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I chose 'AdventureWorksLT2019.bak' and 'AdventureWorksLT2017.bak' data for analysis & visualization. The query editing was done in 'Microsoft SQL Server Management Studio' and the visualization part was using 'Microsoft Power BI'. Also for analysis, I used MS Excel and PowerBI's query tool.

Data Analysis of a Superstore using MS Excel with Visualization

This is the repository for analysis on data-sets of Marketing and Recruiting Campaign of Syracuse University using MS Excel

Analysis of some data-sets in the book 'Statistics for Management and Economics' by Gerald Keller

Data Analysis and creating dashboard using MS Excel on Vrinda superstore dataset

Vrinda Store Data Analysis - Interactive Dashboard creation using MS Excel

Data Analysis and Visualization using SQL, Tableau, Power BI, Ms Excel, D3, Javascript and Vega-Altair

Exploring the Chinook database as my first full fledged data analysis project. I have explored the raw data using SQL, then presented the reports in MS Excel and finally I visualized the data using Tableau.

A simple online tool to view csv files in tabular format and do certain operations like edit, delete, add, update data, sort and filter them. Can be used for data analysis, Or when MS-Excel doesn't open.

Position Description The Report Developer will be a key member of a team responsible for implementing a critical reporting initiative. The individual will work with a team in IT to create key financial and operating metrics. Responsibilities include report development, dashboard development, and assisting the business with data requests and analysis. Report and dashboard development requirements will span across multiple systems and will include integration of data from multiple sources. Responsibilities • Design and implement enterprise reporting and business intelligence solutions. • Gather requirements from business users. • Prepare ad hoc customer, product, and labor analysis. • Dashboard development using Dundas Data Visualization software. • Report development using Microsoft SQL Server Reporting Services (SSRS). • ETL using SQL Server Integration Services (SSIS). • Set up routines within SQL Server to consolidate data from multiple data sources. • Maintain data marts of extracted data. • Conceptual, logical and physical data modeling. • Develop optimized stored procedures. Required experience and attributes • At least 3 years of experience in software development and database design, with emphasis on the SQL Server 2008 (primarily SSRS and SSIS). • Solid experience with MS SQL Server development (stored procedures, views, triggers, functions, query optimization). • Experience with web and browser based applications and technologies. • Attention to detail and possession of a *data stewardship* attitude. • Strong problem solving skills. • Ability to write stored procedures and complex SSIS packages. • Proficient with Microsoft Excel, especially pivot tables, reports, and charts. • Self-motivated and a high degree of intellectual curiosity. • Demonstrated organizational skills & ability to multi-task in a fast-paced environment with competing priorities. • Financial and Operating metrics reporting experience a plus!

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This is a personal data analysis project of mine, I used MS Excel and Power BI to do the whole data analysis processes. The dataset is inputted manually from text invoices into spreadsheets. I used Excel with some Power Query to sort the dataset from monthly sales data into a yearly sales data, and created a dashboard using Power BI.

Conducted a comprehensive analysis of road accident data using MS Excel. Uncovered patterns, identified high-risk areas, and visualized key insights. This project showcases my data analysis skills and the ability to derive meaningful conclusions for informed decision-making in road safety. #MSExcel #DataAnalysis #RoadSafety 🛣️📊

My first Research Project - using Machine Learning and Natural Language Processing in Python and Data Pre-processing in MS Excel, to find out how the world perceives "Naturals" (one that is considered to be born with talent, does well in their field effortlessly) or "Strivers" (one who works hard with determination to achieve great feats). This was done by conducting a Twitter Sentiment Analysis.

Final Year Individual Project: Operation Journal Analysis and Visualization Application Tasks included: Negotiating user requirements, presenting prototype designs, developing data transfer procedures, application architecture design, selecting appropriate tools, application development, testing, writing documentation, and handover to the client. The application was coded using C# language. MS SQL Server 2014 Express was chosen for database storage. Project developed using ORM Entity framework Code First, patterns: Dependency Injection, Repository, Unit of work, MVVM. For a better look and efficiency of graphics WPF framework and graphic libraries such as LiveCharts for WPF and Mahapps.Metro were used. Export data to Excel feature was developed using Epplus library.

Predicting-Athlete-Performance An athlete performance can be determined by many factors such as height, age, and weight. Hence, we analyse which of these factors affect the performance of the athlete the most. Here we use statistical methods like correlation, regression and hypothesis testing. This project helps us to predict the chance of winning in the Olympic games and also helps to choose the most appropriate one among all the athletes by a country for the Olympic games. METHODOLOGY We use R language and MS Excel for this project. First using MS Excel, we filtered our dataset which contained unwanted data’s like name, nationality, year. In R, we have stored the data of each sport in an index of a list. Then we get input from the user to select the sports we want to know about. After the sports is selected by the user, the p value is checked for the variables. Further analysis can be done only if the p-value is lesser than 0.5. This ensures that there is a strong relationship between the variables and further analysis carried out will be correct. Next we plot correlation matrix and scatter plot to know the relation between medals and age, height and weight. Then finally we find the regression equation so the end user can simply give in age, height and weight inputs and can select the highest value for medal. TECHNIQUES USED IN STATISTICS: Correlation Regression P-value FUNCTIONS USED IN R: pairs.panel() lm() summary() Correlation: Correlation is a statistical technique that can show whether and how strongly pairs of variables are related. For example, height and weight are related; taller people tend to be heavier than shorter people. Like all statistical techniques, correlation is only appropriate for certain kinds of data. Correlation works for quantifiable data in which numbers are meaningful, usually quantities of some sort. It cannot be used for purely categorical data, such as gender, brands purchased, or favourite colour. The correlation coefficient that indicates the strength of the relationship between two variables can be found using the following formula: Where: • rxy – the correlation coefficient of the linear relationship between the variables x and y • xi – the values of the x-variable in a sample • x̅ – the mean of the values of the x-variable • yi – the values of the y-variable in a sample • ȳ – the mean of the values of the y-variable Regression: In statistical modelling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome variable') and one or more independent variables (often called 'predictors', 'covariates', or 'features'). The most common form of regression analysis is linear regression, in which a researcher finds the line (or a more complex linear function) that most closely fits the data according to a specific mathematical criterion. Multiple linear regression analysis is essentially similar to the simple linear model, with the exception that multiple independent variables are used in the model. The mathematical representation of multiple linear regression is: Y = a + bX1 + cX2 + dX3 + ϵ Where: • b, c, d – slopes • Y – dependent variable • X1, X2, X3 – independent (explanatory) variables • a – intercept • ϵ – residual (error) P-Values: The p-value for each term tests the null hypothesis that the coefficient is equal to zero (no effect). A low p-value (< 0.05) indicates that you can reject the null hypothesis. In other words, a predictor that has a low p-value is likely to be a meaningful addition to your model because changes in the predictor's value are related to changes in the response variable. Conversely, a larger (insignificant) p-value suggests that changes in the predictor are not associated with changes in the response. The variables used in this test are: • Dependent variable : Medal • Independent variable : Age, Height and Weight How to Run Download the code.R file and the dataset (Extract the athlete_events.rar) to the same directory. Run the first line of the code seperately i.e., Give the input seperately. For example, Enter the sport : Taekwondo After giving the input, run the remaining lines of the code.

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Vrinda Store Sales Data Analysis Dashboard with Insights using Excel. Designed and developed an interactive Excel dashboard for analyzing Vrinda Store Sales Data. Used Excel formulas, PivotTables, charts, and slicers to visualize key business metrics. Data was sourced from YouTube for practice and learning purposes.

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I developed a comprehensive project in Excel, creating Multiple Dashboards and tables to analyze the data. This process involved several stages, including data cleaning and data visualization.

Data Analysis for using Ms Excel

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Sales Data Analysis Report using MS Excel

FNP Sala Data Analysis using MS Excel

Project Objective: The Vrinda Store wants to create an annual sales report for 2022. So that, the owner of the Vrinda store can understand their customers and grow more sales in 2023.

Data Analysis using MS Excel (Dataset downloaded from kaggle)

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Data gathering, cleaning, analysis and visualization using MS Excel

Excel-based crash incident analysis (2019–2023) with data cleaning, pivot tables, visualizations, and an interactive dashboard.