Course Outline
- Introduction
-
Data Analytics Overview
• Examples of Data Analytics Applications
• Initiating Data Interpretation
• Utilizing Basic Statistics for Data Analysis
• Visualizing Data with Charts -
Comparing R and Python for Data Analysis
• Evaluating the Use of R versus Python in Data Analysis -
Setting Up the Working Environment
• Preparing to Write Code
• Writing Data from R to a File
• Configuring the Working Environment
• Downloading and Setting Up R and RStudio - Ensuring the Environment Functions Properly -
Data Summary and Observations
• Conducting Initial Data Observations
• Filtering Data for Detailed Analysis
• Modifying and Executing Provided R Scripts to Validate Results -
RMarkdown
• Utilizing R Markdown
• Updating the RMD File per Your Environment and Validating Execution -
Statistical Measures
• Applying Statistical Measures -
Creating Plots and Charts
• Charting and Plotting Techniques
• Box Plots - Five Key Metrics
• Adapting R Scripts to Your Environment for Execution and Verification -
Correlation Analysis
• Calculating the Correlation Coefficient -
Mosaic Plots
• Constructing Mosaic Plots
• Troubleshooting Code to Ensure Chart Labels are Legible Within the Plot Area -
Pie Charts
• Creating Pie Charts
• Updating the Code to Generate Sales Pie Charts for Segments within the Same Dataset -
Scatter Plots
• Generating Scatter Plots
• Using Provided R Scripts to Update and Create Scatter Plots of All Variables -
Line Graphs
• Creating Line Graphs
• Selecting the First 20 Rows of the Dataset, Updating the R Script, and Executing -
Q-Q Plots
• Quantile-Quantile (Q-Q) Plots
• Modifying the R Script to Generate Q-Q Plots for Discounts -
Python Environment Setup
• Configuring the Python Environment
• Adding Comments to the Python Code (Data_Summary.py)
• Using VS Code IDE to Run the Script
• Getting Started with Python in a Government Context
• Running the Script on Your RStudio Environment; Updating as Needed -
Python and Plotting
• Converting R Code to Python
• Handling Nulls and NAs in Python
• Plotting Techniques in Python
• Coding Bar and Histogram Plots in Python Based on Previous R Scripts -
Project
• Analyzing Data from the Provided Dataset - Financial Sample.xlsx
• Completing Project Work for Government Use -
Database and SQL Integration
• Database Management and Structured Query Language (SQL)
• Installing and Verifying the MySQL Database Environment
• Working with Python and SQL in a Government Context
• Installing MySQL Libraries
• Using GUI Tools for MySQL Databases
• Installing DB Visualizer
• Executing Queries with Python and MySQL
Requirements
A working knowledge of computers and software, along with a foundational understanding of mathematics and statistics, is required. Prior experience in programming is beneficial but not mandatory. This training is suitable for both technical and business professionals who have an interest in expanding their skills for government use.
Testimonials (3)
Hands-on examples allowed us to get an actual feel for how the program works. Good explanations and integration of theoretical concepts and how they relate to practical applications.
Ian - Archeoworks Inc.
Course - ArcGIS Fundamentals
All the topics which he covered including examples. And also explained how they are helpful in our daily job.
madduri madduri - Boskalis Singapore Pte Ltd
Course - QGIS for Geographic Information System
The thing I liked the most about the training was the organization and the location
