Dataset

This dataset was created by Aya Abulnasr

Contents

Dataset

This dataset was created by Momoh Charles osibughe

Contents

Data exploration, cleaning, and arrangement with Covid Death and Covid Vaccination which is involved:

1. Data that going to be using

2. Shows the likelihood of dying if you contract covid in your country

3. Show what percentage of the population got Covid

4. Looking at Countries with the Highest Infection Rate compared to the Population

5. Showing the Country with the Highest Death Count per Population

6. Break things down by continent

7. Continents with the Highest death count per population

8. Looking at Total Population vs Vaccinations

9. Used CTE and Temp Table

10. Creating View to store data for later visualizations

Dataset

This dataset was created by Ashish Roy

Contents

Exploring E-commerce Trends: A Guide to Leveraging Dummy Dataset

Introduction: In the world of e-commerce, data is a powerful asset that can be leveraged to understand customer behavior, improve sales strategies, and enhance overall business performance. This guide explores how to effectively utilize a dummy dataset generated to simulate various aspects of an e-commerce platform. By analyzing this dataset, businesses can gain valuable insights into product trends, customer preferences, and market dynamics.

1. Dataset Overview: The dummy dataset contains information on 1000 products across different categories such as electronics, clothing, home & kitchen, books, toys & games, and more. Each product is associated with attributes such as price, rating, number of reviews, stock quantity, discounts, sales, and date added to inventory. This comprehensive dataset provides a rich source of information for analysis and exploration.

2. Data Analysis: Using tools like Pandas, NumPy, and visualization libraries like Matplotlib or Seaborn, businesses can perform in-depth analysis of the dataset. Key insights such as top-selling products, popular product categories, pricing trends, and seasonal variations can be extracted through exploratory data analysis (EDA). Visualization techniques can be employed to create intuitive graphs and charts for better understanding and communication of findings.

3. Machine Learning Applications: The dataset can be used to train machine learning models for various e-commerce tasks such as product recommendation, sales prediction, customer segmentation, and sentiment analysis. By applying algorithms like linear regression, decision trees, or neural networks, businesses can develop predictive models to optimize inventory management, personalize customer experiences, and drive sales growth.

4. Testing and Prototyping: Businesses can utilize the dummy dataset to test new algorithms, prototype new features, or conduct A/B testing experiments without impacting real user data. This enables rapid iteration and experimentation to validate hypotheses and refine strategies before implementation in a live environment.

5. Educational Resources: The dummy dataset serves as an invaluable educational resource for students, researchers, and professionals interested in learning about e-commerce data analysis and machine learning. Tutorials, workshops, and online courses can be developed using the dataset to teach concepts such as data manipulation, statistical analysis, and model training in the context of e-commerce.

6. Decision Support and Strategy Development: Insights derived from the dataset can inform strategic decision-making processes and guide business strategy development. By understanding customer preferences, market trends, and competitor behavior, businesses can make informed decisions regarding product assortment, pricing strategies, marketing campaigns, and resource allocation.

Conclusion: In conclusion, the dummy dataset provides a versatile and valuable resource for exploring e-commerce trends, understanding customer behavior, and driving business growth. By leveraging this dataset effectively, businesses can unlock actionable insights, optimize operations, and stay ahead in today's competitive e-commerce landscape

Introduction

This dataset is a synthetic e-commerce dataset designed to provide a comprehensive view of transaction, customer, product, and advertising data in a dynamic marketplace. It simulates real-world scenarios with seasonal effects, regional variations, advertising metrics, and customer purchasing behaviors. This dataset can serve as a valuable resource for exploring e-commerce analytics, customer segmentation, product performance, and marketing effectiveness.

The dataset includes detailed transaction-level data featuring product categories, customer demographics, discounts, revenue, and advertising metrics such as impressions, clicks, conversion rates, and ad spend. Seasonal trends and regional multipliers are integrated into the data to create realistic patterns that mimic consumer behavior across different times of the year and geographic regions.

Potential Analyses

1. Customer Insights

• Perform customer segmentation based on demographics, lifetime value, and purchase behavior.
• Analyze trends in customer behavior across regions or product categories.

2. Product Performance

• Identify top-performing products by revenue or units sold.
• Evaluate the impact of discounts and promotions on product sales.

3. Marketing Analytics

• Measure the effectiveness of advertising using CTR, CPC, and conversion rates.
• Assess how ad spend correlates with revenue and impressions.

4. Seasonal Trends

• Analyze seasonality effects on sales volume and revenue.
• Explore spikes in revenue or sales during holiday periods.

5. Regional Analysis

• Investigate regional performance trends using the regional multipliers.
• Examine customer preferences across different regions.

6. Data Science Applications

• Build predictive models for sales forecasting.
• Create clustering models for customer segmentation or product categorization.
• Develop optimization strategies for advertising spend or inventory management.

This dataset provides ample opportunities for data exploration, machine learning, and business analysis. We hope you find it insightful and useful for your projects!

This dataset contains detailed information about the COVID-19 pandemic. The inspiration behind this dataset is to analyze trends, identify patterns, and understand the global impact of COVID-19 through SQL queries. It is designed for anyone interested in data exploration and real-world analytics.

Dataset Description: Zameen.com Property Listings

This dataset contains real estate property listings scrapped from Zameen.com, a popular property portal. The dataset includes various attributes related to the properties listed on the website. The data is collected over time and provides valuable insights into the real estate market in different locations, cities, and provinces.

Potential Uses:

Real estate market analysis: The dataset can be used to analyze property prices, trends, and demand in different locations and cities. Property classification: Properties can be categorized based on their type, purpose, and price range. Location-based insights: Identify popular localities and areas with high demand for real estate. Predictive modeling: Predict property prices or demand using machine learning models based on various attributes.

Context

Understanding the indicators and predictors of national and regional development through exploration of available data.

Content

This data set contains a comprehensive data collected from various indicators, the data, dating back to 1960, has been collected by the World Bank from various renown sources and includes area of Agriculture & Rural Development, Aid Effectiveness, Climate Change, Economy & Growth, Education, Energy & Mining, Environment, External Debt, Financial Sector, Gender, Health, Infrastructure, Labor & Social Protection, Poverty, Private Sector, Public Sector, Science & Technology, Social Development, Trade, Urban Development

Acknowledgements

The data files have been collected directly from World Bank.

This dataset contains job postings related to Data Science roles in 2025, collected from publicly available sources. It includes essential details such as job titles, seniority levels, company information, locations, salaries, industries, company size, and required skills. The dataset has been cleaned and structured to ensure accuracy and consistency, with duplicates and irrelevant entries removed.

It is designed to help researchers, students, and professionals analyze hiring trends, salary ranges, and in-demand skills in the Data Science job market. This dataset can also support projects in machine learning, career prediction, salary forecasting, and workforce analytics.

**Student Mental Health Survey: Scaled Data on IT Students' Academic and Emotional Well-being ** **Overview **This dataset contains survey responses from IT students, focusing on academic stress, mental health, and lifestyle factors. It includes two files that capture different stages of data preparation to suit various analytical needs.

Files Included MentalHealthSurvey.csv:

Description: Contains the original survey data with raw categorical and numerical variables. Usefulness: Ideal for initial data exploration and understanding the unprocessed patterns before any data transformation. MentalHealthSurvey_Cleaned.csv:

Description: This file contains cleaned and preprocessed data with scaled numerical variables. The data was scaled using standard scaling techniques, which adjust the values so that each variable has a mean of 0 and a standard deviation of 1. Why Scaling is Useful: Scaling ensures that all numerical variables contribute equally to statistical models, particularly in factor analysis, where varying scales can skew the results. Scaled data improves model performance, stability, and interpretability, making it especially valuable for advanced analyses like predictive modeling and machine learning. Applications Initial Data Exploration: Use the raw data to explore variable distributions, correlations, and identify potential data quality issues. Advanced Analysis: The cleaned and scaled data is optimal for statistical analysis, helping to uncover meaningful patterns and insights into the factors affecting students' mental health and academic performance. Both files offer a complete view of the dataset, from raw data exploration to scaled data ready for rigorous analysis.

📊 Explore Data with Seaborn Visualization

Welcome to the gateway of data exploration and visualization extravaganza! 🚀 This dataset serves as your golden ticket to dive into the mesmerizing world of data visualization using Seaborn. 🎨✨

Dataset Overview:

This treasure trove of data accompanies a captivating Medium blog post, serving as the canvas upon which you'll paint your visual masterpieces. 🖌️📈 Delve into the depths of Seaborn's capabilities as you embark on an exhilarating journey through various types of charts and graphs. From elegant line plots to stunning heatmaps, this dataset has it all!

Dataset Highlights:

🔍 Curated with care: Each variable meticulously selected to fuel your exploration. 🌟 Rich assortment: An eclectic mix of data points to spark your creativity. 🎯 Practice paradise: The perfect playground for honing your visualization skills. 🔮 Uncover hidden insights: Peel back the layers and reveal the stories hidden within the data.

How to Use:

Embrace your inner data artist! 🎨 Let your imagination run wild as you experiment with Seaborn's powerful visualization tools. Whether you're a seasoned pro or a curious beginner, this dataset offers endless opportunities for discovery and learning.

Ready to Begin?

Grab your virtual palette and brush, and embark on a visual odyssey through the enchanting realm of data analysis and visualization. 🌟 Let the data speak to you, and together, let's paint a picture worth a thousand insights!

Want to Learn More?

Check out the accompanying Medium blog post for a detailed guide on how to utilize this dataset: Data Analysis by Visualization using Seaborn

The data was used with many others for comparing various classifiers. In a classification context, this is a well posed problem with "well behaved" class structures. A good data set for first testing of a new classifier, but not very challenging.

These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wines.

The attributes are:

• Alcohol
• Malic acid
• Ash
• Alcalinity of ash
• Magnesium
• Total phenols
• Flavanoids
• Nonflavanoid phenols
• Proanthocyanins
• Color intensity
• Hue
• OD280/OD315 of diluted wines
• Proline

For Each Attribute: All attributes are continuous

No statistics available, but suggest to standardise variables for certain uses (e.g. for us with classifiers which are NOT scale invariant)

NOTE: 1st attribute is class identifier (target)(1-3)

Acknowledgements: This dataset is also available from Kaggle & UCI machine learning repository, https://archive.ics.uci.edu/dataset/109/wine

This dataset consists of unlabeled data representing various data points collected from different sources and domains. The dataset serves as a blank canvas for unsupervised learning experiments, allowing for the exploration of patterns, clusters, and hidden insights through various data analysis techniques. Researchers and data enthusiasts can use this dataset to develop and test unsupervised learning algorithms, identify underlying structures, and gain a deeper understanding of data without predefined labels.

The AdventureWorks DW 2008 dataset, originally provided by Microsoft, has been converted into CSV files for easier use, making it accessible for data exploration on platforms like Kaggle. The dataset is licensed under the Microsoft Public License (MS-PL), which is a permissive open-source license. This means you are free to use, modify, and share the dataset, whether for personal or commercial purposes, provided that you include the original license terms. However, it's important to note that the dataset is provided "as-is" without any warranty or guarantee from Microsoft.

I really enjoy working with the AdventureWorks DW 2008 dataset. It offers a rich and well-structured environment that's perfect for writing and learning SQL queries. The data warehouse includes a variety of tables, such as facts and dimensions, making it an excellent resource for both beginners and experienced SQL users to practice querying and exploring relational databases.

Now, with the dataset available in CSV format, it can be easily used with Python for exploratory data analysis (EDA), and it’s also well-suited for applying machine learning techniques such as regression, classification, and clustering.

If you’re planning to dive into the data, all the best! It's a fantastic resource to learn from and experiment with. Cheers!

Dataset

This dataset was created by Robert Currie

Released under Data files © Original Authors

Contents

This is a Tabular-Text Dataset. It entails list of programs available at an autonomous college with details of subjects and the information about which of the developmental needs are fulfilled on completion of the syllabus respective subject. Developmental Needs are segregated as Local, Regional , National and Global.

Image Credits:

Image by Mohamed Hassan from Pixabay

Dataset

This dataset was created by voona sanjana

Released under Data files © Original Authors

Contents

Reddit: /r/travel

An Exploration of Users & Posts

By Reddit [source]

About this dataset

Traveling can be an incredibly exciting and rewarding experience; it is the perfect way to break away from the everyday routine and explore new cultures, sights, and sounds. For those planning a travel-related adventure – whether international or local – having access to real-user experiences in the form of advice and recommendations can mean the difference between a fantastic journey and a costly mistake. That's why this dataset of Reddit posts history on 'travel' is particularly useful for exploring Reddit users' opinions, desires, and experiences with their travel endeavors.

This dataset contains information on over 750+ Reddit posts regarding traveling as well as thousands of related comments over an extended period of time. For every post listed, data such as title, score (number of upvotes), URL link to page, number of comments given per post/comment thread, creation date/time stamp for both post/comment threads can be found.

All together these attributes provide detailed insights into user sentiments towards various aspects regarding traveling: What topics are they most interested in? What do they think are the best (or worst) destinations? Are there any tips or pitfalls that could inform our own decisions when embarking on our next journey? All this information resulting from our analysis will give us better guidance when helping us make smarter decisions during our planning process!

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For more datasets, click here.

Featured Notebooks

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How to use the dataset

This dataset provides valuable insights into the various opinions, desires and experiences of Redditors about travel-related activities. The data consists of posts and comments collected from the 'travel' sub reddit page on Reddit. To get started with this dataset, you need to first understand that each post includes data such as title, score, ID, url, number of comments created at the timestamp etc. This can be used to understand the kind of conversations that are happening in these forums regarding travel related topics.

Research Ideas

• Analyzing user sentiment around various topics in the travel industry such as airlines, hotels, attractions and experiences.
• Comparing time of year to the frequency of posts related to summer vacation or other holiday specific activities.
• Examining which geographical locations generate the most interest among Redditors, and applying this data to marketing campaigns for those areas

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: travel.csv | Column name | Description | |:--------------|:--------------------------------------------------------| | title | The title of the post. (String) | | score | The number of upvotes the post has received. (Integer) | | url | The URL of the post. (String) | | comms_num | The number of comments the post has received. (Integer) | | created | The date and time the post was created. (DateTime) | | body | The body of the post. (String) | | timestamp | The date and time the post was last updated. (DateTime) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Reddit.

This dataset provides a comprehensive, time-series record of the global COVID-19 pandemic, including daily counts of confirmed cases, deaths, and recoveries across multiple countries and regions. It is designed to support data scientists, researchers, and public health professionals in conducting exploratory data analysis, forecasting, and impact assessment studies related to the spread and consequences of the virus.