Analyzing Coffee Shop Sales: Excel Insights 📈

In my first Data Analytics Project, I Discover the secrets of a fictional coffee shop's success with my data-driven analysis. By Analyzing a 5-sheet Excel dataset, I've uncovered valuable sales trends, customer preferences, and insights that can guide future business decisions. 📊☕

DATA CLEANING 🧹

• REMOVED DUPLICATES OR IRRELEVANT ENTRIES: Thoroughly eliminated duplicate records and irrelevant data to refine the dataset for analysis.

• FIXED STRUCTURAL ERRORS: Rectified any inconsistencies or structural issues within the data to ensure uniformity and accuracy.

• CHECKED FOR DATA CONSISTENCY: Verified the integrity and coherence of the dataset by identifying and resolving any inconsistencies or discrepancies.

DATA MANIPULATION 🛠️

• UTILIZED LOOKUPS: Used Excel's lookup functions for efficient data retrieval and analysis.

• IMPLEMENTED INDEX MATCH: Leveraged the Index Match function to perform advanced data searches and matches.

• APPLIED SUMIFS FUNCTIONS: Utilized SumIFs to calculate totals based on specified criteria.

• CALCULATED PROFITS: Used relevant formulas and techniques to determine profit margins and insights from the data.

PIVOTING THE DATA 𝄜

• CREATED PIVOT TABLES: Utilized Excel's PivotTable feature to pivot the data for in-depth analysis.

• FILTERED DATA: Utilized pivot tables to filter and analyze specific subsets of data, enabling focused insights. Specially used in “PEAK HOURS” and “TOP 3 PRODUCTS” charts.

VISUALIZATION 📊

• KEY INSIGHTS: Unveiled the grand total sales revenue while also analyzing the average bill per person, offering comprehensive insights into the coffee shop's performance and customer spending habits.

• SALES TREND ANALYSIS: Used Line chart to compute total sales across various time intervals, revealing valuable insights into evolving sales trends.

• PEAK HOUR ANALYSIS: Leveraged Clustered Column chart to identify peak sales hours, shedding light on optimal operating times and potential staffing needs.

• TOP 3 PRODUCTS IDENTIFICATION: Utilized Clustered Bar chart to determine the top three coffee types, facilitating strategic decisions regarding inventory management and marketing focus.

*I also used a Timeline to visualize chronological data trends and identify key patterns over specific times.

While it's a significant milestone for me, I recognize that there's always room for growth and improvement. Your feedback and insights are invaluable to me as I continue to refine my skills and tackle future projects. I'm eager to hear your thoughts and suggestions on how I can make my next endeavor even more impactful and insightful.

THANKS TO: WsCube Tech Mo Chen Alex Freberg

TOOLS USED: Microsoft Excel

DataAnalytics #DataAnalyst #ExcelProject #DataVisualization #BusinessIntelligence #SalesAnalysis #DataAnalysis #DataDrivenDecisions

In this Excel project, I utilized data-cleaning techniques to preprocess a dataset and created an interactive dashboard using pivot tables and charts. The objective was to provide users with a dynamic and intuitive tool to analyze and visualize data across multiple dimensions.

Project Highlights:

Data Cleaning: Employed robust data cleaning techniques to ensure the accuracy and consistency of the dataset. This involved handling missing values, removing duplicates, and standardizing data formats.

Pivot Tables: Utilized Excel's pivot tables to summarize and aggregate the dataset, enabling efficient data analysis and exploration. Created three pivot tables to extract valuable insights from the data.

Interactive Dashboard: Developed an interactive dashboard that allows users to dynamically explore the data through three slicers. These slicers provide the flexibility to select different data columns for visualization on the charts, empowering users to dive deep into the dataset based on their specific needs.

Data Visualization: Designed visually appealing charts to represent key trends and patterns in the dataset. Leveraged Excel's charting capabilities to present information in a clear and concise manner. The charts provide valuable insights into the dataset and help users make data-driven decisions.

User-Friendly Experience: Ensured a user-friendly experience by implementing intuitive navigation and interaction within the dashboard. Users can easily filter and drill down into the data to gain a deeper understanding and uncover valuable insights.

This dataset contains a cleaned version of the Montgomery County Fleet Equipment Inventory.

✅ Data Cleaning Steps: - Removed duplicate records - Fixed spelling errors - Merged department names using Flash Fill - Removed unnecessary whitespace - Converted CSV to Excel (.XLSX) format

📂 Original Dataset Source: Montgomery County Public Dataset

This project focuses on data mapping, integration, and analysis to support the development and enhancement of six UNCDF operational applications: OrgTraveler, Comms Central, Internal Support Hub, Partnership 360, SmartHR, and TimeTrack. These apps streamline workflows for travel claims, internal support, partnership management, and time tracking within UNCDF.Key Features and Tools:Data Mapping for Salesforce CRM Migration: Structured and mapped data flows to ensure compatibility and seamless migration to Salesforce CRM.Python for Data Cleaning and Transformation: Utilized pandas, numpy, and APIs to clean, preprocess, and transform raw datasets into standardized formats.Power BI Dashboards: Designed interactive dashboards to visualize workflows and monitor performance metrics for decision-making.Collaboration Across Platforms: Integrated Google Collab for code collaboration and Microsoft Excel for data validation and analysis.

This project explores the Titanic dataset using Microsoft Excel.

The goal of this analysis is to understand key survival patterns based on gender, class, age, and fare. The dataset was cleaned and analyzed in Excel, and a dynamic dashboard was created using Pivot Tables, Charts, and Slicers.

Key Highlights: - Data cleaning and categorization of Fare and Age groups - Analysis of survival rate by gender, class, and embarkation - KPI cards to show overall survival insights - Interactive dashboard with slicers (Gender, Class, Survival Status)

Tools Used: Microsoft Excel (Data Cleaning, Pivot Charts, Slicers) Dataset Source: Titanic Dataset from Kaggle Competitions

**Introduction ** This case study will be based on Cyclistic, a bike sharing company in Chicago. I will perform tasks of a junior data analyst to answer business questions. I will do this by following a process that includes the following phases: ask, prepare, process, analyze, share and act.

Background Cyclistic is a bike sharing company that operates 5828 bikes within 692 docking stations. The company has been around since 2016 and separates itself from the competition due to the fact that they offer a variety of bike services including assistive options. Lily Moreno is the director of the marketing team and will be the person to receive these insights from this analysis.

Case Study and business task Lily Morenos perspective on how to generate more income by marketing Cyclistics services correctly includes converting casual riders (one day passes and/or pay per ride customers) into annual riders with a membership. Annual riders are more profitable than casual riders according to the finance analysts. She would rather see a campaign targeting casual riders into annual riders, instead of launching campaigns targeting new costumers. So her strategy as the manager of the marketing team is simply to maximize the amount of annual riders by converting casual riders.

In order to make a data driven decision, Moreno needs the following insights: - A better understanding of how casual riders and annual riders differ - Why would a casual rider become an annual one - How digital media can affect the marketing tactics

Moreno has directed me to the first question - how do casual riders and annual riders differ?

Stakeholders Lily Moreno, manager of the marketing team Cyclistic Marketing team Executive team

Data sources and organization Data used in this report is made available and is licensed by Motivate International Inc. Personal data is hidden to protect personal information. Data used is from the past 12 months (01/04/2021 – 31/03/2022) of bike share dataset.

By merging all 12 monthly bike share data provided, an extensive amount of data with 5,400,000 rows were returned and included in this analysis.

Data security and limitations: Personal information is secured and hidden to prevent unlawful use. Original files are backed up in folders and subfolders.

Tools and documentation of cleaning process The tools used for data verification and data cleaning are Microsoft Excel and R programming. The original files made accessible by Motivate International Inc. are backed up in their original format and in separate files.

Microsoft Excel is used to generally look through the dataset and get a overview of the content. I performed simple checks of the data by filtering, sorting, formatting and standardizing the data to make it easily mergeable.. In Excel, I also changed data type to have the right format, removed unnecessary data if its incomplete or incorrect, created new columns to subtract and reformat existing columns and deleting empty cells. These tasks are easily done in spreadsheets and provides an initial cleaning process of the data.

R will be used to perform queries of bigger datasets such as this one. R will also be used to create visualizations to answer the question at hand.

Limitations Microsoft Excel has a limitation of 1,048,576 rows while the data of the 12 months combined are over 5,500,000 rows. When combining the 12 months of data into one table/sheet, Excel is no longer efficient and I switched over to R programming.

Google Ads Sales Dataset for Data Analytics Campaigns (Raw & Uncleaned) 📝 Dataset Overview This dataset contains raw, uncleaned advertising data from a simulated Google Ads campaign promoting data analytics courses and services. It closely mimics what real digital marketers and analysts would encounter when working with exported campaign data — including typos, formatting issues, missing values, and inconsistencies.

It is ideal for practicing:

Data cleaning

Exploratory Data Analysis (EDA)

Marketing analytics

Campaign performance insights

Dashboard creation using tools like Excel, Python, or Power BI

📁 Columns in the Dataset Column Name ----- -Description Ad_ID --------Unique ID of the ad campaign Campaign_Name ------Name of the campaign (with typos and variations) Clicks --Number of clicks received Impressions --Number of ad impressions Cost --Total cost of the ad (in ₹ or $ format with missing values) Leads ---Number of leads generated Conversions ----Number of actual conversions (signups, sales, etc.) Conversion Rate ---Calculated conversion rate (Conversions ÷ Clicks) Sale_Amount ---Revenue generated from the conversions Ad_Date------ Date of the ad activity (in inconsistent formats like YYYY/MM/DD, DD-MM-YY) Location ------------City where the ad was served (includes spelling/case variations) Device------------ Device type (Mobile, Desktop, Tablet with mixed casing) Keyword ----------Keyword that triggered the ad (with typos)

⚠️ Data Quality Issues (Intentional) This dataset was intentionally left raw and uncleaned to reflect real-world messiness, such as:

Inconsistent date formats

Spelling errors (e.g., "analitics", "anaytics")

Duplicate rows

Mixed units and symbols in cost/revenue columns

Missing values

Irregular casing in categorical fields (e.g., "mobile", "Mobile", "MOBILE")

🎯 Use Cases Data cleaning exercises in Python (Pandas), R, Excel

Data preprocessing for machine learning

Campaign performance analysis

Conversion optimization tracking

Building dashboards in Power BI, Tableau, or Looker

💡 Sample Analysis Ideas Track campaign cost vs. return (ROI)

Analyze click-through rates (CTR) by device or location

Clean and standardize campaign names and keywords

Investigate keyword performance vs. conversions

🔖 Tags Digital Marketing · Google Ads · Marketing Analytics · Data Cleaning · Pandas Practice · Business Analytics · CRM Data

--------CALL CENTER PERFORMANCE DATASET ANALYSIS--------

This is a self-guided project.

The Call Center dataset contained customer data such as caller id, customer name, date, call channel, city, state, reason for calling, call duration, e.t.c.

I tasked myself with identifying trends and patterns so as to create a summarical overview of the data which can give an overview-level understanding of the data to technical and non-technical viewers.

OBJECTIVES: Create a dashboard (using charts, slicers and KPIs) which can be used to statistically track, monitor and visualize the performance of a Call Center.

SOFTWARE TOOLS USED: Microsoft Excel

ANALYTICAL ACTIONS PERFORMED: Data Importation, Data Processing, Data Cleaning, VLOOKUP Pivot Tables Data Visualization (Dashboard creation) Connection Reporting (connecting slicers to Dashboard)

📊 Student Performance Analysis

Project: Data Analysis using Excel Pivot Tables & Charts

Executive Summary

Based on the analysis of 6,607 students, this project identifies that active student habits (Attendance, Tutoring) are stronger predictors of success than environmental factors (Income, Resources).

Key Insights

1. Show Up: Attendance is the #1 driver of success.
2. Get Help: Students attending 6 tutoring sessions/week scored 5 points higher on average.
3. Sleep Myth: Sleep duration showed no correlation with exam scores.

Tools Used

• Microsoft Excel: Pivot Tables, Advanced Charting, Statistical Analysis, Data Cleaning.

Source of Dataset(.csv)

• https://www.kaggle.com/datasets/ayeshaseherr/student-performance

📊 Instagram Reach Analysis | تحليل الوصول في إنستغرام

An exploratory data analysis project using Excel to understand what influences Instagram post reach and engagement.
مشروع تحليل استكشافي لفهم العوامل المؤثرة في وصول منشورات إنستغرام وتفاعل المستخدمين، باستخدام Excel.

📁 Project Description | وصف المشروع

This project uses an Instagram dataset imported from Kaggle to explore how different factors like hashtags, saves, shares, and caption length influence impressions and engagement.
يستخدم هذا المشروع بيانات من إنستغرام تم استيرادها من منصة Kaggle لتحليل كيف تؤثر عوامل مثل الهاشتاقات، الحفظ، المشاركة، وطول التسمية التوضيحية في عدد مرات الظهور والتفاعل.

🛠️ Tools Used | الأدوات المستخدمة

• Microsoft Excel
• Pivot Tables
• TRIM, WRAP, and other Excel formulas
• مايكروسوفت إكسل
• الجداول المحورية
• دوال مثل TRIM و WRAP وغيرها في Excel

🧹 Data Cleaning | تنظيف البيانات

• Removed unnecessary spaces using TRIM
• Removed 17 duplicate rows → 103 unique rows remained

• Standardized formatting: freeze top row, wrap text, center align

• إزالة المسافات غير الضرورية باستخدام TRIM

• حذف 17 صفًا مكررًا → تبقى 103 صفوف فريدة

• تنسيق موحد: تثبيت الصف الأول، لف النص، وتوسيط المحتوى

🔍 Key Analysis Highlights | أبرز نتائج التحليل

1. Impressions by Source | مرات الظهور حسب المصدر

• Highest reach: Home > Hashtags > Explore > Other
• Some totals exceed 100% due to overlapping

2. Engagement Insights | رؤى حول التفاعل

• Saves strongly correlate with higher impressions
• Caption length is inversely related to likes
• Shares have weak correlation with impressions

3. Hashtag Patterns | تحليل الهاشتاقات

• Most used: #Thecleverprogrammer, #Amankharwal, #Python
• Repeating hashtags does not guarantee higher reach

✅ Conclusion | الخلاصة

Shorter captions and higher save counts contribute more to reach than repeated hashtags. Profile visits are often linked to new followers.
العناوين القصيرة وعدد الحفظات تلعب دورًا أكبر في الوصول من تكرار الهاشتاقات. كما أن زيارات الملف الشخصي ترتبط غالبًا بزيادة المتابعين.

👩‍💻 Author | المؤلفة

Raghad's LinkedIn

🧠 Inspiration | الإلهام

Inspired by content from TheCleverProgrammer, Aman Kharwal, and Kaggle datasets.
استُلهم المشروع من محتوى TheCleverProgrammer وأمان خروال، وبيانات من Kaggle.

💬 Feedback | الملاحظات

Feel free to open an issue or share suggestions!
يسعدنا تلقي ملاحظاتكم واقتراحاتكم عبر صفحة المشروع.

This project analyzes the FIFA World Cup dataset from 1930 to 2022 using Excel.

The analysis answers three key questions: 1️⃣ Countries who are FIFA World Cup Champions till date. 2️⃣ Top 5 teams with the most goals scored. 3️⃣ Host countries and number of times they have hosted.

The workbook includes:

Raw_Data – original dataset

Cleaned_Data – standardized dataset after removing unnecessary columns

Q1_Pivot, Q2_Pivot, Q3_Pivot – pivot tables for each question

Dashboard – visual summary of insights

Tools Used: Excel formulas, XLOOKUP, IFS, Pivot Tables, Charts, and Dashboard design.

This synthetic dataset captures detailed participant engagement across various community programs and events throughout the year 2024. It's designed to simulate real-world volunteer and event participation data and is ideal for building dashboards, practicing data cleaning, or creating portfolio projects in tools like Tableau, Power BI, Excel or Google Data Studio (Looker).

Airbnb Price & Room Analysis in Boston Using Tableau 📊

I recently worked on an Airbnb Boston dataset to analyze pricing trends and room details using Tableau. This project focused on understanding Airbnb pricing patterns, room availability, and geographic price distribution across different zip codes in Boston.

🔹 Key Steps & Techniques: ✔ Data Cleaning & Preparation:

Used Data Interpreter to clean the raw Excel dataset. Removed duplicates and handled missing values for accurate insights. ✔ Data Joining:

Joined listings and calendar tables using a common key (ID) to combine pricing information with room details. Ensured correct relationship to avoid duplication and incorrect aggregations. ✔ Dashboard Insights: 📈 Revenue Trends Over Time – Visualized how Airbnb prices fluctuated over a year in Boston. 🏠 Price Per Zipcode & Bedroom Count – Mapped average prices across Boston zip codes to highlight expensive and affordable areas. 📊 Distinct Listings by Room Type – Explored how many 1, 2, 3, 4, and 5-bedroom listings are available in Boston.

🔥 Key Takeaways from the Boston Airbnb Analysis: 📌 Larger Listings Are More Expensive – As expected, the average price increases with the number of bedrooms, with 1-bedroom listings averaging $144 and 5-bedroom listings reaching $445. 📌 Certain Boston Zip Codes Are More Expensive – Prices vary significantly, with some areas averaging over $200 per night, while others remain below $50. 📌 Seasonality Impacts Pricing – The revenue trend shows fluctuations over time, suggesting that Airbnb prices increase during peak seasons and drop during low-demand periods in Boston.

🛠 Tools Used: ✅ Tableau for visualization & dashboard creation. ✅ Microsoft Excel for raw data handling.