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Course Outline
- Introduction
-
What is Data Analytics
• Examples of Data Analytics
• Starting to interpret the data
• Using basic statistics to interpret the data
• Using charts to interpret the data -
R and Python
• Comparing the use of R versus Python for data analysis -
Working Environment
• Preparing to code
• Writing data from R to a file
• Setting up the working environment
• Downloading and setting up R and RStudio - ensuring the environment functions correctly -
Getting Data Summary and Observations
• Data observations
• Data observations - filtering the data
• Using the provided R scripts to modify, execute, and verify results -
RMarkdown
• R Markdown
• Using the RMD file to execute after updating for your environment and validating the output -
Statistical Measures
• Statistical measures -
Plots and Charts
• Charting and plotting
• Box plots - five key metrics
• Updating the R scripts for your environment, executing, and verifying results -
Correlation
• Correlation coefficient -
Mosaic Plots
• Constructing mosaic plots
• Troubleshooting code to ensure chart labels are legible within the designated area -
Pie Chart
• Creating pie charts
• Updating the code to generate a sales pie chart for segments within the same dataset -
Scatter Plots
• Creating scatter plots
• Using the provided R script to update and generate scatter plots for all variables -
Line Graph
• Creating line graphs
• Considering the first 20 rows of the dataset, updating the R script, and executing it -
Q-Q Plots
• Q-Q plots - Quantile-Quantile plots
• Updating the R script to generate a Q-Q plot for discounts -
Python Environment
• Setting up the Python environment
• Adding comments to the Python code (Data_Sumamry.py)
• Using the VS Code IDE to run the script
• Getting started with Python
• Running the script in your RStudio environment and updating it as needed -
Python and Plotting
• Converting working Python code from R code
• Handling Python nulls and NAs
• Plotting in Python
• Writing Python code for bar charts and histograms based on R scripts from previous sections -
Project
• Analysing the data for the provided dataset - Financial Sample.xlsx
• Project work -
Database and SQL
• Databases and Structured Query Language (SQL)
• Installing the MySQL database and verifying your environment
• Working with Python plus SQL
• Installing MySQL libraries
• GUI tool for MySQL database
• Installing DB Visualizer
• Using Python with SQL
• Using Python with MySQL database to run queries
Requirements
A working knowledge of computers and software, along with basic understanding of mathematics and statistics. Prior programming experience is beneficial. The course is suitable for both technical and business professionals with an interest 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.
