Practice: build Power BI reports using Python language features
    • Conclusion and further steps

    When: November 3-4, from 10:00 to 18:00
    Where: Software Museum, 40/85 Saksaganskogo St., Kyiv, 01033


    Tickets

    10% FROM 3 TICKETS
    15% FROM 5 TICKETS
    25% DISCOUNT FOR STUDENTS

    Unfortunately, all the tickets are sold. But you can buy a ticket to the Data Science UA conference
    2000 ₴
    till 12 October

    2500 ₴
    October 13 – October 26

    2900 ₴
    from October 27