✅ 5 of the best Kaggle datasets
💸 For data science projects (in finance)
👨🏻💻 If you are looking for datasets to do financial projects, the datasets presented on the Kaggle site can be a great option.
⏪ These datasets are usually clean and ready to use and are very suitable for machine learning models. Some of these datasets are even updated daily and you can use them for deeper analysis.👇
1️⃣ S&P 500 stock dataset (daily update)
📎 Link: S&P 500 Stocks
2️⃣ Database of loans and debts
📎 Link: Loans & Liability
3️⃣ Dataset of frequent use of credit card
📎 Link: Credit Card Spending Habits
4️⃣ Company bankruptcy prediction dataset
📎 Link: Company Bankruptcy Prediction
5️⃣ Credit score classification dataset
📎 Link: Credit score classification
Hope this helps you
💸 For data science projects (in finance)
👨🏻💻 If you are looking for datasets to do financial projects, the datasets presented on the Kaggle site can be a great option.
⏪ These datasets are usually clean and ready to use and are very suitable for machine learning models. Some of these datasets are even updated daily and you can use them for deeper analysis.👇
1️⃣ S&P 500 stock dataset (daily update)
📎 Link: S&P 500 Stocks
2️⃣ Database of loans and debts
📎 Link: Loans & Liability
3️⃣ Dataset of frequent use of credit card
📎 Link: Credit Card Spending Habits
4️⃣ Company bankruptcy prediction dataset
📎 Link: Company Bankruptcy Prediction
5️⃣ Credit score classification dataset
📎 Link: Credit score classification
Hope this helps you
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Top 10 Python libraries commonly used by data scientists
1. NumPy: A fundamental package for scientific computing with support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions.
2. pandas: A powerful data manipulation and analysis library that provides data structures and functions for working with structured data.
3. matplotlib: A widely-used plotting library for creating a variety of visualizations, including line plots, bar charts, histograms, scatter plots, and more.
4. scikit-learn: A comprehensive machine learning library that provides tools for data mining and data analysis, including algorithms for classification, regression, clustering, and more.
5. TensorFlow: An open-source machine learning framework developed by Google for building and training machine learning models, particularly for deep learning tasks.
6. Keras: A high-level neural networks API that is built on top of TensorFlow and provides an easy-to-use interface for building and training deep learning models.
7. Seaborn: A data visualization library based on matplotlib that provides a high-level interface for creating informative and attractive statistical graphics.
8. SciPy: A library that builds on NumPy and provides a wide range of scientific and technical computing functions, including optimization, integration, interpolation, and more.
9. Statsmodels: A library that provides classes and functions for the estimation of many different statistical models, as well as conducting statistical tests and exploring data.
10. XGBoost: An optimized gradient boosting library that is widely used for supervised learning tasks, such as regression and classification.
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1. NumPy: A fundamental package for scientific computing with support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions.
2. pandas: A powerful data manipulation and analysis library that provides data structures and functions for working with structured data.
3. matplotlib: A widely-used plotting library for creating a variety of visualizations, including line plots, bar charts, histograms, scatter plots, and more.
4. scikit-learn: A comprehensive machine learning library that provides tools for data mining and data analysis, including algorithms for classification, regression, clustering, and more.
5. TensorFlow: An open-source machine learning framework developed by Google for building and training machine learning models, particularly for deep learning tasks.
6. Keras: A high-level neural networks API that is built on top of TensorFlow and provides an easy-to-use interface for building and training deep learning models.
7. Seaborn: A data visualization library based on matplotlib that provides a high-level interface for creating informative and attractive statistical graphics.
8. SciPy: A library that builds on NumPy and provides a wide range of scientific and technical computing functions, including optimization, integration, interpolation, and more.
9. Statsmodels: A library that provides classes and functions for the estimation of many different statistical models, as well as conducting statistical tests and exploring data.
10. XGBoost: An optimized gradient boosting library that is widely used for supervised learning tasks, such as regression and classification.
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5 Essential Portfolio Projects for data analysts 😄👇
1. Exploratory Data Analysis (EDA) on a Real Dataset: Choose a dataset related to your interests, perform thorough EDA, visualize trends, and draw insights. This showcases your ability to understand data and derive meaningful conclusions.
Free websites to find datasets: https://news.1rj.ru/str/DataPortfolio/8
2. Predictive Modeling Project: Build a predictive model, such as a linear regression or classification model. Use a dataset to train and test your model, and evaluate its performance. Highlight your skills in machine learning and statistical analysis.
3. Data Cleaning and Transformation: Take a messy dataset and demonstrate your skills in cleaning and transforming data. Showcase your ability to handle missing values, outliers, and prepare data for analysis.
4. Dashboard Creation: Utilize tools like Tableau or Power BI to create an interactive dashboard. This project demonstrates your ability to present data insights in a visually appealing and user-friendly manner.
5. Time Series Analysis: Work with time-series data to forecast future trends. This could involve stock prices, weather data, or any other time-dependent dataset. Showcase your understanding of time-series concepts and forecasting techniques.
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1. Exploratory Data Analysis (EDA) on a Real Dataset: Choose a dataset related to your interests, perform thorough EDA, visualize trends, and draw insights. This showcases your ability to understand data and derive meaningful conclusions.
Free websites to find datasets: https://news.1rj.ru/str/DataPortfolio/8
2. Predictive Modeling Project: Build a predictive model, such as a linear regression or classification model. Use a dataset to train and test your model, and evaluate its performance. Highlight your skills in machine learning and statistical analysis.
3. Data Cleaning and Transformation: Take a messy dataset and demonstrate your skills in cleaning and transforming data. Showcase your ability to handle missing values, outliers, and prepare data for analysis.
4. Dashboard Creation: Utilize tools like Tableau or Power BI to create an interactive dashboard. This project demonstrates your ability to present data insights in a visually appealing and user-friendly manner.
5. Time Series Analysis: Work with time-series data to forecast future trends. This could involve stock prices, weather data, or any other time-dependent dataset. Showcase your understanding of time-series concepts and forecasting techniques.
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Having a strong portfolio is one of the best ways to stand out when applying for a Data Analyst role. But it’s important to choose the right projects that show your skills and creativity. Here’s how you can create meaningful projects:-
Don’t work on the same old ideas like simple sales dashboards or stock price analysis. These projects are very common and don’t make you stand out. Instead, try to pick unique and interesting topics that recruiters haven’t seen before.
Think about real problems faced by companies. For example, mobility companies like Uber, Ola, or Rapido face issues where some drivers ask customers to cancel rides so they can complete trips offline. This leads to revenue loss for the company. You can take this as example to create a project to analyze this problem, quantify the losses, and suggest solutions.
Use multiple tools in a single project to show your versatility. For example, you can use SQL to clean and organize data, Python to analyze it, and Power BI to create dashboards. This shows you can handle an entire process from start to finish.
Focus on projects that solve real business problems like reducing customer churn, optimizing marketing budgets, or segmenting customers into different groups. These projects show that you understand how businesses operate and how data can make an impact.
Explain how you thought through the problem when you present your project. For example, if you analyzed driver cancellations, explain how you broke the problem into smaller parts, analyzed the data, and came up with solutions. This helps others see your problem-solving approach.
Combine multiple related problems into one project to make it more impactful. For example, you could analyze driver cancellations, identify peak times for offline completions, and create a dashboard to monitor revenue loss. Combining ideas makes your project more comprehensive and impressive.
Try to find data sets that aren’t commonly used. Instead of downloading the same datasets everyone uses, explore platforms like Kaggle or open data portals, or even create your own data. This will make your projects look fresh and unique.
Always share clear and actionable results in your projects. For example, if you worked on driver cancellations, suggest ways to reduce them, like adjusting incentives or monitoring systems. Finish your project with a clear and engaging dashboard to show your findings.
By working on unique and meaningful projects, you can show your skills, creativity, and ability to solve real problems.
#dataportfolio
Don’t work on the same old ideas like simple sales dashboards or stock price analysis. These projects are very common and don’t make you stand out. Instead, try to pick unique and interesting topics that recruiters haven’t seen before.
Think about real problems faced by companies. For example, mobility companies like Uber, Ola, or Rapido face issues where some drivers ask customers to cancel rides so they can complete trips offline. This leads to revenue loss for the company. You can take this as example to create a project to analyze this problem, quantify the losses, and suggest solutions.
Use multiple tools in a single project to show your versatility. For example, you can use SQL to clean and organize data, Python to analyze it, and Power BI to create dashboards. This shows you can handle an entire process from start to finish.
Focus on projects that solve real business problems like reducing customer churn, optimizing marketing budgets, or segmenting customers into different groups. These projects show that you understand how businesses operate and how data can make an impact.
Explain how you thought through the problem when you present your project. For example, if you analyzed driver cancellations, explain how you broke the problem into smaller parts, analyzed the data, and came up with solutions. This helps others see your problem-solving approach.
Combine multiple related problems into one project to make it more impactful. For example, you could analyze driver cancellations, identify peak times for offline completions, and create a dashboard to monitor revenue loss. Combining ideas makes your project more comprehensive and impressive.
Try to find data sets that aren’t commonly used. Instead of downloading the same datasets everyone uses, explore platforms like Kaggle or open data portals, or even create your own data. This will make your projects look fresh and unique.
Always share clear and actionable results in your projects. For example, if you worked on driver cancellations, suggest ways to reduce them, like adjusting incentives or monitoring systems. Finish your project with a clear and engaging dashboard to show your findings.
By working on unique and meaningful projects, you can show your skills, creativity, and ability to solve real problems.
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🚀Here are 5 fresh Project ideas for Data Analysts 👇
🎯 𝗔𝗶𝗿𝗯𝗻𝗯 𝗢𝗽𝗲𝗻 𝗗𝗮𝘁𝗮 🏠
https://www.kaggle.com/datasets/arianazmoudeh/airbnbopendata
💡This dataset describes the listing activity of homestays in New York City
🎯 𝗧𝗼𝗽 𝗦𝗽𝗼𝘁𝗶𝗳𝘆 𝘀𝗼𝗻𝗴𝘀 𝗳𝗿𝗼𝗺 𝟮𝟬𝟭𝟬-𝟮𝟬𝟭𝟵 🎵
https://www.kaggle.com/datasets/leonardopena/top-spotify-songs-from-20102019-by-year
🎯𝗪𝗮𝗹𝗺𝗮𝗿𝘁 𝗦𝘁𝗼𝗿𝗲 𝗦𝗮𝗹𝗲𝘀 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 📈
https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting/data
💡Use historical markdown data to predict store sales
🎯 𝗡𝗲𝘁𝗳𝗹𝗶𝘅 𝗠𝗼𝘃𝗶𝗲𝘀 𝗮𝗻𝗱 𝗧𝗩 𝗦𝗵𝗼𝘄𝘀 📺
https://www.kaggle.com/datasets/shivamb/netflix-shows
💡Listings of movies and tv shows on Netflix - Regularly Updated
🎯𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗷𝗼𝗯𝘀 𝗹𝗶𝘀𝘁𝗶𝗻𝗴𝘀 💼
https://www.kaggle.com/datasets/cedricaubin/linkedin-data-analyst-jobs-listings
💡More than 8400 rows of data analyst jobs from USA, Canada and Africa.
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🎯 𝗔𝗶𝗿𝗯𝗻𝗯 𝗢𝗽𝗲𝗻 𝗗𝗮𝘁𝗮 🏠
https://www.kaggle.com/datasets/arianazmoudeh/airbnbopendata
💡This dataset describes the listing activity of homestays in New York City
🎯 𝗧𝗼𝗽 𝗦𝗽𝗼𝘁𝗶𝗳𝘆 𝘀𝗼𝗻𝗴𝘀 𝗳𝗿𝗼𝗺 𝟮𝟬𝟭𝟬-𝟮𝟬𝟭𝟵 🎵
https://www.kaggle.com/datasets/leonardopena/top-spotify-songs-from-20102019-by-year
🎯𝗪𝗮𝗹𝗺𝗮𝗿𝘁 𝗦𝘁𝗼𝗿𝗲 𝗦𝗮𝗹𝗲𝘀 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 📈
https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting/data
💡Use historical markdown data to predict store sales
🎯 𝗡𝗲𝘁𝗳𝗹𝗶𝘅 𝗠𝗼𝘃𝗶𝗲𝘀 𝗮𝗻𝗱 𝗧𝗩 𝗦𝗵𝗼𝘄𝘀 📺
https://www.kaggle.com/datasets/shivamb/netflix-shows
💡Listings of movies and tv shows on Netflix - Regularly Updated
🎯𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗷𝗼𝗯𝘀 𝗹𝗶𝘀𝘁𝗶𝗻𝗴𝘀 💼
https://www.kaggle.com/datasets/cedricaubin/linkedin-data-analyst-jobs-listings
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Data Cleaning Techniques in Python ✅
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