Basic Column Analysis

Kaleidoscope

In this step, we will conduct basic column analytics to gain deeper insights into the dataset by exploring specific questions.

 

Task #1: What is the Time Range of the Dataset?

For effective data analysis and trend forecasting, understanding the time span covered by our dataset is crucial. This task aims to identify the temporal boundaries of our data by determining the earliest and latest order dates recorded in the Superstore sales data.

Objective: The goal is to find the date range of the orders in the Superstore sales data, specifically the earliest (minimum) and latest (maximum) order dates.

Expected Result: The result should display two dates: the earliest ('min_order_date') and the latest ('max_order_date') found in the 'Order_Date' column of the dataset.

Data Source: dataacademykz.superstore.sales

 

Task #2: How Many Orders Do We Have in the Dataset?

Understanding the volume of orders is a critical aspect of business analysis, as it reflects the scale of operations and customer engagement. This task focuses on quantifying the total number of orders placed, as captured in the Superstore sales data.

Objective: The task is to determine the total number of distinct orders that have been placed, as recorded in the Superstore sales data.

Expected Result: 5,009

Data Source: dataacademykz.superstore.sales

 

Task #3: How Many Customers Do We Have in the Dataset?

Gaining insight into the size of our customer base is essential for strategic planning and resource allocation. This task aims to quantify the number of unique customers we serve, as recorded in our dataset.

Objective: Determine the total number of unique customers represented in the dataset.

Expected Result: 793

Data Source: dataacademykz.superstore.sales

 

Task #4: Which Shipping Options Are Available for Our Customers?

To enhance customer satisfaction and operational efficiency, it's important to understand the variety of shipping options we offer. This task involves analyzing the dataset to uncover the different shipping methods available to our customers.

Objective: Discover the range of shipping options available to customers as represented in the dataset.

Expected Result: The query is expected to yield a list of different shipping options.

Ship modes

Data Source: dataacademykz.superstore.sales

 

Task #5: Which Segments Do We Have?

Our dataset contains valuable information about customer classifications. Understanding these classifications is crucial for targeted strategies in marketing and product development.

Objective: Identify the different customer segments present in the dataset, based on the 'Segment' column.

Expected Result: The expected outcome of this query is to identify a finite set of distinct customer segments. It is anticipated to reveal specific segment names that classify the customers.

Data Source: dataacademykz.superstore.sales

 

Task #6: What Are the Different Product Categories and Sub-Categories in Our Dataset?

To understand the range of products we deal with, it's essential to know the categories and sub-categories they belong to. This can be achieved by querying the Category and Sub_Category columns from our dataset.

Objective: Explore and list the various product categories and sub-categories available in the dataset, to gain a comprehensive understanding of the product range.

Expected Result: The expectation is to generate a list showcasing each unique combination of category and sub-category.

Data Source: dataacademykz.superstore.sales