Lecture 2: Tools and Technologies in BI
Illya Mowerman, Ph.D.
Introduction
Business Intelligence (BI) relies on various tools and technologies to analyze and visualize data.
This lecture introduces key BI tools:
R, R-Studio, Tableau, Power BI, and Python
.
We will compare their applications and learn how to set up a development environment.
Introduction to R
R
is a programming language for statistical computing and data visualization.
Strong libraries for data analysis:
ggplot2, dplyr, tidyr
.
Popular in academia and data science for statistical modeling.
Introduction to R-Studio
R-Studio
is an Integrated Development Environment (IDE) for R.
Features:
Script editor, console, and environment window.
Built-in visualization and debugging tools.
Easy package management.
Helps in organizing and running R scripts efficiently.
Introduction to R/Shiny
Shiny
is an R package that allows users to build interactive web applications using R.
Enables the creation of interactive dashboards and data-driven web apps.
No need for HTML, CSS, or JavaScript knowledge.
Used in BI for creating user-friendly interfaces for data analysis and reporting.
Python-Based Alternatives to Shiny
Python frameworks that provide similar functionality to Shiny:
Dash (by Plotly)
: Builds interactive web applications.
Streamlit
: Lightweight framework for rapid data-driven applications.
Panel (by HoloViz)
: Supports multiple visualization libraries.
Enables Python developers to create web applications for BI and analytics.
R and Python Integration for BI Applications
R can integrate with Python-based BI tools
using:
Dash for R
: R version of Dash for interactive applications.
Reticulate
: Allows running Python code within R scripts.
Shiny for Python
: New framework bringing Shiny’s reactivity model to Python.
Introduction to Tableau
Tableau
is a data visualization tool for interactive dashboards.
Features:
Drag-and-drop interface.
Connects to multiple data sources (Excel, SQL, cloud services).
Used for reporting and data storytelling.
Introduction to Power BI
Power BI
is a Microsoft-powered BI tool for data visualization and reporting.
Seamless integration with Microsoft products.
Strong data modeling capabilities with DAX and Power Query.
Real-time dashboarding and collaboration.
Introduction to Python
Python
is a versatile programming language widely used in BI and data science.
Libraries for data analysis:
Pandas
(data manipulation)
NumPy
(numerical computing)
Matplotlib/Seaborn
(visualization)
Scikit-learn
(machine learning)
Comparison of BI Tools
Feature
R
Tableau
Power_BI
Python
Coding Required
Yes
No
No
Yes
Best For
Statistical Analysis
Data Visualization
Business Analytics
General Data Science
Learning Curve
Moderate
Easy
Easy
Moderate
Enterprise Use
Medium
High
High
High
Open Source
Yes
No
No
Yes
Comparison of R and Python for BI
Feature
R
Python
IDEs
R-Studio
Jupyter, PyCharm, VS Code
Web Apps
Shiny
Dash, Streamlit, Panel
Data Analysis
Tidyverse
Pandas, NumPy, Matplotlib
Machine Learning
Caret, mlr3, xgboost
Scikit-learn, TensorFlow, PyTorch
Comparison of Web App Frameworks
Feature
Shiny_R
Dash_Python
Streamlit_Python
Panel_Python
Ease of Use
Moderate
Moderate
Easy
Moderate
Interactivity
High
High
High
High
UI Customization
Moderate
High
Low
High
Best For
Data Science Apps/BI Dashboards
BI Dashboards
Quick Prototyping
Data Science Apps
Learning Curve
Moderate
Moderate
Low
Moderate
Open Source
Yes
Yes
Yes
Yes
Introduction to Code Repositories
GitHub, GitLab, Bitbucket
allow version control, collaboration, and deployment.
Essential for maintaining and managing BI development projects.
Introduction to Docker
Docker
enables containerization, allowing applications to run consistently across environments.
Eliminates dependency conflicts and enables easy deployment.
Introduction to Jenkins
Jenkins
is an automation server for continuous integration and deployment (CI/CD).
Automates software build, test, and deployment processes.
Streamlines BI workflows and ensures code reliability.
Summary
Choosing the right BI tool depends on project needs.
Python and R offer strong BI capabilities with different strengths.
Interactive web apps can be built using R/Shiny or Python frameworks.
Code repositories, Docker, and Jenkins enhance BI project management.
Q&A
Open floor for questions and discussions.
