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 Duration 14 hours

Course Outline

  • Introduction
  • Foundations of Data Analytics
    • Practical examples of data analytics for government
    • Initial methods for interpreting data
    • Application of basic statistics in data interpretation
    • Utilization of charts for data interpretation
  • R and Python Programming
    • Comparative use of R versus Python for data analysis in public sector contexts
  • Development Environment Setup
    • Preparing the coding environment
    • Writing data from R to file formats
    • Configuring the working environment for government applications
    • Installation and validation of R and RStudio to ensure operational readiness
  • Data Summary and Observations
    • Generating data observations for accountability and reporting
    • Filtering data to focus on relevant government metrics
    • Executing provided R scripts to modify, run, and verify results
  • RMarkdown Documentation
    • Creating reports using R Markdown
    • Executing and validating RMD files after adapting them to the specific government environment
  • Statistical Measures
    • Calculation and interpretation of core statistical measures
  • Data Visualization: Plots and Charts
    • Techniques for charting and plotting for public communication
    • Construction of Box Plots highlighting five key metrics
    • Updating and executing R scripts to verify visual outputs
  • Correlation Analysis
    • Calculation and interpretation of Correlation Coefficients
  • Mosaic Plots
    • Construction of Mosaic Plots for categorical analysis
    • Troubleshooting code to ensure legible chart labels within the designated area
  • Pie Charts
    • Techniques for Pie Chart creation
    • Updating code to generate Sales Pie Charts for segments within the same dataset
  • Scatter Plots
    • Techniques for Scatter Plotting
    • Utilizing provided R scripts to update and generate scatter plots for all variables
  • Line Graphs
    • Construction of Line Graphs
    • Executing updated R scripts using the first 20 rows of the dataset
  • Q-Q Plots
    • Generation of Quantile-Quantile (Q-Q) Plots
    • Updating R scripts to produce Q-Q plots for Discounts
  • Python Environment
    • Configuration of the Python development environment
    • Adding comments to Python code (Data_Summary.py) for documentation purposes
    • Executing scripts using the VS Code IDE
    • Foundational steps for starting with Python
    • Running scripts in the RStudio environment and updating as necessary
  • Python and Plotting
    • Translating working R code into Python
    • Handling Nulls and NAs in Python
    • Implementation of plotting functions in Python
    • Generating bar charts and histograms in Python based on previous R scripts
  • Capstone Project
    • Comprehensive data analysis of the provided dataset - Financial Sample.xlsx
    • Execution of project deliverables for government stakeholders
  • Database and SQL Integration
    • Overview of Databases and Structured Query Language
    • Installation of MySQL and verification of the government-compliant environment
    • Initiating workflows with Python and SQL
    • Installation of required MySQL libraries
    • Utilization of GUI tools for MySQL database management
    • Installation of DB Visualizer
    • Integration of Python with SQL queries
    • Executing queries using Python with the MySQL database

Requirements

Working knowledge of computers and software, along with basic proficiency in mathematics and statistics. Prior programming experience is beneficial. The course is suitable for both technical and business professionals with an interest in learning data analytics for government applications.

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