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Course Outline
- Introduction
-
What is Data Analytics
• Examples of Data Analytics
• Starting to interpret the data
• Using basic stats to interpret the data
• Using charts to interpret the data -
R and Python
• Comparison of R vs Python for Data Analysis -
Working Environment
• Getting Ready to Code
• Writing Data from R to a File
• Preparing the Working Environment
• Download and set up R and RStudio - ensuring the environment is functional -
Getting Data Summary and Observations
• Data Observations
• Data Observations - Filtering the Data
• Use the provided R scripts to modify, execute them to get the results, and verify -
R Markdown
• R Markdown • Use the RMD file to execute after updating it for your environment, and validate. -
Statistical Measures
• Statistical Measures -
Plots and Charts
• Charting and Plotting
• Box Plots - five metrics
• Update the R scripts for your environment, execute, and verify. -
Correlation
• Correlation Coefficient -
Mosaic Plots
• Mosaic Plot Construction
• Troubleshoot the code so that chart labels are legible within the area -
Pie Chart
• Pie Charting
• Update the code to get the Sales Pie Chart for the Segments within the same dataset -
Scatter Plots
• Scatter Plotting
• Use the provided R script to update and generate scatter plots for all variables. -
Line Graph
• Line Graph
• Consider taking the first 20 rows of the dataset, update the R script, and execute -
Q-Q Plots
• Q-Q Plots - Quantile-Quantile plots
• Update the R script to get the Q-Q plot for Discounts -
Python Environment
• Python Environment • Add comments to the Python code (Data_Summary.py)
• Use VS Code IDE to run the script
• Getting Started with Python
• Use the script to run on your RStudio environment; update the script as needed -
Python and Plotting
• Working Python code derived from R Code
• Handling Python Nulls and NAs
• Plotting in Python
• Python code for bar and histograms based on R scripts from previous sections -
Project
• Analyze the data for the given dataset - Financial Sample.xlsx
• Project Work -
Database and SQL
• Database and Structured Query Language
• Install MySQL database and verify your environment
• Getting to work with Python plus SQL
• Install MySQL libraries
• GUI tool for MySQL database
• Install DB Visualizer
• Using Python with SQL
• Python with MySQL database for running queries
Requirements
Working knowledge of computers and software, along with basic knowledge of math/statistics. Prior programming knowledge is helpful. The course is suitable for both technical and business professionals interested in learning.
14 Hours
Testimonials (2)
Doing Exercise
Joe Pang - Lands Department, Hong Kong
Course - QGIS for Geographic Information System
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.
