Data Science UA > Data Science Visualization Course: 3.0

Data Science Visualization Course: 3.0

Dmitry Guzenko

April 12 — 13

from 10:00 am till 6:00 pm

iHub

10, Khreshchatyk street

Facebook

Подія

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

April 12-13 Data Analyst Dmitry Guzenko will hold a two-day Data Science Visualization course.
Dmytro has over 20 years of experience in business process automation and ERP implementation, 10 years of experience in system analysis and business model architecture, 3 years of data management expertise to improve company performance.
He has experience in solutions architecture, business analysis and can tell everything about best practices to increase investor value.

Course content

• Introduction to data visualization
• Best practices and methods for data visualisation
• Comparison of free data visualization platforms
• Workshop in groups: modeling visualization based on business requirements
• Practical work: development and publication of visualizations made using MS Power BI Desktop, Tableau Desktop Public Edition
• Practical work: development of R rendering, ggplot2 library, RGL, Plotly
• Practical work: development of Python visualization, matplotlib library, plotly, bokeh

In the course you will find the answers to the questions:

• Practices and approaches for quality visualization. What mistakes should be avoided? How to make the data speak for themselves and show the necessary business insights?
• Self BI systems overview
• How to make visualization quickly, without programming, and give others access to the generated visualization, within the organization or even beyond?
• How to make it a tool that companies would not have to pay money for or the value would be very affordable?

You will gain practical skills in Microsoft Power BI and Tableau.

Also, Data Science and machine learning projects require a deeper analysis of data that is implemented using R and Python. Part of the course is devoted to exploring some basic imaging capabilities using popular R and Python libraries.

For whom the course was developed:

• for executives and top managers who want to understand their data and design the boards and reports themselves;
• IT professionals, business and data analysts seeking to understand the capabilities, benefits and limitations of the 2 most popular imaging systems: Power BI, Tableau;
• Data Scientists, developers who want to speed up and streamline customer engagement, respond quickly to changing business requirements, spend less time getting a valuable product, and get more insights with minimal cost.

Invited speakers

[:en]nika[:]

Veronica Tamayo Flores

Head of consulting, Data Science UA

Alexandr Vasiliev

DataStream

Dmytro Patsiliandra

Chief Instrument Technician / Party Manager ,Vestland Offshore AS

Course program

April 12

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. Microsoft Power BI development of reports

• Use of additional data functionality
• Reporting using standard items
• Developing reports using advanced visual capabilities

April 13

Block 5. Publication of reports

• Publication of reports for organization employees
• Build a SharePoint Analytics Center
• Setting permissions for dashboards and reports
• Publishing reports on the Internet for common access
• Practice: Developing an analytics publication in different scenarios

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

• Statistical methods of data analysis
• Types of statistics and their interpretation
• Advantages and features of R for Data Mining
• Configure 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