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Course Outline
- Introduction
-
Data Analytics Overview
• Illustrative Examples of Data Analytics
• Fundamentals of Data Interpretation
• Application of Basic Statistical Methods for Analysis
• Utilization of Visualizations for Data Insight -
R and Python Comparison
• Comparative Analysis: R Versus Python for Analytical Tasks -
Operational Environment Setup
• Preparation of the Coding Workspace
• Exporting Data from R to File Formats
• Configuration of the Working Environment
• Installation and Verification of R and RStudio for Operational Readiness -
Data Summarization and Observation
• Initial Data Inspection
• Data Filtering Techniques for Observations
• Modification and Execution of Provided R Scripts to Validate Results -
RMarkdown Documentation
• Overview of R Markdown
• Execution and Validation of the RMD File Following Environment-Specific Updates -
Statistical Metrics
• Application of Statistical Measures -
Visualizations and Charts
• Techniques for Charting and Plotting
• Construction of Box Plots Utilizing Five Key Metrics
• Adaptation, Execution, and Verification of R Scripts Within the Local Environment -
Correlation Analysis
• Determination of Correlation Coefficients -
Mosaic Plot Construction
• Construction Methodology for Mosaic Plots
• Troubleshooting Code to Ensure Label Legibility Within Designated Areas -
Pie Chart Generation
• Pie Chart Formatting
• Coding Adjustments to Generate Sales Pie Charts for Specific Segments Within the Dataset -
Scatter Plot Analysis
• Scatter Plot Methodology
• Modification and Execution of R Scripts to Visualize Correlations Among All Variables -
Line Graph Construction
• Line Graph Formatting
• Implementation of a Subset Containing the First 20 Rows for Script Execution -
Q-Q Plot Application
• Understanding Quantile-Quantile (Q-Q) Plots
• Coding Adjustments to Generate Q-Q Plots Specifically for Discount Metrics -
Python Operational Setup
• Configuration of the Python Environment
• Addition of Documentation Comments to the Data_Sumamry.py Script
• Execution of Scripts via the VS Code Integrated Development Environment (IDE)
• Fundamentals of Python Programming
• Adaptation and Execution of Scripts Within the RStudio Environment as Required -
Python Data Visualization
• Translation of R Code to Functional Python Code
• Handling of Null Values and Missing Entries (NAs)
• Visualization Techniques in Python
• Development of Bar and Histogram Plots in Python Based on Previous R Script Logic -
Capstone Project
• Data Analysis of the Financial Sample.xlsx Dataset
• Capstone Project Implementation -
Database Management and SQL
• Introduction to Databases and Structured Query Language (SQL)
• Installation and Verification of MySQL Database Infrastructure
• Integration of Python with SQL Systems
• Installation of Necessary MySQL Library Dependencies
• Utilization of Graphical User Interface (GUI) Tools for MySQL Management
• Installation of DB Visualizer Application
• Interfacing Python with SQL Queries
• Execution of Database Queries Using Python and MySQL for government data processing needs
Requirements
Participants require foundational proficiency in computing systems and applications, along with elementary competency in mathematics and statistics. Previous experience in software development is advantageous. This opportunity is designed for both technical practitioners and business stakeholders who are motivated to expand their expertise for government sectors.
14 Hours
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Equipped with examples