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Course Outline
- Introduction
-
Data Analytics Overview
• Examples of Data Analytics Applications
• Initiating Data Interpretation
• Utilizing Basic Statistics for Data Interpretation
• Employing Charts for Data Visualization -
R and Python in Data Analysis
• Comparison of R and Python for Data Analysis -
Setting Up the Working Environment
• Preparing to Code
• Writing Data from R to a File
• Configuring the Working Environment
• Downloading and Installing R and RStudio; Ensuring Proper Environment Functionality -
Summarizing and Observing Data
• Conducting Data Observations
• Filtering Data for Specific Observations
• Modifying and Executing Provided R Scripts to Obtain Results and Verify Accuracy -
RMarkdown for Documentation
• Utilizing R Markdown
• Updating the RMD File per Your Environment, Executing, and Validating Results -
Statistical Measures
• Statistical Measurement Techniques -
Data Visualization: Plots and Charts
• Charting and Plotting Data
• Box Plots - Five Key Metrics
• Updating R Scripts per Your Environment, Executing, and Verifying Results -
Correlation Analysis
• Calculating the Correlation Coefficient -
Mosaic Plots for Categorical Data
• Constructing Mosaic Plots
• Troubleshooting Code to Ensure Legible Chart Labels within the Plot Area -
Pie Charts for Proportional Representation
• Creating Pie Charts
• Updating Code to Generate Sales Pie Charts for Segments within the Same Dataset -
Scatter Plots for Variable Relationships
• Generating Scatter Plots
• Using Provided R Scripts to Update and Create Scatter Plots of All Variables -
Line Graphs for Trend Analysis
• Creating Line Graphs
• Selecting the First 20 Rows of the Dataset, Updating the R Script, and Executing -
Quantile-Quantile (Q-Q) Plots for Distribution Comparison
• Generating Q-Q Plots - Quantile-Quantile Plots
• Updating the R Script to Create a Q-Q Plot for Discounts -
Setting Up the Python Environment
• Configuring the Python Environment
• Adding Comments to the Python Code (Data_Summary.py)
• Running Scripts Using VS Code IDE
• Getting Started with Python for Data Analysis
• Adapting and Executing Provided Scripts in RStudio -
Python for Plotting
• Converting Working R Code to Python Code
• Handling Nulls and NAs in Python
• Data Visualization with Python
• Adapting R Scripts to Create Bar and Histogram Plots in Python -
Project: Analyzing a Financial Dataset
• Analyzing the Provided Dataset - Financial Sample.xlsx
• Project Deliverables -
Database Management with SQL
• Database and Structured Query Language (SQL) Basics
• Installing MySQL Database and Verifying Environment Setup
• Integrating Python with SQL for Data Analysis
• Installing Required MySQL Libraries
• Using a GUI Tool for MySQL Database Management
• Installing DB Visualizer
• Executing Queries in Python with MySQL Database
Requirements
A foundational understanding of computers and software, along with basic knowledge of mathematics and statistics, is required. Previous programming experience is beneficial but not mandatory. This training is suitable for both technical and business professionals who are interested in enhancing their skills for government and other sectors.
14 Hours
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
