Data Science Visualization Course

Dmitry Guzenko

November 3 - 4

from 10:00 am till 6:00 pm

Software & Computer Museum

Saksaganskogo str., 40/85

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

November 3-4 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 visualization techniques
Comparison of free platforms for data visualization
Workshop: simulation of visualization based on business requirements
Practical work: developing and publishing visualizations performed using MS Power BI Desktop, QlikView Personal Edition, Tableau Desktop Public Edition
R Visualization Development Practices, ggplot libraries, ggplot2, rgl, plotly
The practice of developing Python visualization, the matplotlib library, plotly, bokeh

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

Practices and approaches for qualitative visualization. What mistakes should be avoided? How to make the data speak for itself and show the necessary insights?
What does the term Self BI mean?
How to render the visualization fast without programming and give others access to the created visualization within the organization or even beyond its borders?
How to do it with such tools, for the use of which companies would not have to pay money, or the cost would be very affordable, and the result is highly professional?

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:

MS Power BI Desktop
QlikView Personal Edition
Tableau Desktop Public Edition

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.

For whom the course was developed:

For executives and top managers who want to understand their data and develop their own reports
IT professionals, business analysts and data analysts who want to understand the capabilities, benefits and limitations of the three most popular visualization systems: Power BI, QlikView, Tableau
Data Scientists, developers who want to speed up and simplify the process of interaction with customers, respond faster to changing business requirements, spend less time on obtaining a valuable product and seek more insights with minimal cost.

Requirements for participants:

Basic knowledge of data structures
Basic knowledge of English
Skills and experience with Excel
Laptop
Implementation of the first laboratory installation work in accordance with the given instruction

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