Basic components of retail sales data
Retail sales data analysis facilitates the better understanding of customer behaviour and improve sales performance. Understanding of historical trends and patterns of sales data requires metrics such as total sales, sales by product category revenue by region, average transaction value, and units per transaction. Further, customer segmentation analysis requires metrics such as customer lifetime value (CLV), recency of transaction (RFM) analysis and frequency of purchases analysis. Machine learning (ML) and statistical tools enable product performance analysis, predictive and perspective analysis.
Exploring Python Pandas
Pandas, an open-source data analysis library for Python, was developed in 2008 and released in 2010. Pandas library has many query features in Python, applicable to a wide range of data including financial data and environmental data. The short video illustrates the application of Python Pandas to analyse hydrological data.
Understanding statistical functions in R and Python basics
Exploratory data analysis and preliminary statistical analysis require basic statistical functions. R and Python contain various packages for specific analysis and Python libraries such as pandas and NumPy contain statistical functions. Below is the collection of basic statistical functions in R and Python.
Calculating columns in R explained
R and dplyr libraries can help calculate column values. The following example shows the mutate() function to calculate discharge (cubic meter per second) for different sites with simulated velocity(square meter) and area (meter) data.
Explore Plotly Essentials
Plotly library contains effective tools to visualise the data. Time series data for various categories can be depicted with animation bar charts. Codes can be used for the data with various categories in one column and values in another. The animated bar chart below presents the life expectancy values of the selected countries with y-axis values ranging from 0 to 100.