Analyzing sales data is essential for any business looking to make informed decisions and optimize its operations. In this project, we will utilize Microsoft Excel and Power Query to conduct a comprehensive analysis of Superstore sales data. Our primary objectives will be to establish meaningful connections between various data sheets, ensure data quality, and calculate critical metrics such as the Cost of Goods Sold (COGS) and discount values. Below are the key steps and elements of this analysis:

1- Data Import and Transformation:

• Gather and import relevant sales data from various sources into Excel.
• Utilize Power Query to clean, transform, and structure the data for analysis.
• Merge and link different data sheets to create a cohesive dataset, ensuring that all data fields are connected logically.

2- Data Quality Assessment:

• Perform data quality checks to identify and address issues like missing values, duplicates, outliers, and data inconsistencies.
• Standardize data formats and ensure that all data is in a consistent, usable state.

3- Calculating COGS:

• Determine the Cost of Goods Sold (COGS) for each product sold by considering factors like purchase price, shipping costs, and any additional expenses.
• Apply appropriate formulas and calculations to determine COGS accurately.

4- Discount Analysis:

• Analyze the discount values offered on products to understand their impact on sales and profitability.
• Calculate the average discount percentage, identify trends, and visualize the data using charts or graphs.

5- Sales Metrics:

• Calculate and analyze various sales metrics, such as total revenue, profit margins, and sales growth.
• Utilize Excel functions to compute these metrics and create visuals for better insights.

6- Visualization:

• Create visualizations, such as charts, graphs, and pivot tables, to present the data in an understandable and actionable format.
• Visual representations can help identify trends, outliers, and patterns in the data.

7- Report Generation:

• Compile the findings and insights into a well-structured report or dashboard, making it easy for stakeholders to understand and make informed decisions.

Throughout this analysis, the goal is to provide a clear and comprehensive understanding of the Superstore's sales performance. By using Excel and Power Query, we can efficiently manage and analyze the data, ensuring that the insights gained contribute to the store's growth and success.

Tool: Microsoft Excel

Dataset: Coffee Sales

Process: 1. Data Cleaning: • Remove duplicates and blanks. • Standardize date and currency formats.

1. Data Manipulation: • Sorting and filtering function to work
   with interest subsets of data. • Use XLOOKUP, INDEX-MATCH and IF
   formula for efficient data manipulation, such as retrieving, matching and organising information in spreadsheets

2. Data Analysis: • Create Pivot Tables and Pivot Charts with the formatting to visualize trends.

3. Dashboard Development: • Insert Slicers with the formatting for easy filtering and dynamic updates.

Highlights: This project aims to understand coffee sales trends by country, roast type, and year, which could help identify marketing opportunities and customer segments.

A colleague, Lucas, has asked you to update a spreadsheet called Reseller Details that records details of Adventure Work’s resellers in the United States. This information in the spreadsheet was downloaded from another system. The download process created several inconsistencies or errors within the data.

These errors include unnecessary spaces, the use of the wrong case, and entries that need to be joined together or split apart.

You now need to add formulas to the worksheet to standardize the data so that it can be used for analysis.

🧥 Snitch Fashion Sales (Uncleaned) Dataset 📌 Context This is a synthetic dataset representing sales transactions from Snitch, a fictional Indian clothing brand. The dataset simulates real-world retail sales data with uncleaned records, designed for learners and professionals to practice data cleaning, exploratory data analysis (EDA), and dashboard building using tools like Python, Power BI, or Excel.

📊 What You’ll Find The dataset includes over 2,500 records of fashion product sales across various Indian cities. It contains common data issues such as:

Missing values

Incorrect date formats

Duplicates

Typos in categories and city names

Unrealistic discounts and profit values

🧾 Columns Explained Column --Description Order_ID ------Unique ID for each sale (some duplicates) Customer_Name ------Name of the customer (inconsistent formatting) Product_Category ---Clothing category (e.g., T-Shirts, Jeans — includes typos) Product_Name -----Specific product sold Units_Sold --Quantity sold (some negative or null) Unit_Price --Price per unit (some missing or zero) Discount_% ----Discount applied (some >100% or missing) Sales_Amount ------Total revenue after discount (some miscalculations) Order_Date ---------Order date (multiple formats or missing) City -------Indian city (includes typos like "Hyd", "bengaluru") Segment----- Market segment (B2C, B2B, or missing) Profit ---------Profit made on the sale (some unrealistic/negative)

💡 How to Use This Dataset Clean and standardize messy data

Convert dates and correct formats

Perform EDA to find:

Top-selling categories

Impact of discounts on sales and profits

Monthly/quarterly trends

Segment-based performance

Create dashboards in Power BI or Excel Pivot Table

Document findings in a PDF/Markdown report

🎯 Ideal For Aspiring data analysts and data scientists

Excel / Power BI dashboard learners

Portfolio project creators

Kaggle competitions or practice

📌 License This is a synthetic dataset created for educational use only. No real customer or business data is included.

This study examined the effects of treadmill running (TR) regimens on craniofacial pain- and anxiety-like behaviors, as well as their effects on neural changes in specific brain regions of male mice subjected to repeated social defeat stress (SDS) for 10 days. Behavioral and immunohistochemical experiments were conducted to evaluate the impact of TR regimens on SDS-related those behaviors, as well as epigenetic and neural activity markers in the anterior cingulate cortex (ACC), insular cortex (IC), rostral ventromedial medulla (RVM), and cervical spinal dorsal horn (C2). Behavioral responses were quantified using multiple tests, while immunohistochemistry measured histone H3 acetylation, histone deacetylases (HDAC1, HDAC2), and neural activity markers (FosB and phosphorylated cAMP response element-binding protein (pCREB). The effects of both short-term TR (2 days, TR2) and long-term TR (10 days, TR10) regimens were conducted. TR10 significantly reduced anxiety- and formalin-evoked craniofacial pain-like behaviors in SDS mice. It normalized SDS-induced increases in histone H3 acetylation in both the anterior and posterior portions of the ACC, as well as the anterior portion of the IC. These inhibitory effects were also observed in SDS-related increases in HDAC1, FosB, and pCREB expression. Additionally, TR10 normalized increased histone H3 acetylation in the RVM and C2 regions, with specific effects on FosB and pCREB expression observed in the C2 region. In contrast, TR2 showed limited effects on craniofacial pain-like behaviors but reduced anxiety-like behaviors in SDS mice. Under sham conditions, TR2 had minimal impact on histone H3 acetylation. Paradoxically, TR2 increased formalin-evoked craniofacial pain-like behaviors during the early phase despite not altering acetylated histone H3 expression. In conclusion, the TR10 regimen is effective in attenuating SDS-induced craniofacial pain- and anxiety-like behaviors, likely by normalizing epigenetic modifications and neural activity in key brain regions.