SQL_Understanding-the-bicycle-market-

• As part of a bicycle store chain, collaborated with the new team leader to provide insights into the bicycle market and enhance their understanding of brands and categories sold at the stores.
• Leveraged SQL to analyze the store's database, extract relevant information, and perform comprehensive market research. Conducted queries and data analysis to identify popular brands, assess market trends, and understand sales distribution across different categories.
• Generated informative reports and visualizations to present findings and help the team leader make informed decisions regarding inventory management and marketing strategies.
• Contributed to the team's understanding of customer preferences, allowing for targeted product offerings and improved customer satisfaction.
• This project demonstrates proficiency in SQL, data analysis, and market research, showcasing the ability to leverage data to provide valuable insights for business decision-making. market. Specifically, they need to understand better the brands and categories for sale at your stores.

💾 The data

You have access to the following tables:

products

• "product_id" - Product identifier.
• "product_name" - The name of the bicycle.
• "brand_id" - You can look up the brand's name in the "brands" table.
• "category_id" - You can look up the category's name in the "categories" table.
• "model_year" - The model year of the bicycle.
• "list_price" - The price of the bicycle.

brands

• "brand_id" - Matches the identifier in the "products" table.
• "brand_name" - One of the nine brands the store sells.

categories

• "category_id" - Matches the identifier in the "products" table.
• "category_name" - One of the seven product categories in the store.

stocks

• "store_id" - store identifier
• "product_id" - Matches the identifier in the "products" table
• "quantity" - the quantity of products in this store

💪 Challenge

Help your team leader understand your company's products. Include:

1. What is the most expensive item your company sells? The least expensive?
2. How many different products of each category does your company sell?
3. What are the top three brands with the highest average list price? The top three categories?
4. Any other insights you found during your analysis?

Methodology

• Inferential Statistics
• Data Visualization

languages

• python
• SQL

Packages

• pandas
• matplotlib
• seabon
• sqlite3
• ipython_sql (creating magic tool)