This article describes a free, open-source collection of templates for the popular Excel (2013, and later versions) spreadsheet program. These templates are spreadsheet files that allow easy and intuitive learning and the implementation of practical examples concerning descriptive statistics, random variables, confidence intervals, and hypothesis testing. Although they are designed to be used with Excel, they can also be employed with other free spreadsheet programs (changing some particular formulas). Moreover, we exploit some possibilities of the ActiveX controls of the Excel Developer Menu to perform interactive Gaussian density charts. Finally, it is important to note that they can be often embedded in a web page, so it is not necessary to employ Excel software for their use. These templates have been designed as a useful tool to teach basic statistics and to carry out data analysis even when the students are not familiar with Excel. Additionally, they can be used as a complement to other analytical software packages. They aim to assist students in learning statistics, within an intuitive working environment. Supplementary materials with the Excel templates are available online.

Vrinda Store: Interactive Ms Excel dashboardVrinda Store: Interactive Ms Excel dashboard Feb 2024 - Mar 2024Feb 2024 - Mar 2024 The owner of Vrinda store wants to create an annual sales report for 2022. So that their employees can understand their customers and grow more sales further. Questions asked by Owner of Vrinda store are as follows:- 1) Compare the sales and orders using single chart. 2) Which month got the highest sales and orders? 3) Who purchased more - women per men in 2022? 4) What are different order status in 2022?

And some other questions related to business. The owner of Vrinda store wanted a visual story of their data. Which can depict all the real time progress and sales insight of the store. This project is a Ms Excel dashboard which presents an interactive visual story to help the Owner and employees in increasing their sales. Task performed : Data cleaning, Data processing, Data analysis, Data visualization, Report. Tool used : Ms Excel The owner of Vrinda store wants to create an annual sales report for 2022. So that their employees can understand their customers and grow more sales further. Questions asked by Owner of Vrinda store are as follows:- 1) Compare the sales and orders using single chart. 2) Which month got the highest sales and orders? 3) Who purchased more - women per men in 2022? 4) What are different order status in 2022? And some other questions related to business. The owner of Vrinda store wanted a visual story of their data. Which can depict all the real time progress and sales insight of the store. This project is a Ms Excel dashboard which presents an interactive visual story to help the Owner and employees in increasing their sales. Task performed : Data cleaning, Data processing, Data analysis, Data visualization, Report. Tool used : Ms Excel Skills: Data Analysis · Data Analytics · ms excel · Pivot Tables

The CCQM_Retrospectoscope system combines a nominally complete database of results from Consultative Committee for the Amount of Substance: Metrology in Chemistry and Biology (CCQM) studies with a number of graphical tools for trying to make sense of the data. This system supports a diverse collection of often eye-opening appraisals of participation and measurement performance throughout the history of the CCQM activities. The appraisals include the bias, uncertainty, and degrees of equivalence of results submitted by individual national metrology or designated institutes (NMI|DIs); the relative performance of NMI|DIs, and the uncertainty function characteristic of entire Working Groups (WGs). The system is implemented in Excel using Microsoft?s Visual Basic for Applications (VBA) programs. It runs on both Windows and Macintosh platforms.

This project focuses on developing a machine learning-driven system to classify hospital claims and treatment outcomes, offering a second opinion on healthcare costs and decision-making for insurance claims and treatment efficacy.Key Features and Tools:Machine Learning Algorithms: Leveraging Python (pandas, numpy, scikit-learn) for predictive modeling to assess claim validity and treatment outcomes.APIs Integration: Used Google Maps API to retrieve and map the locations of private hospitals in Malaysia.GIS Mapping Dashboard: Created a GIS-enabled dashboard in Microsoft Power BI to visualize private hospital distribution across Malaysia, aiding healthcare planning and analysis.Advanced Analytics Tools: Integrated Microsoft Excel, Python, and Google Collab for data processing and automation workflows.

The global spreadsheets software market is experiencing robust growth, driven by increasing digitalization across industries and the rising adoption of cloud-based solutions. The market, estimated at $20 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033, reaching approximately $35 billion by 2033. This growth is fueled by several factors, including the expanding need for data analysis and visualization across SMEs and large enterprises, the increasing availability of user-friendly and feature-rich spreadsheet software, and the growing preference for collaborative tools that facilitate seamless teamwork. The market is segmented by operating system (Windows, Macintosh, Linux, Others) and user type (SMEs, Large Enterprises). While Microsoft Excel maintains a dominant market share, the rise of cloud-based alternatives like Google Sheets and collaborative platforms is challenging this dominance, fostering competition and innovation. Furthermore, the increasing integration of spreadsheets with other business intelligence tools further enhances their utility and fuels demand. Geographic expansion, particularly in developing economies with rising internet penetration, also contributes significantly to market expansion. However, factors such as the high initial investment in software licenses and the need for specialized training can restrain market growth, particularly for smaller businesses with limited budgets and technical expertise. The increasing complexity of data analysis necessitates enhanced security features and data protection measures, which add cost and require ongoing investment. Moreover, the emergence of advanced analytical tools and specialized data visualization software presents a competitive challenge, demanding continuous innovation and adaptation from existing spreadsheet software providers. Nevertheless, the overall market outlook remains positive, driven by sustained demand from diverse industries and technological advancements within the software landscape.

Two bootstrap tools are provided in the form of Excel spreadsheets. One tool is to compute means and confidence intervals from user provided data. The other tool computes p-values for significant difference testing of two user provided data sets. All means are weighted with weights provided by the user. Instructions are provided for each Excel spreadsheet tool. Download the tools as "Original Format".

📊 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 is the excel spreadsheet dataset containing our analysis of papers performing mining software repositories research from the conferences ICSE, ESEC/FSE, and MSR from the years 2018 - 2020. The data is broken into columns and can be explained at a high-level as follows:

Column Content

1 The paper being analyzed

2 Does the paper state the data they analyzed is available

3 Does the paper perform some sort of data analysis or sampling using data others have compiled in the past

4 Does the paper state a timestamp for when they begin their work

5 Does the paper state the use of systems pre-built to help with MSR work

6 - 18 Forms of sampling researchers may have employed to select their data

19 What datasets (if any) were used in the analysis

20 What tools (if any) were used in the analysis

21 How they performed their data sampling workflow

22 How they performed their data filtering workflow

23 How they performed their data retrieval workflow

24 Did they create any scripts in each of these workflows

25 - 33 Did they publish a replication package and what is contained within

34 Is the paper describing a tool for research or not

35 Short description of the paper read

36 A high-level category of the work performed in each paper

To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.

Sheet 1 (Raw-Data): The raw data of the study is provided, presenting the tagging results for the used measures described in the paper. For each subject, it includes multiple columns: A. a sequential student ID B an ID that defines a random group label and the notation C. the used notation: user Story or use Cases D. the case they were assigned to: IFA, Sim, or Hos E. the subject's exam grade (total points out of 100). Empty cells mean that the subject did not take the first exam F. a categorical representation of the grade L/M/H, where H is greater or equal to 80, M is between 65 included and 80 excluded, L otherwise G. the total number of classes in the student's conceptual model H. the total number of relationships in the student's conceptual model I. the total number of classes in the expert's conceptual model J. the total number of relationships in the expert's conceptual model K-O. the total number of encountered situations of alignment, wrong representation, system-oriented, omitted, missing (see tagging scheme below) P. the researchers' judgement on how well the derivation process explanation was explained by the student: well explained (a systematic mapping that can be easily reproduced), partially explained (vague indication of the mapping ), or not present.

Tagging scheme:
Aligned (AL) - A concept is represented as a class in both models, either

with the same name or using synonyms or clearly linkable names; Wrongly represented (WR) - A class in the domain expert model is incorrectly represented in the student model, either (i) via an attribute, method, or relationship rather than class, or (ii) using a generic term (e.g., user'' instead ofurban planner''); System-oriented (SO) - A class in CM-Stud that denotes a technical implementation aspect, e.g., access control. Classes that represent legacy system or the system under design (portal, simulator) are legitimate; Omitted (OM) - A class in CM-Expert that does not appear in any way in CM-Stud; Missing (MI) - A class in CM-Stud that does not appear in any way in CM-Expert.

All the calculations and information provided in the following sheets

originate from that raw data.

Sheet 2 (Descriptive-Stats): Shows a summary of statistics from the data collection,

including the number of subjects per case, per notation, per process derivation rigor category, and per exam grade category.

Sheet 3 (Size-Ratio):

The number of classes within the student model divided by the number of classes within the expert model is calculated (describing the size ratio). We provide box plots to allow a visual comparison of the shape of the distribution, its central value, and its variability for each group (by case, notation, process, and exam grade) . The primary focus in this study is on the number of classes. However, we also provided the size ratio for the number of relationships between student and expert model.

Sheet 4 (Overall):

Provides an overview of all subjects regarding the encountered situations, completeness, and correctness, respectively. Correctness is defined as the ratio of classes in a student model that is fully aligned with the classes in the corresponding expert model. It is calculated by dividing the number of aligned concepts (AL) by the sum of the number of aligned concepts (AL), omitted concepts (OM), system-oriented concepts (SO), and wrong representations (WR). Completeness on the other hand, is defined as the ratio of classes in a student model that are correctly or incorrectly represented over the number of classes in the expert model. Completeness is calculated by dividing the sum of aligned concepts (AL) and wrong representations (WR) by the sum of the number of aligned concepts (AL), wrong representations (WR) and omitted concepts (OM). The overview is complemented with general diverging stacked bar charts that illustrate correctness and completeness.

For sheet 4 as well as for the following four sheets, diverging stacked bar

charts are provided to visualize the effect of each of the independent and mediated variables. The charts are based on the relative numbers of encountered situations for each student. In addition, a "Buffer" is calculated witch solely serves the purpose of constructing the diverging stacked bar charts in Excel. Finally, at the bottom of each sheet, the significance (T-test) and effect size (Hedges' g) for both completeness and correctness are provided. Hedges' g was calculated with an online tool: https://www.psychometrica.de/effect_size.html. The independent and moderating variables can be found as follows:

Sheet 5 (By-Notation):

Model correctness and model completeness is compared by notation - UC, US.

Sheet 6 (By-Case):

Model correctness and model completeness is compared by case - SIM, HOS, IFA.

Sheet 7 (By-Process):

Model correctness and model completeness is compared by how well the derivation process is explained - well explained, partially explained, not present.

Sheet 8 (By-Grade):

Model correctness and model completeness is compared by the exam grades, converted to categorical values High, Low , and Medium.

The global spreadsheet software market is experiencing robust growth, driven by the increasing adoption of cloud-based solutions and the rising demand for data analysis tools across various industries. The market, estimated at $50 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $150 billion by the end of the forecast period. This growth is fueled by several key factors. Firstly, the increasing reliance on data-driven decision-making across businesses, irrespective of size, necessitates efficient data management and analysis capabilities provided by spreadsheet software. Secondly, the proliferation of cloud-based spreadsheet applications offers enhanced collaboration, accessibility, and scalability, making them attractive to organizations of all sizes. Finally, continuous advancements in features like advanced analytics, data visualization, and integration with other business applications enhance the overall utility and appeal of these tools. Major players like Microsoft, Google, and Zoho are continuously innovating, adding new features and improving user experience to maintain their market leadership. However, the market also faces challenges. Security concerns related to data storage and access in cloud-based solutions, and the need for continuous training and upskilling to leverage advanced features, pose limitations to wider adoption. Despite these challenges, the long-term outlook for the spreadsheet software market remains positive. The increasing digitization of businesses and the expanding adoption of big data analytics will propel demand for sophisticated spreadsheet tools. The emergence of niche players focusing on specific industry needs and specialized functionalities will also contribute to market expansion. Competition will remain fierce among established players and newcomers, prompting innovation and improvement in the overall product offerings. The market will witness consolidation through mergers and acquisitions, and a shift towards subscription-based models, further driving market growth and shaping the competitive landscape. The geographic distribution of the market will see continued growth in developing economies, driven by increasing internet penetration and smartphone adoption.

An excel table with grainsize distribution data and statistics, in micrometres and phi. 13 samples. Data measured with laser diffraction.

Go Linter Evaluation Dataset

This is a publicly available dataset for 'An empirical evaluation of Golang static code analysis tools for real-world issues.' Please refer to the data according to the names of the spreadsheets.

Authors: Jianwei Wu, James Clause

Collected Survey Data:
- This Excel file contains the collected survey data for the empirical study in details.

R Scripts and Raw Data:
- These scripts are used for data analysis and processing.
- This is the initial data collected from surveys or other sources before any processing or analysis.

Surveys for External Participants:
- This Excel file contains survey data collected for the evaluation of Go linters.
- This folder contains the surveys sent to external participants for collecting their feedback or data.

Recruitment Letter.pdf:
- This PDF contains an example of the recruitment letter sent to potential survey participants, inviting them to take part in the study.

Outputs from Existing Go Linters and Summarized Categories.xlsx:
- This Excel file contains outputs from various Go linters and categorized summaries of these outputs. It helps in comparing the performance and features of different linters.

Selection of Go Linters.xlsx:
- This Excel file lists the Go linters selected for evaluation, along with criteria or reasons for their selection.

UD IRB Exempt Letter.pdf:
- This PDF contains the Institutional Review Board (IRB) exemption letter from the University of Delaware (UD), indicating that the study involving human participants was exempt from full review.

Survey Template.pdf:
- This PDF contains an example of the survey sent to the participants.

govet issues.pdf:
- This PDF contains a list of reported issues about govet. Collected from various pull requests.

Approved linters:
- staticcheck gofmt govet revive gosec deadcode errcheck.

Table 2.jpg:

- A detailed figure to show the technical data in Table 2 of the paper.

The global spreadsheet editor market is experiencing robust growth, driven by the increasing digitization of businesses and the rising demand for efficient data management solutions across various industries. The market, estimated at $50 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 10% from 2025 to 2033, reaching approximately $130 billion by 2033. This growth is fueled by several factors, including the expanding adoption of cloud-based spreadsheet editors offering enhanced collaboration and accessibility features, the increasing need for data analysis and visualization tools within organizations of all sizes (Large Enterprises and SMBs), and the integration of spreadsheet software with other business applications through APIs offered by companies like Zapier. The free segment holds a significant market share, particularly among individual users and small businesses, while the paid segment, which offers advanced features and support, contributes substantially to overall market revenue. Key players such as Microsoft, Google, and LibreOffice dominate the market, but emerging players are continually introducing innovative features and pricing models to gain a competitive edge. Significant regional variations exist. North America currently holds the largest market share due to high technology adoption and a well-established digital infrastructure, followed by Europe and Asia-Pacific. However, the Asia-Pacific region is anticipated to experience the fastest growth in the forecast period due to rapid technological advancements and increasing internet penetration across countries like India and China. Growth restraints include security concerns related to cloud storage, the cost of implementation and training for complex software, and the increasing competition from specialized data analysis tools. Despite these challenges, the consistent demand for streamlined data management across diverse sectors ensures the continued expansion of the spreadsheet editor market in the coming years. The market’s evolution reflects a shift towards user-friendly, feature-rich, and collaborative solutions that are seamlessly integrated into broader business ecosystems.

Big Data Technology Market Outlook

The global big data technology market size was valued at approximately $162 billion in 2023 and is projected to reach around $471 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 12.6% during the forecast period. The growth of this market is primarily driven by the increasing demand for data analytics and insights to enhance business operations, coupled with advancements in AI and machine learning technologies.

One of the principal growth factors of the big data technology market is the rapid digital transformation across various industries. Businesses are increasingly recognizing the value of data-driven decision-making processes, leading to the widespread adoption of big data analytics. Additionally, the proliferation of smart devices and the Internet of Things (IoT) has led to an exponential increase in data generation, necessitating robust big data solutions to analyze and extract meaningful insights. Organizations are leveraging big data to streamline operations, improve customer engagement, and gain a competitive edge.

Another significant growth driver is the advent of advanced technologies like artificial intelligence (AI) and machine learning (ML). These technologies are being integrated into big data platforms to enhance predictive analytics and real-time decision-making capabilities. AI and ML algorithms excel at identifying patterns within large datasets, which can be invaluable for predictive maintenance in manufacturing, fraud detection in banking, and personalized marketing in retail. The combination of big data with AI and ML is enabling organizations to unlock new revenue streams, optimize resource utilization, and improve operational efficiency.

Moreover, regulatory requirements and data privacy concerns are pushing organizations to adopt big data technologies. Governments worldwide are implementing stringent data protection regulations, like the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States. These regulations necessitate robust data management and analytics solutions to ensure compliance and avoid hefty fines. As a result, organizations are investing heavily in big data platforms that offer secure and compliant data handling capabilities.

As organizations continue to navigate the complexities of data management, the role of Big Data Professional Services becomes increasingly critical. These services offer specialized expertise in implementing and managing big data solutions, ensuring that businesses can effectively harness the power of their data. Professional services encompass a range of offerings, including consulting, system integration, and managed services, tailored to meet the unique needs of each organization. By leveraging the knowledge and experience of big data professionals, companies can optimize their data strategies, streamline operations, and achieve their business objectives more efficiently. The demand for these services is driven by the growing complexity of big data ecosystems and the need for seamless integration with existing IT infrastructure.

Regionally, North America holds a dominant position in the big data technology market, primarily due to the early adoption of advanced technologies and the presence of key market players. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by increasing digitalization, the rapid growth of industries such as e-commerce and telecommunications, and supportive government initiatives aimed at fostering technological innovation.

Component Analysis

The big data technology market is segmented into software, hardware, and services. The software segment encompasses data management software, analytics software, and data visualization tools, among others. This segment is expected to witness substantial growth due to the increasing demand for data analytics solutions that can handle vast amounts of data. Advanced analytics software, in particular, is gaining traction as organizations seek to gain deeper insights and make data-driven decisions. Companies are increasingly adopting sophisticated data visualization tools to present complex data in an easily understandable format, thereby enhancing decision-making processes.

An educational tool using Canadian Nutrient File (CNF) and MS Excel for nutritional analysis of food recipes and dietary assessment from 24-hour food recall. The downloadable template provides detailed instructions and enables users to calculate the energy and nutrient content of food recipes and assess nutrient intake of individuals using the nutrient profile generated for ingredients/foods from the Canadian Nutrient File. The tool is suitable for use in nutrition and dietetic courses, agri-food industries, food service sectors, dietetic practice and research.

Graph Database Market Outlook

The global graph database market size was valued at USD 1.5 billion in 2023 and is projected to reach USD 8.5 billion by 2032, growing at a CAGR of 21.2% from 2024 to 2032. The substantial growth of this market is driven primarily by increasing data complexity, advancements in data analytics technologies, and the rising need for more efficient database management systems.

One of the primary growth factors for the graph database market is the exponential increase in data generation. As organizations generate vast amounts of data from various sources such as social media, e-commerce platforms, and IoT devices, the need for sophisticated data management and analysis tools becomes paramount. Traditional relational databases struggle to handle the complexity and interconnectivity of this data, leading to a shift towards graph databases which excel in managing such intricate relationships.

Another significant driver is the growing adoption of artificial intelligence (AI) and machine learning (ML) technologies. These technologies rely heavily on connected data for predictive analytics and decision-making processes. Graph databases, with their inherent ability to model relationships between data points effectively, provide a robust foundation for AI and ML applications. This synergy between AI/ML and graph databases further accelerates market growth.

Additionally, the increasing prevalence of personalized customer experiences across industries like retail, finance, and healthcare is fueling demand for graph databases. Businesses are leveraging graph databases to analyze customer behaviors, preferences, and interactions in real-time, enabling them to offer tailored recommendations and services. This enhanced customer experience translates to higher customer satisfaction and retention, driving further adoption of graph databases.

From a regional perspective, North America currently holds the largest market share due to early adoption of advanced technologies and the presence of key market players. However, significant growth is also anticipated in the Asia-Pacific region, driven by rapid digital transformation, increasing investments in IT infrastructure, and growing awareness of the benefits of graph databases. Europe is also expected to witness steady growth, supported by stringent data management regulations and a strong focus on data privacy and security.

Component Analysis

The graph database market can be segmented into two primary components: software and services. The software segment holds the largest market share, driven by extensive adoption across various industries. Graph database software is designed to create, manage, and query graph databases, offering features such as scalability, high performance, and efficient handling of complex data relationships. The growth in this segment is propelled by continuous advancements and innovations in graph database technologies. Companies are increasingly investing in research and development to enhance the capabilities of their graph database software products, catering to the evolving needs of their customers.

On the other hand, the services segment is also witnessing substantial growth. This segment includes consulting, implementation, and support services provided by vendors to help organizations effectively deploy and manage graph databases. As businesses recognize the benefits of graph databases, the demand for expert services to ensure successful implementation and integration into existing systems is rising. Additionally, ongoing support and maintenance services are crucial for the smooth operation of graph databases, driving further growth in this segment.

The increasing complexity of data and the need for specialized expertise to manage and analyze it effectively are key factors contributing to the growth of the services segment. Organizations often lack the in-house skills required to harness the full potential of graph databases, prompting them to seek external assistance. This trend is particularly evident in large enterprises, where the scale and complexity of data necessitate robust support services.

Moreover, the services segment is benefiting from the growing trend of outsourcing IT functions. Many organizations are opting to outsource their database management needs to specialized service providers, allowing them to focus on their core business activities. This shift towards outsourcing is further bolstering the demand for graph database services, driving market growth.

Context

This notebook serves to showcase my problem solving ability, knowledge of the data analysis process, proficiency with Excel and its various tools and functions, as well as my strategic mindset and statistical prowess. This project consist of an auditing prompt provided by Hive Data, a raw Excel data set, a cleaned and audited version of the raw Excel data set, and my description of my thought process and knowledge used during completion of the project. The prompt can be found below:

Hive Data Audit Prompt

The raw data that accompanies the prompt can be found below:

Hive Annotation Job Results - Raw Data

^ These are the tools I was given to complete my task. The rest of the work is entirely my own.

To summarize broadly, my task was to audit the dataset and summarize my process and results. Specifically, I was to create a method for identifying which "jobs" - explained in the prompt above - needed to be rerun based on a set of "background facts," or criteria. The description of my extensive thought process and results can be found below in the Content section.

Content

Brendan Kelley April 23, 2021

Hive Data Audit Prompt Results

This paper explains the auditing process of the “Hive Annotation Job Results” data. It includes the preparation, analysis, visualization, and summary of the data. It is accompanied by the results of the audit in the excel file “Hive Annotation Job Results – Audited”.

Observation

The “Hive Annotation Job Results” data comes in the form of a single excel sheet. It contains 7 columns and 5,001 rows, including column headers. The data includes “file”, “object id”, and the pseudonym for five questions that each client was instructed to answer about their respective table: “tabular”, “semantic”, “definition list”, “header row”, and “header column”. The “file” column includes non-unique (that is, there are multiple instances of the same value in the column) numbers separated by a dash. The “object id” column includes non-unique numbers ranging from 5 to 487539. The columns containing the answers to the five questions include Boolean values - TRUE or FALSE – which depend upon the yes/no worker judgement.

Use of the COUNTIF() function reveals that there are no values other than TRUE or FALSE in any of the five question columns. The VLOOKUP() function reveals that the data does not include any missing values in any of the cells.

Assumptions

Based on the clean state of the data and the guidelines of the Hive Data Audit Prompt, the assumption is that duplicate values in the “file” column are acceptable and should not be removed. Similarly, duplicated values in the “object id” column are acceptable and should not be removed. The data is therefore clean and is ready for analysis/auditing.

Preparation

The purpose of the audit is to analyze the accuracy of the yes/no worker judgement of each question according to the guidelines of the background facts. The background facts are as follows:

• A table that is a definition list should automatically be tabular and also semantic • Semantic tables should automatically be tabular • If a table is NOT tabular, then it is definitely not semantic nor a definition list • A tabular table that has a header row OR header column should definitely be semantic

These background facts serve as instructions for how the answers to the five questions should interact with one another. These facts can be re-written to establish criteria for each question:

For tabular column: - If the table is a definition list, it is also tabular - If the table is semantic, it is also tabular

For semantic column: - If the table is a definition list, it is also semantic - If the table is not tabular, it is not semantic - If the table is tabular and has either a header row or a header column...

This Excel workbook includes tools to analyze Massachusetts public high school dropouts in grades 9–12 in order to determine the size of particular groups of dropouts for strategic interventions and planning.

The analysis includes characteristics of students in grades 9–12 who dropped out of school in the 2021-22 school year. For more information, please visit the Massachusetts Analysis of Dropout Data homepage.

Risk of bias assessment for assessing the heterogeneity involved within included studies in the article Efficacy and safety of various drug combinations in plaque psoriasis: Meta analysis