A queue is a first-in, first-out (FIFO) data structure where elements are inserted at the rear and deleted from the front. There are two common implementations - a linear array implementation where the front and rear indices are incremented as elements are added/removed, and a circular array implementation where the indices wrap around to avoid unused space. Queues have applications in printing, scheduling, and call centers where requests are handled in the order received.
This document provides an overview and introduction to a proposed web service to help farmers in India. The proposed service would allow farmers, suppliers, and administrators to login separately and would include features like a complaints page for farmers, advertisement pages for suppliers, and SMS notifications to farmers about new ads. The service aims to improve communication and transparency between farmers and suppliers. It describes the motivation as addressing issues with middlemen and proposes the service could connect multiple villages. The document then outlines the methodology, technologies used like HTML, Java, CSS, JavaScript, and MySQL, and the scope which includes ensuring greater farmer profitability and bringing transparency to the agricultural system.
The document discusses an Excel training module that covers filtering data, formulas, and combining data from multiple sources. It includes topics like freezing panes, variance analysis, Autofill, cell comments, advanced filtering, and formulas for comparing cells. Videos and examples are provided to demonstrate techniques like filtering by color and text.
Definition of tree , tree structure, types of structure, Degree and Forest, Examples., Binary tree definitions, types of binary tree, Examples., details of binary tree representations, types of representations, examples.
The document is a presentation about lookup functions in Microsoft Excel. It introduces LOOKUP, VLOOKUP, and HLOOKUP functions. It provides examples of how to use each function to lookup values in tables and return results. It also shares some fun facts about Excel's history and capabilities.
This document provides an introduction to advanced Excel topics. It discusses what advanced Excel involves, including complex calculations, large data handling, data analysis, and better data visualization. Some key advanced Excel topics covered are data validation, logical functions, what-if analysis, goal seek, lookup functions, pivot tables, array functions, Excel dashboards, advanced charting techniques, VBA and macros, and Power Query. The document also discusses how advanced Excel skills are useful for business and career purposes by allowing users to efficiently arrange, manage, and analyze large amounts of data.
A queue is a first-in, first-out (FIFO) data structure where elements are inserted at the rear and deleted from the front. There are two common implementations - a linear array implementation where the front and rear indices are incremented as elements are added/removed, and a circular array implementation where the indices wrap around to avoid unused space. Queues have applications in printing, scheduling, and call centers where requests are handled in the order received.
This document provides an overview and introduction to a proposed web service to help farmers in India. The proposed service would allow farmers, suppliers, and administrators to login separately and would include features like a complaints page for farmers, advertisement pages for suppliers, and SMS notifications to farmers about new ads. The service aims to improve communication and transparency between farmers and suppliers. It describes the motivation as addressing issues with middlemen and proposes the service could connect multiple villages. The document then outlines the methodology, technologies used like HTML, Java, CSS, JavaScript, and MySQL, and the scope which includes ensuring greater farmer profitability and bringing transparency to the agricultural system.
The document discusses an Excel training module that covers filtering data, formulas, and combining data from multiple sources. It includes topics like freezing panes, variance analysis, Autofill, cell comments, advanced filtering, and formulas for comparing cells. Videos and examples are provided to demonstrate techniques like filtering by color and text.
Definition of tree , tree structure, types of structure, Degree and Forest, Examples., Binary tree definitions, types of binary tree, Examples., details of binary tree representations, types of representations, examples.
The document is a presentation about lookup functions in Microsoft Excel. It introduces LOOKUP, VLOOKUP, and HLOOKUP functions. It provides examples of how to use each function to lookup values in tables and return results. It also shares some fun facts about Excel's history and capabilities.
This document provides an introduction to advanced Excel topics. It discusses what advanced Excel involves, including complex calculations, large data handling, data analysis, and better data visualization. Some key advanced Excel topics covered are data validation, logical functions, what-if analysis, goal seek, lookup functions, pivot tables, array functions, Excel dashboards, advanced charting techniques, VBA and macros, and Power Query. The document also discusses how advanced Excel skills are useful for business and career purposes by allowing users to efficiently arrange, manage, and analyze large amounts of data.
This document discusses database normalization and different normal forms including 1NF, 2NF, 3NF, and BCNF. It defines anomalies like insertion, update, and deletion anomalies that can occur when data is not normalized. Examples are provided to illustrate the different normal forms and how denormalizing data can lead to anomalies. The key aspects of each normal form like removing repeating groups (1NF), removing functional dependencies on non-prime attributes (2NF), and removing transitive dependencies (3NF, BCNF) are explained.
The document describes two database design exercises. The first involves designing a database to store information about employees, departments, and employee children. It involves entities for employees, departments, and children and relationships indicating employees work in departments, departments have managers, and children can be identified through their parent employee.
The second exercise involves designing a database for an art gallery. It involves entities for artists, artworks, groups, and customers. Artworks are created by artists and can belong to multiple groups. Customers like certain artists and groups. The solution diagrams the entities and relationships along with constraints like each artwork is created by one artist.
The purpose of the project entitled as “Hospital Management System” is to computerize the
Front Office Management of Hospital to develop software which is user friendly simple, fast,
and cost – effective. It deals with the collection of patient’s information like add patient, update
patient, delete patient, search patient, view patient diagnosis, etc. Traditionally, it was done
manually. The main function of the system is register and store patient details and doctor details
and retrieve these details as and when required, and also to manipulate these details
meaningfully. The Hospital Management System can be entered using a username and
password. It is accessible by an Admin, Doctor & Receptionist. Only they can add data into
the database. The data can be retrieved easily. The data are well protected for personal use and
makes the data processing very fast.
PL/SQL is a combination of SQL along with the procedural features of programming languages.
It provides specific syntax for this purpose and supports exactly the same datatypes as SQL.
The document discusses various data analysis and visualization techniques in Microsoft Excel including filtering, sorting, formulas, functions, pivot tables, charts and conditional formatting. It provides step-by-step instructions on how to use these tools to extract insights from data by filtering to select specific records, using formulas and functions like VLOOKUP to perform calculations, sorting data, creating pivot tables and pivot charts to summarize and visualize data relationships, and applying conditional formatting to highlight important values.
This document provides an overview of CS8492 - Database Management Systems course objectives and content. The course aims to teach fundamental database concepts including data models, relational databases, SQL, transaction processing, and storage techniques. It covers topics such as the relational model, keys, normalization, database architecture, languages, and integrity constraints. The document also provides examples and definitions of relational database concepts.
This document provides an overview of Excel functions VLOOKUP, HLOOKUP, and INDEX MATCH. It defines each function and provides examples of their usage. VLOOKUP and HLOOKUP search for a value in a table and return a corresponding value from the same row or column. INDEX MATCH is a combination of the INDEX and MATCH functions that allows lookup of values in both vertical and horizontal tables without limitations of VLOOKUP and HLOOKUP. The document demonstrates how INDEX MATCH provides more flexibility than VLOOKUP and HLOOKUP through examples.
Difference between fact tables and dimension tablesKamran Haider
The document discusses the differences between fact tables and dimension tables in a data warehouse. It also discusses surrogate keys, natural keys, and why surrogate keys are used in data warehouses. Specifically:
- Fact tables contain measures/facts and foreign keys, while dimension tables contain descriptive attributes. Surrogate keys are integers assigned sequentially in dimension tables to join with fact tables.
- Surrogate keys are used instead of natural keys for faster joins, better performance, and to integrate heterogeneous data sources. They allow maintaining historical and current data when natural keys may change over time.
- The document outlines advantages like performance and disadvantages like unnecessary burden during ETL of using surrogate keys versus natural keys which have business meaning but can impact performance
How to use Hlookup find an exact match Excel Advise
In this presentation we are telling you guys that In Microsoft Excel How to use Hlookup find an exact match.
The H in HLOOKUP stands for horizontal.
Hlookup Function is used to search a value in another Table and if found return the corresponding value of that table for the specified row.
You can lookup value in one of the two following ways:
Range Lookup
Range lookup is used when you want to search for ranges, it will look for nearest minimum value from the first row of the table.
Exact Lookup
We use this kind of lookups when we need to seek exact value.
Lookup value is the value to be found in the first row of the table. It can be a number, text or cell address..
Table array is a range where you want to find your lookup value.
Row num is the row number in table array from which the matching value will be returned
Range lookup is a logical value that specifies whether you want HLOOKUP to find an exact match or an approximate match.
if range lookup is true then it will return you exact match or an approximate matching value. And if range lookup is false it will give you exact match.
This document contains the agenda for a Power Query event. It includes sections on the sponsor, organizers, presenter Marco Pozzan, and an agenda with topics like Power Query basics, functions, error handling, privacy settings, and demos. The event aims to teach participants about Power Query's capabilities for data transformation, integration, and analysis.
This presentation gives a clear and concise description of joins in sql and several types of sql joins.
These slides also contains the pictorial representation as well as syntax for each type of joins.
Triggers are stored procedures that are automatically executed in response to data modification events like insert, update or delete on a table. There are two types of triggers - DML triggers which respond to data manipulation statements, and DDL triggers which respond to data definition language statements like create, alter or drop. Triggers can be used to perform validation, auditing or other actions in response to data changes. They can be disabled, enabled or modified as needed using SQL statements.
University Database Management Project Focuses on managing the data associated with the Academic and Research department of the University. This presentation consists of the problem statement, E-R Diagram, the way of normalizing tables, an overview of the procedures, views, triggers etc and some of the complex queries.
One is requested to go through the doc file to get an in-depth information about the project and its functionality.
Doc file link - https://www.scribd.com/doc/269150294/University-Database-Management-System
This topic is covered under Data modelling and implementation. This project looks after an efficient billing management in a medical store. it includes a flow chart, data flow diagram, normalization etc.
Nestle is the 36th biggest company in the world founded in 1866 in Switzerland. It has factories and operations around the world and is a world leader in coffee and chocolate. Nestle produces over 8000 food products across categories like milk, beverages, prepared dishes and confectionery. It aims to foster a creative working environment that encourages innovation, flexibility and teamwork.
The document discusses dimensional modeling concepts used in data warehouse design. Dimensional modeling organizes data into facts and dimensions. Facts are measures that are analyzed, while dimensions provide context for the facts. The dimensional model uses star and snowflake schemas to store data in denormalized tables optimized for querying. Key aspects covered include fact and dimension tables, slowly changing dimensions, and handling many-to-many and recursive relationships.
1. The document defines and provides examples of various data structures including lists, stacks, queues, trees, and their properties.
2. Key concepts covered include linear and non-linear data structures, common tree types, tree traversals, and operations on different data structures like insertion, deletion, and searching.
3. Examples are provided to illustrate concepts like binary search trees, tree representation and traversal methods.
This document discusses database anomalies such as insertion, deletion, and modification anomalies. Insertion anomalies occur when new information cannot be added due to missing or incorrect data. Deletion anomalies happen when related data is lost upon deleting a record. Modification anomalies result from inconsistencies caused by partial updates or redundant data. The document provides an example of a bad database design that is prone to these anomalies and explains how normalization helps avoid anomalies by separating data into distinct tables.
The Fruit Bazaar System allows users, administrators, and shipping agents to interact. Users can log in, add items to their cart, and check out to place orders. Administrators can log in with privileges to modify the catalog, maintain user data, and process orders. Shipping agents deliver ordered items from the warehouse to customers.
This document analyzes sports data from the Olympics from 1896 to 2016 to evaluate factors that influence countries' and athletes' performance over time. It uses data visualization and linear regression models to examine relationships between variables like total medals won and GDP or population. The analysis finds that visualization provides insights into the evolution of the Games and improvements in countries' performances. It aims to help countries identify mistakes and enhance future development.
Splay Method of Model Acquisition Assessmentijtsrd
We know that the study of an object under investigation using a mathematical model is called modeling. The purpose of modeling will be to determine the properties of the object under study, to model it and to assess its condition. Turapov U. U | Isroilov U. B | Raxmatov A. SH | Egamov S. M | Isabekov B. I "Splay-Method of Model Acquisition Assessment" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38141.pdf Paper URL : https://www.ijtsrd.com/other-scientific-research-area/other/38141/splaymethod-of-model-acquisition-assessment/turapov-u-u
This document discusses database normalization and different normal forms including 1NF, 2NF, 3NF, and BCNF. It defines anomalies like insertion, update, and deletion anomalies that can occur when data is not normalized. Examples are provided to illustrate the different normal forms and how denormalizing data can lead to anomalies. The key aspects of each normal form like removing repeating groups (1NF), removing functional dependencies on non-prime attributes (2NF), and removing transitive dependencies (3NF, BCNF) are explained.
The document describes two database design exercises. The first involves designing a database to store information about employees, departments, and employee children. It involves entities for employees, departments, and children and relationships indicating employees work in departments, departments have managers, and children can be identified through their parent employee.
The second exercise involves designing a database for an art gallery. It involves entities for artists, artworks, groups, and customers. Artworks are created by artists and can belong to multiple groups. Customers like certain artists and groups. The solution diagrams the entities and relationships along with constraints like each artwork is created by one artist.
The purpose of the project entitled as “Hospital Management System” is to computerize the
Front Office Management of Hospital to develop software which is user friendly simple, fast,
and cost – effective. It deals with the collection of patient’s information like add patient, update
patient, delete patient, search patient, view patient diagnosis, etc. Traditionally, it was done
manually. The main function of the system is register and store patient details and doctor details
and retrieve these details as and when required, and also to manipulate these details
meaningfully. The Hospital Management System can be entered using a username and
password. It is accessible by an Admin, Doctor & Receptionist. Only they can add data into
the database. The data can be retrieved easily. The data are well protected for personal use and
makes the data processing very fast.
PL/SQL is a combination of SQL along with the procedural features of programming languages.
It provides specific syntax for this purpose and supports exactly the same datatypes as SQL.
The document discusses various data analysis and visualization techniques in Microsoft Excel including filtering, sorting, formulas, functions, pivot tables, charts and conditional formatting. It provides step-by-step instructions on how to use these tools to extract insights from data by filtering to select specific records, using formulas and functions like VLOOKUP to perform calculations, sorting data, creating pivot tables and pivot charts to summarize and visualize data relationships, and applying conditional formatting to highlight important values.
This document provides an overview of CS8492 - Database Management Systems course objectives and content. The course aims to teach fundamental database concepts including data models, relational databases, SQL, transaction processing, and storage techniques. It covers topics such as the relational model, keys, normalization, database architecture, languages, and integrity constraints. The document also provides examples and definitions of relational database concepts.
This document provides an overview of Excel functions VLOOKUP, HLOOKUP, and INDEX MATCH. It defines each function and provides examples of their usage. VLOOKUP and HLOOKUP search for a value in a table and return a corresponding value from the same row or column. INDEX MATCH is a combination of the INDEX and MATCH functions that allows lookup of values in both vertical and horizontal tables without limitations of VLOOKUP and HLOOKUP. The document demonstrates how INDEX MATCH provides more flexibility than VLOOKUP and HLOOKUP through examples.
Difference between fact tables and dimension tablesKamran Haider
The document discusses the differences between fact tables and dimension tables in a data warehouse. It also discusses surrogate keys, natural keys, and why surrogate keys are used in data warehouses. Specifically:
- Fact tables contain measures/facts and foreign keys, while dimension tables contain descriptive attributes. Surrogate keys are integers assigned sequentially in dimension tables to join with fact tables.
- Surrogate keys are used instead of natural keys for faster joins, better performance, and to integrate heterogeneous data sources. They allow maintaining historical and current data when natural keys may change over time.
- The document outlines advantages like performance and disadvantages like unnecessary burden during ETL of using surrogate keys versus natural keys which have business meaning but can impact performance
How to use Hlookup find an exact match Excel Advise
In this presentation we are telling you guys that In Microsoft Excel How to use Hlookup find an exact match.
The H in HLOOKUP stands for horizontal.
Hlookup Function is used to search a value in another Table and if found return the corresponding value of that table for the specified row.
You can lookup value in one of the two following ways:
Range Lookup
Range lookup is used when you want to search for ranges, it will look for nearest minimum value from the first row of the table.
Exact Lookup
We use this kind of lookups when we need to seek exact value.
Lookup value is the value to be found in the first row of the table. It can be a number, text or cell address..
Table array is a range where you want to find your lookup value.
Row num is the row number in table array from which the matching value will be returned
Range lookup is a logical value that specifies whether you want HLOOKUP to find an exact match or an approximate match.
if range lookup is true then it will return you exact match or an approximate matching value. And if range lookup is false it will give you exact match.
This document contains the agenda for a Power Query event. It includes sections on the sponsor, organizers, presenter Marco Pozzan, and an agenda with topics like Power Query basics, functions, error handling, privacy settings, and demos. The event aims to teach participants about Power Query's capabilities for data transformation, integration, and analysis.
This presentation gives a clear and concise description of joins in sql and several types of sql joins.
These slides also contains the pictorial representation as well as syntax for each type of joins.
Triggers are stored procedures that are automatically executed in response to data modification events like insert, update or delete on a table. There are two types of triggers - DML triggers which respond to data manipulation statements, and DDL triggers which respond to data definition language statements like create, alter or drop. Triggers can be used to perform validation, auditing or other actions in response to data changes. They can be disabled, enabled or modified as needed using SQL statements.
University Database Management Project Focuses on managing the data associated with the Academic and Research department of the University. This presentation consists of the problem statement, E-R Diagram, the way of normalizing tables, an overview of the procedures, views, triggers etc and some of the complex queries.
One is requested to go through the doc file to get an in-depth information about the project and its functionality.
Doc file link - https://www.scribd.com/doc/269150294/University-Database-Management-System
This topic is covered under Data modelling and implementation. This project looks after an efficient billing management in a medical store. it includes a flow chart, data flow diagram, normalization etc.
Nestle is the 36th biggest company in the world founded in 1866 in Switzerland. It has factories and operations around the world and is a world leader in coffee and chocolate. Nestle produces over 8000 food products across categories like milk, beverages, prepared dishes and confectionery. It aims to foster a creative working environment that encourages innovation, flexibility and teamwork.
The document discusses dimensional modeling concepts used in data warehouse design. Dimensional modeling organizes data into facts and dimensions. Facts are measures that are analyzed, while dimensions provide context for the facts. The dimensional model uses star and snowflake schemas to store data in denormalized tables optimized for querying. Key aspects covered include fact and dimension tables, slowly changing dimensions, and handling many-to-many and recursive relationships.
1. The document defines and provides examples of various data structures including lists, stacks, queues, trees, and their properties.
2. Key concepts covered include linear and non-linear data structures, common tree types, tree traversals, and operations on different data structures like insertion, deletion, and searching.
3. Examples are provided to illustrate concepts like binary search trees, tree representation and traversal methods.
This document discusses database anomalies such as insertion, deletion, and modification anomalies. Insertion anomalies occur when new information cannot be added due to missing or incorrect data. Deletion anomalies happen when related data is lost upon deleting a record. Modification anomalies result from inconsistencies caused by partial updates or redundant data. The document provides an example of a bad database design that is prone to these anomalies and explains how normalization helps avoid anomalies by separating data into distinct tables.
The Fruit Bazaar System allows users, administrators, and shipping agents to interact. Users can log in, add items to their cart, and check out to place orders. Administrators can log in with privileges to modify the catalog, maintain user data, and process orders. Shipping agents deliver ordered items from the warehouse to customers.
This document analyzes sports data from the Olympics from 1896 to 2016 to evaluate factors that influence countries' and athletes' performance over time. It uses data visualization and linear regression models to examine relationships between variables like total medals won and GDP or population. The analysis finds that visualization provides insights into the evolution of the Games and improvements in countries' performances. It aims to help countries identify mistakes and enhance future development.
Splay Method of Model Acquisition Assessmentijtsrd
We know that the study of an object under investigation using a mathematical model is called modeling. The purpose of modeling will be to determine the properties of the object under study, to model it and to assess its condition. Turapov U. U | Isroilov U. B | Raxmatov A. SH | Egamov S. M | Isabekov B. I "Splay-Method of Model Acquisition Assessment" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38141.pdf Paper URL : https://www.ijtsrd.com/other-scientific-research-area/other/38141/splaymethod-of-model-acquisition-assessment/turapov-u-u
Application of K-Means Clustering Algorithm for Classification of NBA GuardsEditor IJCATR
In this study, we discuss the application of K-means clustering technique on classification of NBA guards, including
determination category number, classification results analysis and evaluation about result. Based on the NBA data, using rebounds,
assists and points as clustering factors to K-Means clustering analysis. We implement an improved K-Means clustering analysis for
classification of NBA guards. Further experimental result shows that the best sample classification number is six according to the
mean square error function evaluation. Depending on K-means clustering algorithm the final classification reflects an objective and
comprehensive classification, objective evaluation for NBA guards
This document outlines the key concepts and formulas in business statistics across 5 units. Unit 1 covers definitions of statistics, primary and secondary data collection methods, questionnaires, tabulation, classification and scheduling. Unit 2 covers measures of central tendency. Unit 3 covers measures of dispersion. Unit 4 covers correlation and regression. Unit 5 covers index numbers, time series analysis, seasonal and cyclical variations. It also provides example problems for each unit to practice applying the statistical concepts.
This document provides an overview of key topics in business statistics including:
- Definitions of statistics, primary and secondary data, and data collection methods
- Methods for organizing data such as classification, tabulation, and diagrams
- Measures of central tendency including the mean, median, and mode
- Measures of dispersion like the range, quartile deviation, and standard deviation
- Examples of calculating various statistical measures for different data sets
The document serves as an introduction to foundational statistical concepts for business students.
In the domain of Sport Analytics, Global Positioning Systems devices are intensively used as they permit to retrieve players' movements. Team sports' managers and coaches are interested on the relation between players' patterns of movements and team performance, in order to better manage their team. In this paper we propose a Cluster Analysis and Multidimensional Scaling approach to find and describe separate patterns of players movements. Using real data of multiple professional basketball teams, we find, consistently over different case studies, that in the defensive clusters players are close one to another while the transition cluster are characterized by a large space among them. Moreover, we find the pattern of players' positioning that produce the best shooting performance.
This research aims to solve the problem selection to a decision. In the profile matching method, a parameter assessed on the difference between the target value with the value that is owned by an individual. There are two important parameters in this method such as core factors and secondary factors. These values are converted into a percentage of weight so as to produce the final decision as a determinant of the data which will be closer to the predetermined targets. By doing this method, sorting the data against specific criteria that are dynamically preformed.
Analysis of Knowledge Points in the National College Students Mathematics Com...ijtsrd
The National Mathematical Competition for Undergraduates is a national high level subject competition for undergraduates. In this paper, 82 knowledge points have been sorted out according to the competition content of the China College Students Mathematics Competition non mathematics category and the real questions of the previous competition preliminaries. The evaluation index system of the importance of knowledge points is obtained by the evaluation matrix, and the principal component analysis PCA algorithm is used to establish a model, and statistical analysis is used to obtain the knowledge points with high contribution rate and comprehensive score. The knowledge points to be tested include the calculation of triple integral and double integral, the calculation and application of surface integral, and functions. The knowledge points that will appear with high probability include the geometric meaning of differential, the known binary differential to find the original function, and the properties of power series, Power series to find the limit, effectively helping non mathematical college students to prepare for exams. He Xujie | Hong Hairu | Li Tingyu | Yang Chen "Analysis of Knowledge Points in the National College Students Mathematics Competition (Non-Professional Group) Based on PCA" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-6 , October 2022, URL: https://www.ijtsrd.com/papers/ijtsrd51813.pdf Paper URL: https://www.ijtsrd.com/other-scientific-research-area/other/51813/analysis-of-knowledge-points-in-the-national-college-students-mathematics-competition-nonprofessional-group-based-on-pca/he-xujie
This document discusses descriptive statistics and numerical measures used to describe data sets. It introduces measures of central tendency including the mean, median, and mode. The mean is the average value calculated by summing all values and dividing by the number of values. The median is the middle value when values are arranged in order. The mode is the most frequently occurring value. The document also discusses measures of dispersion like range and standard deviation which describe how spread out the data is. Examples are provided to demonstrate calculating the mean, median and other descriptive statistics.
Representing verifiable statistical index computations as linked dataJose Emilio Labra Gayo
Presentation slides at SemStats 2014 Workshop.
In this paper we describe the development of the Web Index linked data portal that represents statistical index data and computations. The Web Index is a multi-dimensional measure of the World Wide Web’s contribution to development and human rights globally. It covers 81 countries and incorporates indicators that assess several areas like universal access; freedom and openness; relevant content; and empowerment.
In order to empower the Web Index transparency, one internal requirement was that every published data could be externally verified. The verification could be that it was just raw data obtained from an external source, in which case, the system must provide a link to the data source or that the value has been internally computed, in which case, the system provides links to those values. The resulting portal contains data that can be tracked to its sources so an external agent
can validate the whole index computation process.
We describe the different aspects on the development of the WebIndex data portal, which also offers new linked data visualization tools. Although in this paper we concentrate on the Web Index development, we consider that this approach can be generalized to other projects which involve the publication of externally verifiable statistical computations.
This document summarizes a live webinar about creating and querying a graph database of Olympic data. It describes loading data on athletes, countries, sports, events and medals from 1896-2012 into a Neo4j graph database. It then demonstrates several example queries of the Olympic graph, such as the number of sports per games, medals per country per sport, and athletes who medaled in multiple sports.
Conference cum Observer Report Format 18-09-23.docxJugulKishor1
The document summarizes details of the International Conference on Sustainable Emerging Innovations in Engineering and Technology organized in a hybrid format by the Department of ECE, ABES Engineering College, Ghaziabad from September 14-15, 2023. It provides information on paper submissions, acceptance rates, registration fees, special sessions, representation of IEEE UP Section officials, and key statistics on paper presentations. The conference had 7 technical tracks and received 341 papers from India and 4 from other nations, of which 165 papers were accepted for presentation.
This presentations shows how to create a time/date dimension for PowerPivot from the date data in your fact table. I also shows the DAX functions that you can use to add columns to the fact table or a separate dimension table.
This document discusses measuring attainment of course outcomes (COs) and program outcomes (POs) as per the guidelines of the Self-Assessment Report (SAR) published by the National Board of Accreditation (NBA) for engineering programs in India. It presents a simplified approach for measuring CO and PO attainment. Course outcomes are defined for each course and mapped to POs on a scale of 1-3. The attainment of COs is measured based on student performance in internal and external assessments. The percentage of students achieving the target score for each CO is calculated and averaged to determine the CO attainment level based on NBA guidelines. This process is demonstrated through an example course. The CO attainment levels across
Six Sigma Process Capability Study (PCS) Training Module Frank-G. Adler
The Process Capability Study (PCS) Training Module v3.0 includes:
1. MS PowerPoint Presentation including 98 slides covering Introduction to Six Sigma, Creating and analyzing a Histogram, Basic Statistics & Product Capability, Statistical Process Control for Variable Data, Definitions of Process Capability Indices, Confidence Interval Analysis for Capability Indices, Capability Study for Non-Normal Distributed Processes, and several Exercises.
2. MS Excel Confidence Interval Analysis Calculator making it really easy to calculate Confidence Intervals for Capability Indices and other Statistics.
This document summarizes a research project investigating the relationship between the height of starting back five forwards in elite male rugby teams and their lineout success rate. The researcher collected height and lineout statistics for 80 professional rugby players over four games and analyzed the data using Excel. The results showed little correlation between height and lineout success. The researcher was surprised by this finding, as it did not support the hypotheses. Overall, the conclusion was that average back five forward height does not seem to impact lineout win percentage. Areas for future improvement include learning more advanced Excel skills and considering other factors like hookers' throwing techniques.
The document provides an overview of key concepts in statistics including:
- Statistics involves collecting, organizing, analyzing, and drawing inferences from data.
- Descriptive statistics summarize and characterize data through measures like mean, median, range.
- Inferential statistics make conclusions about populations based on sample data using techniques like hypothesis testing and estimation.
- Data must be properly classified, organized into frequency distributions and displayed visually in charts and graphs to facilitate analysis and interpretation.
Extremely Randomized Trees classification model achieved the best performance in predicting flight cancellations. The model used 31 features from flight, weather, and historical flight data to predict cancellations for over 2.8 million flights. Cross-validation determined the optimal number of trees and features. Testing on a holdout dataset produced an ROC AUC of 0.89, accurately predicting cancellations for the imbalanced dataset where cancellations were only 1.14% of flights. The model provides probabilistic cancellation predictions and could help travelers and airlines.
Ähnlich wie INT217 Project Report: Excel Dashboard (20)
This document is a report submitted by a group of students for their income tax calculator project. It includes sections on the purpose of the project, basic income tax fundamentals, a description of the application including screenshots, and the source code for building the GUI application in Python using Tkinter. The application allows users to calculate their income tax liability under the old and new tax regimes and determine which provides greater savings.
LPU Summer Training Project Viva PPT - Modern Big Data Analysis with SQL Spec...Qazi Maaz Arshad
The document summarizes a Modern Big Data Analysis with SQL Specialization course completed as part of a summer training. It discusses the course content and timeline, a project to analyze movie data using SQL and MySQL Workbench, and key learnings. The project involved creating a database from multiple data sets and using SQL queries to calculate results. An entity relationship diagram and screenshots demonstrate the project design and outputs.
Municipal Solid Waste Management in Developing CountriesQazi Maaz Arshad
This document discusses municipal solid waste management in developing countries. It begins by defining waste and providing classifications of waste based on source and type. It then outlines the key steps in municipal solid waste management systems, including waste generation, storage, collection, transport, processing, recovery, and disposal. Several factors that affect municipal solid waste management are also discussed. The document then provides an overview of the current scenario of municipal solid waste management in India, challenges faced, key stakeholders, and policies and initiatives implemented by the Indian government. It concludes by comparing municipal solid waste management approaches between developed, developing, and least developed countries.
This document outlines topics to be covered in ultrasonic testing including basic principles of sound generation, test techniques, inspection applications, equipment, instrumentation, and ultrasonic flaw detection which will be discussed to understand non-destructive testing using ultrasound.
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1. 1
INT 217 Introduction to Data Management
An Excel Dashboard – Olympics Statistics
Submitted By
Qazi Maaz Arshad
11906424 - RKM039B50
B. Tech - Computer Science and Engineering
Under the Guidance of
Sameeksha Khare – 26806
Discipline of CSE/IT
Lovely School of Computer Science and Engineering
LOVELY PROFESSIONAL UNIVERSITY
PHAGWARA, PUNJAB
2. 2
Certificate
This is to certify that Qazi Maaz Arshad bearing Registration no.
11906424 has successfully completed INT 217 (Introduction to Data
Management) project titled, “An Excel Dashboard – Olympics
Statistics” under my guidance and supervision. To the best of my
knowledge, the present work is the result of his original development,
effort, and study.
Sameeksha Khare - 26806
School of Computer Science and Engineering
Lovely Professional University
Phagwara, Punjab.
Date
3. 3
Student Declaration
I, Qazi Maaz Arshad, 11906424 a student of B.Tech Computer
Science and Engineering under CSE/IT Discipline at, Lovely
Professional University, Punjab, hereby declare that all the
information furnished in this project report is based on my own
intensive work and is genuine.
Qazi Maaz Arshad
11906424 - RKM039B50
B. Tech - Computer Science and Engineering
Lovely Professional University, Phagwara
Maaz
28/11/2021
4. 4
Acknowledgement
The opportunity of attaining a course based on Data Management
using Excel at Lovely Professional University was worth learning. It
was a prestige for me to be part of it. During the period of my course,
I received tremendous knowledge related to Microsoft Excel and
Data Management.
Pre-eminently, I would like to express my deep gratitude and special
thanks to my course teacher Sameeksha Khare for her theoretical
knowledge and encouragement on this project and for her valuable
guidance and affection for the successful completion of this project.
Secondly, I would like to thank Lovely Professional University for
giving me an opportunity to learn this course.
Lastly, I would like to thank the almighty and my parents for their
constant encouragement, moral support, personal attention, and care.
Qazi Maaz Arshad
11906424 - RKM039B50
B. Tech - Computer Science and Engineering
Lovely Professional University, Phagwara
Maaz
6. 6
List of Tables and Figures
Sr. No. Table and Figure Name Page No.
3.1 Raw Data Set 13
4.1 Raw Data from Kaggle 14
4.2 Importing CSV Data Sets 15
4.3 Loading to Connection 15
4.4 Transforming Data 16
4.5 Deleting columns in Power Query Editor 16
4.6 Table After Transforming in Power Query
Editor
17
4.7 All the Queries and Connections 17
4.8 Combining Queries 18
4.9 Merging Tables 18
4.10 Selecting Columns to be Included in
Merged Table
18
4.11 Loading Data in Excel 19
4.12 Data Set After Transforming and Loading 19
4.13 Deleting Columns 20
4.14 Renaming Columns 20
4.15 Applying Filter 21
4.16 Filtering Out Unwanted Values 21
4.17 Filtering Out Unwanted Values 22
4.18 Aligning Data in Cells 23
4.19 Applying Style to Table 23
4.20 Clean Data Set 23
7. 7
5.1 Objective 1 - Pivot Table 1 24
5.2 Objective 1 - Pivot Table 2 25
5.3 Objective 1 - Pivot Table 3 25
5.4 Objective 1 - Pivot Table 4 26
5.5 Objective 1 – Calculation for Doughnut
Chart
26
5.6 Objective 1 Result 27
5.7 Objective 2 Pivot Table and Settings 27
5.8 Objective 2 Result 28
5.9 Objective 3 Pivot Table and Settings 29
5.10 Objective 3 Result 29
5.11 Objective 4 Pivot Table and Settings 30
5.12 Objective 4 Result 30
5.13 Objective 5 Pivot Table and Settings 31
5.14 Objective 5 Result 31
5.15 Objective 6 Pivot Table and Settings 32
5.16 Objective 6 Result 32
5.17 Objective 7 Pivot Table and Settings 33
5.18 Objective 7 Result 33
5.19 Objective 8 Pivot Table and Settings 34
5.20 Objective 8 Result 35
5.21 Objective 9 Pivot Table and Settings 36
5.22 Objective 9 Result 36
5.23 Objective 10 Pivot Table and Settings 37
5.24 Objective 10 Result 37
8. 8
5.25 Objective 11 Pivot Table and Settings 38
5.26 Objective 11 Result 38
5.27 Objective 12 Pivot Table and Settings 39
5.28 Objective 12 Result 40
5.29 Objective 13 Pivot Table and Settings 41
5.30 Objective 13 Custom Sort 41
5.31 Objective 13 Result 41
5.32 Objective 14 Pivot Table and Settings 42
5.33 Objective 14 Custom Sort 43
5.34 Objective 14 Result 43
6.1 The Dashboard 44
6.2 Dark/Light Toggle Switch 45
6.3 Dark/Light Theme 46
6.4 Hidden Links 47
6.5 Shortcut Links to Dashboard and Master
Sheet
47
6.6 The Master Sheet 48
9. 9
Abstract
Excel is a software program created by Microsoft that uses
spreadsheets to organize numbers and data with formulas and
functions. Excel analysis is ubiquitous around the world and used by
businesses of all sizes to perform data analysis. Excel features
calculation, graphing tools, pivot tables, and a macro programming
language called Visual Basic for Applications, and several other
features which make Excel a perfect choice to manage and analyze
data. My project is an Excel Dashboard. The Excel Dashboard is used
to display overviews of large data tracks. Excel Dashboards use
dashboard elements like tables, charts, and gauges to show the
overviews. The dashboards ease the decision-making process by
showing the vital parts of the data in the same window. In this report,
I have shared a project where I have done data analysis of an
Olympics data set. This report also presents my learning during my
course classes.
10. 10
Chapter 1 - Introduction
I have created an Excel dashboard of an Olympics data set. This
dashboard explains and highlights important facts, records, and trends
in the Olympics history.
The data set used contains information regarding all the previous
Winter and Summer Olympics. It includes information regarding all
the participants involved in the games, the participating nations, the
games played, when the Olympics were held, who was the host
country, which participants won medals, what medals they won (gold,
silver, bronze), what was the age, height, and weight of the players.
The data set contains details of Approx. 271117 players.
I have scrubbed and organized the entire data set and performed the
analysis of a clean data set. I have deduced and calculated important
results from the data set with the help of various Excel features like
pivot tables and functions and represented them in the form of a
dynamic dashboard using excel visualizing tools and various charts.
Chapter 2 - Objectives
This project on Olympics statistics provides the records, facts, and
trends of all the Summer and Winter Olympics since 1896 with
respect to participants, nations, and games in various aspects.
However, here are the few main objectives that are discussed in the
dashboard.
Showing the total number of participants and winners, with
respect to their game, nationality, age, year of participation,
gender, and many more.
Showing the performance of countries overall, or in a particular
year, game, gender-based contributions, etc.
11. 11
Showing the performance of players overall, or in a particular
year, game, gender-based contributions, or of a particular
country. etc.
Showing the most popular sports in the Olympics overall, or
among males/females, participation index of a particular nation
in a particular sport, etc.
Highlighting the relationship between medal victory and age of
players overall, in a particular season, in a specific sport or
nation, etc.
Highlighting the relationship between medal victory and height
and weight of players overall, in a particular season, in a
specific sport or nation, etc.
Showing the male vs female medal victory ratio in the
Olympics, overall, in a particular season, in a specific sport or
nation, etc.
Displaying trends in countries performance over the years with
respect to a particular sport, gender specific, only in a particular
season, etc.
Highlighting the difference in countries performance in Summer
and Winter Olympics.
Showing participation index with respect to the host country.
Chapter 3 - Source of Dataset
The dataset is taken from Kaggle. Kaggle is a community of data
scientists and data enthusiasts. This platform allows users to find and
publish data sets.
I have selected an Olympics data set which contains important details
of 120 years of Olympics History.
Here are the details of my chosen data set.
12. 12
Name - 120 years of Olympic history: athletes and results
Link - https://www.kaggle.com/heesoo37/120-years-of-olympic-
history-athletes-and-results
Author – rgriffin
Format – CSV
No. of Data Sets – 2
Size – 42 MB
No. of Rows – (1) 271116 + (2) 230
No. of Columns – (1) 15 + (2) 3
Data Set 1: Data Fields
ID – (Integer) – Each player has a unique id
Name – (String) – Name of player
Sex – (Char) – M represents males, F represents Females
Age – (Integer) – Shows age of players in years
Height – (Integer) – Height of players in Centimeter (cm)
Weight – (Integer) – Weight of players in Kilograms (Kg)
Team – (String) – Name of sports team of players
NOC – (String) – Initials of countries
Games – (String) – Represents year and season ex. 2020 Summer
Year – (Date) – Year of player participation
Season – (String) – Identifies the season Summer or Winter
City – (String) – Gives the name of the host country
Sport – (String) – Name of the sport. ex. Badminton
Event – (String) – Sub-event of the sport. ex. Men’s Single
Badminton
Medal – (String) – Shows medal won Gold, Silver, Bronze, or
NA if not won
Data Set 2: Data Fields
NOC – (String) – Initials of countries
Region – (String) – Gives the name of the country/place
Notes – (String) – Details about the region
13. 13
Table 3.1 – Raw Data Set
Data Set 1 Data Set 2
Chapter 4 - ETL Process
In computing, extract, transform, load (ETL) is a process to prepare
data for analysis, especially in data warehousing. Data extraction
involves extracting data from homogeneous or heterogeneous sources,
while data transformation processes data by transforming them into a
proper storage format/structure for the purposes of querying and
analysis; finally, data loading describes the insertion of data into the
final target location such as an operational data store, a data mart, or a
data warehouse. A properly designed ETL system extracts data from
the source systems, enforces data quality and consistency standards,
conforms data so that separate sources can be used together, and
finally delivers data in a presentation-ready format so that application
14. 14
developers can build applications and end users can make decisions.
I have also performed many steps in the ETL process to prepare my
data for analysis:
Extraction
The raw data has been taken from Kaggle, before processing the data
it looked like this
Table 4.1 – Raw Data from Kaggle
15. 15
The data can be imported into excel directly from the web using get
data features, but I have first downloaded the CSV files manually
from excel then imported it into excel using the get data feature.
Step 1 – Open a new excel workbook.
Step 2 – Use the Get Data feature.
Table 4.2 – Importing CSV Data Sets
Step 3 – Load to Connection
Table 4.3 – Loading to Connection
16. 16
Step 4 – Repeat the process for all the data sets. In my case there
were only two data sets.
Now that we have extracted the data from the source and have
imported it, now is the time to transform the data.
Transform
If we want to transform the data before loading it can be done.
Although transformation can be done even after loading the data, but
it is better to first process the data before loading.
Step 1 – Use Get Data feature. But instead of loading to connection,
click on transform.
Table 4.4 - Transforming Data
Step 2 – Remove the unwanted rows or column or modify the data in
Power Query Editor.
Table 4.5 – Deleting columns in Power Query Editor
17. 17
Now I have removed the unwanted column in the data set.
Table 4.6 – Table After Transforming in Power Query Editor
Now after we had imported and transformed all the data sets it will
look like this
Table 4.7 – All the Queries and Connections
Step 3 – Merging both data sets. In data set 1 I have the name of
country as NOC (only initials of the country) which makes it difficult
to identify the country, in the other data set I have the full names of
countries, so I want to merge the regions column (country name) of
data set 2 to data set 1, comparing the NOC column.
18. 18
To merge queries, use the Get Data feature -> Use Combine Queries
-> Merge
Table 4.8 – Combining Queries
Step 4 – Now select the base table, merging table, comparing column
and other necessary inputs.
Table 4.9 – Merging Tables
Step 5 – Now select the columns we want to see in the merge table.
Table 4.10 – Selecting Columns to be Included in Merged Table
19. 19
Now that we have the desired data, we can finally load it into excel
workbook.
Load
When we have the desired data, we can load it into the excel.
Table 4.11 – Loading Data in Excel
This is the data we get after extracting, transforming, and merging the
two data sets
Table 4.12 – Data Set After Transforming and Loading
20. 20
But the resultant still has some inconsistencies.
Like NA values in few cells, and it still has few columns which I
don’t want to use, so it will be better to modify and eliminate the
inconsistent data.
Transform 2.0
Step 1 – Deleting the unwanted columns.
Table 4.13 – Deleting Columns
Step 2 – Renaming the columns
Table 4.14 – Renaming Columns
21. 21
Step 3 – Deleting the rows with null and unwanted values.
3.1 -> Apply filter
Table 4.15 – Applying Filter
3.2 -> Select the column which contains values that need to be
deleted. Then unselect the value to be deleted.
Table 4.16 – Filtering Out Unwanted Values
3.3 -> Repeat the process for all the columns in which we want to
delete the unwanted values.
22. 22
3.4 -> When done, select the entire sheet (ctrl + A), copy entire sheet,
create a new sheet, and paste.
Table 4.17 – Filtering Out Unwanted Values
Now we have deleted all the unnecessary things from our data.
Step 4 – It will be better to arrange data in cells properly and apply
style.
23. 23
Table 4.18 – Aligning Data in Cells
Table 4.19 – Applying Style to Table
Final Clean Data Set
Table 4.20 – Clean Data Set
24. 24
Chapter 5 - Data Analysis
Objective 1 – Displaying Number of Participants
Description – The objective is to display the count of number of
participants and winners, both male and female separately (of
different games, years, age, and many other).
Requirements –
4 Pivot Tables
Divide (/) and Subtract (-) Formula, Get Pivot Data Formula
Doughnut Charts (Customized Pie Chart + Text Box)
Many Slicers
Specifications -
Pivot Table 1 – To count total males, females, and participants
Table 5.1 – Objective 1 - Pivot Table 1
25. 25
Pivot Table 2 – To count total medals won
Table 5.2 – Objective 1 - Pivot Table 2
Pivot Table 3 – To count medals won by males
Table 5.3 – Objective 1 - Pivot Table 3
26. 26
Pivot Table 4 – To count medals won by females
Table 5.4 – Objective 1 - Pivot Table 4
Percentage Calculation – For Doughnut Chart
Table 5.5 – Objective 1 – Calculation for Doughnut Chart
27. 27
Result and Visualization –
Table 5.6 – Objective 1 Result
Objective 2 – Showing Best Performing Countries
Description – The objective is to display the best performing
countries. Also, based on different sports, year, gender, and many
other fields.
Requirements –
Pivot Table
Pivot Chart
Many Slicers
Specifications – To display a countries performance, In the pivot
table fields add Country in the rows, and medals as column, and count
of medal in the values column.
Uncheck NA option in the medals column and apply Top 10 value
filter to display only the top 10 countries.
Also, apply largest to smallest filter in gold medal column, because
medal determines the ranking.
After all the steps, insert a column chart in the pivot table.
Table 5.7 – Objective 2 Pivot Table and Settings
28. 28
Result and Visualization –
Table 5.8 – Objective 2 Result
Objective 3 – Displaying Top Olympic Medalists
Description – The objective is to display the best performing players.
Also, based on different sports, year, gender, and many other fields.
Requirements –
Pivot Table
Pivot Chart
Many Slicers
Specifications – To display a players performance, In the pivot table
fields add Name in the rows, and medals as column, and Count of
Medal in the values column.
Uncheck NA option in the medals column and apply Top 10 value
filter to display only the top 10 players.
Also, apply largest to smallest filter in gold medal column, because
medal determines the ranking.
After all the steps, insert a column chart in the pivot table.
Then apply the required slicers.
29. 29
Table 5.9 – Objective 3 Pivot Table and Settings
Result and Visualization –
Table 5.10 – Objective 3 Result
Objective 4 – Male vs Female Performance Ratio
Description – The objective is to find the Male vs Female medal
victory ratio. Also, based on sports, nation, year, season, and a lot
more.
Requirements –
Pivot Table
Pivot Chart
Many Slicers
30. 30
Specifications – To display Male vs Female ratio, In the pivot table
fields add Sex then Medal in the rows, and count of medal in the
values column.
Uncheck NA option in the medals.
After all the steps, insert a pie chart in the pivot table.
Then apply the required slicers.
Table 5.11 – Objective 4 Pivot Table and Settings
Result and Visualization –
Table 5.12 – Objective 4 Result
31. 31
Objective 5 – Show Most Popular Sports
Description – The objective is to display the sports which record
highest participation. Also, with respect to different country, year,
gender, and many other fields.
Requirements –
Pivot Table
Pivot Chart
Many Slicers
Specifications – To get sports with highest participation index, In the
pivot table fields add Sport in the rows, and Sex as column, and Count
of Name in the values column.
Apply Top 10 value filter and limit to 12 to display 12 most popular
sports.
After all the steps, insert a Doughnut chart in the pivot table.
Then apply the required slicers.
Table 5.13 – Objective 5 Pivot Table and Settings
Result and Visualization –
Table 5.14 – Objective 5 Result
32. 32
Objective 6 – Relation Between Medal Victory and Age
Description – The objective is to display the relationship and trend
between medal victory and age. Also, for different sports, year,
gender, and many other fields.
Requirements –
Pivot Table
Pivot Chart
Many Slicers
Specifications – To find the relationship between medal victory and
age, In the pivot table fields add Age in the rows, and Medal in
column, and Count of Medal in the values column.
Uncheck NA option in the medals column and apply Top 20 value
filter to display only the stats of only top 20 ages.
After all the steps, insert a line chart in the pivot table.
Then apply the required slicers.
Table 5.15 – Objective 6 Pivot Table and Settings
Result and Visualization –
Table 5.16 – Objective 6 Result
33. 33
Objective 7 – Trends in Countries Performance in Years
Description – To display countries performance over the years. Also,
in different sports, based on gender, and many other fields.
Requirements –
Pivot Table
Pivot Chart
Many Slicers
Specifications – To display a countries performance over the years,
In the pivot table fields add Country in the rows then add Year, and
Count of Medal in the values column.
Uncheck NA option in the medals column and apply Top 1 value
filter to display only 1 country at a time.
Also, apply largest to smallest filter in Age column, so trend can be
easily understood.
After all the steps, insert a line chart in the pivot table.
Then apply the required slicers.
Table 5.17 – Objective 7 Pivot Table and Settings
Result and Visualization –
Table 5.18 – Objective 7 Result
34. 34
Objective 8 – Difference in Country Performance
(Summer vs Winter) Olympics
Description – To show the difference in medal victory of a nation in
Summer and Winter Olympics. Also, in different sports, gender-
specific, and many other fields.
Requirements –
Pivot Table
Pivot Chart
Many Slicers
Specifications – To show the difference in Summer and Winter
Olympics performance of a country, In the pivot table fields add
Country in the rows then add Season again in the rows, add Medal in
columns, and Count of Medal in the values column.
Uncheck NA option in the medals column and apply Top 1 value
filter in pivot table to display only 1 country at a time.
After all the steps, insert a bar chart in the pivot table.
Then apply the required slicers.
Table 5.19 – Objective 8 Pivot Table and Settings
35. 35
Result and Visualization –
Table 5.20 – Objective 8 Result
Objective 9 – Relationship: Medal Victory vs Weight
Description – The objective is to establish a relationship between
medal victory and weight. Also, for different sports, nation, gender-
specific, and many more.
Requirements –
Pivot Table
Pivot Chart
Many Slicers
Specifications – To establish a relationship between medal victory
and weight, In the pivot table fields add Weight in the rows, add
Medal in columns, and Count of Medal in the values column.
Uncheck NA option in the medals column and apply Top 10 value
filter in pivot table to display 10 weights only.
After all the steps, insert an area chart in the pivot table.
Then apply the required slicers.
36. 36
Table 5.21 – Objective 9 Pivot Table and Settings
Result and Visualization –
Table 5.22 – Objective 9 Result
Objective 10 – Relationship: Medal Victory vs Height
Description – The objective is to establish a relationship between
medal victory and height. Also, for different sports, nation, gender-
specific, and many more.
Requirements –
Pivot Table
Pivot Chart
Many Slicers
37. 37
Specifications – To establish a relationship between medal victory
and height, In the pivot table fields add Height in the rows, add Medal
in columns, and Count of Medal in the values column.
Uncheck NA option in the medals column and apply Top 10 value
filter in pivot table to display 10 heights only.
After all the steps, insert an area chart in the pivot table.
Then apply the required slicers.
Table 5.23 – Objective 10 Pivot Table and Settings
Result and Visualization –
Table 5.24 – Objective 10 Result
38. 38
Objective 11 – Show Player Participation Index of Host
Country
Description – The objective is to show the male/female players
strength in the host country. Also, for different sports, nation, and
many more.
Requirements –
Pivot Table
Pivot Chart
Many Slicers
Specifications – To show the player participation index of the host
countries, In the pivot table fields add City in the rows, add Sex in
columns, and Count of Name in the values column.
Apply largest to smallest filter in cities column in pivot table.
After all the steps, insert a column chart in the pivot table.
Then apply the required slicers.
Table 5.25 – Objective 11 Pivot Table and Settings
Result and Visualization –
Table 5.26 – Objective 11 Result
39. 39
Objective 12 – Show Most Dominant Countries in
Olympics using World Map
Description – The objective is show countries using world map
which have won most medals in the Olympics.
Requirements –
Pivot Table
Normal Table
Map Chart
Specifications – Map charts don’t work with pivot tables, so we need
to create a normal table, but first fetch the data from the data set using
pivot table. In the pivot table fields add Country in the rows, add
Medal in columns, and Count of Medal in the values column.
Uncheck NA option in the medal’s column.
Then copy the Country and Grand Total column from the pivot
table to a new sheet.
Once done, then insert the Map chart in the table.
Table 5.27 – Objective 12 Pivot Table and Settings
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Result and Visualization –
Table 5.28 – Objective 12 Result
Objective 13 – Show Top 10 Olympics Players Overall
Description – The objective is show top 10 Olympic medalists by
standard ranking definition of Olympics.
Requirements –
Pivot Table
Normal Table
Sort Filter
Bar Chart
Specifications – Multiple custom sorting does not work with pivot
tables, so we need to create a normal table, but first fetch the data
from the data set using pivot table. In the pivot table fields add Name
in the rows, add Medal in columns, and Count of Medal in the values
column.
Uncheck NA option in the medal’s column.
Then copy the entire Pivot table excluding the first row (i.e
column header) to a new sheet.
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Table 5.29 – Objective 13 Pivot Table and Settings
Once done, then apply custom sort in the new table.
First sort by Gold – largest to smallest, then by Silver - largest to
smallest, then by Bronze - largest to smallest.
At last, just select top 10 manually and insert a column chart.
Table 5.30 – Objective 13 Custom Sort
Result and Visualization –
Table 5.31 – Objective 13 Result
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Objective 14 – Show Top 10 Nations in Olympics Overall
Description – The objective is show top 10 countries by standard
ranking definition of Olympics.
Requirements –
Pivot Table
Normal Table
Sort Filter
Bar Chart
Specifications – Multiple custom sorting does not work with pivot
tables, so we need to create a normal table, but first fetch the data
from the data set using pivot table. In the pivot table fields add
Country in the rows, add Medal in columns, and Count of Medal in
the values column.
Uncheck NA option in the medal’s column.
Then copy the entire Pivot table excluding the first row (i.e
column header) to a new sheet.
Table 5.32 – Objective 14 Pivot Table and Settings
Once done, then apply custom sort in the new table.
First sort by Gold – largest to smallest, then by Silver - largest to
smallest, then by Bronze - largest to smallest.
At last, just select top 10 manually and insert a column chart.
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Table 5.33 – Objective 14 Custom Sort
Result and Visualization –
Table 5.34 – Objective 14 Result
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I have created this dashboard by combining all the results obtained in
the data analysis, then connecting the required slicers, adding images,
objects, and style to the dashboard.
This dashboard can be used to find various, records, stats, and trends
in the Olympics like:
Which is the best performing country in Winter Olympics.
Who won most bronze medals in hockey.
Which female wrestler of India has won an Olympic medal.
What is the male/female participation and victory index of Nigeria.
How many players have never won a medal.
Who was the youngest female gold medalist in archery.
What is the most optimum height and weight that has produced most
medalists in swimming.
How many gold medals USA won in 2016 Summer Olympics.
Which host city recorded the highest participants in ice skating.
etc. and a lot more data can be easily deduced from this dashboard,
and not only just number but the result will be graphically
represented.
This dashboard comes with some special features as well.
Special Features
This dashboard has many special features some are hidden, and some
are visible. Important features are:
Dark/Light theme switch – This toggle switch allows user to change
the theme color of the dashboard from white to black (light to dark).
Excel does not provide such features; this switch has been added with
a trick. This toggle switch is just an Image, and this image is linked
to another copy of this dashboard which has all charts with dark.
Figure 6.2 – Dark/Light Toggle Switch
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Hidden Links and Master Sheet – The dashboard has many hidden
links which takes user directly to the source data and source pivot
table which helps to understand the architecture of specific parts of
the dashboard.
Figure 6.4 – Hidden Links
Also every single sheet also has shortcut links which takes directly to
dashboard and Master Sheet.
Figure 6.5 – Shortcut Links to Dashboard and Master Sheet
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Master Sheet – A master sheet is a sheet in the workbook which has
links to all the sheets of the workbook. It makes it easier to navigate
in the workbook.
Figure 6.6 – The Master Sheet
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Summary of Report
In this report I have discussed in detail my project, its working,
making, features, and applications. I have explained each step of
making an Olympics Statistics Dashboard using from a raw data using
Excel.
This report highlights all the processes involved in the making in
serial order from ETL processes extracting, transforming, and loading
data to using several excel features like pivot table, filtering, sorting,
formulas to perform data analysis and deduce important results then
representing them graphically using charts.
I have also attached the preview of the dashboard, and all the
objectives in this report.
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References
120 years of Olympic history: athletes and results (Data Set) -
https://www.kaggle.com/heesoo37/120-years-of-olympic-history-
athletes-and-results. (Accessed on 20th Nov 2021).
https://exceljet.net/excel-pivot-tables. (Accessed on 23rd Nov
2021).