The data story ends in a proposal in Jordan. And you need a bunch of kitchen equipment: bowls, mixers, knife, baking sheet, oven, and some towels. Hi hvc, You are right. The simplest way to use them is to create data stories and publishing them over web. Met by too many options, we become paralyzed, overwhelmed, and unable to make a decision. The citation is: Removed the mentioning of the number of cities. Huge Datasets — things are getting serious now. While some of the initial datasets were usually dating website dataset at other places, I have met a few interesting datasets on the platform, not present at other places. Limitations of the dataset are discussed. You should mention possible legal problems arising from releasing users photo. Best, Sara It seems that the repository will be permanently offline at OSF due to the DMCA note. dating website dataset
Introduction If there is one sentence, which summarizes the essence of learning data science, it is this: The best way to learn data science is to apply data science. If you are a beginner, you improve tremendously with each new project you undertake. If you are an experienced data science professional, you already know what I am talking about. However, when I give this advice to people, they usually ask something in return — Where can I get datasets for practice? They fail to realize the amount of learning they can get out from working on these projects to get a boost in their career. How can you use these sources? There is no end to how you can use these data sources. The application and usage is only limited by your creativity and application. The simplest way to use them is to create data stories and publishing them over web. This would not only improve your data and visualization skills, but also improve your structured thinking. So, go ahead, work on these projects and share them with the larger world to showcase your data prowess! I have divided these sources in various sections to help you categorize data sources based on application. We then provide links to dataset for specific purpose — Text Mining, Image classification, Recommendation engine etc. This should provide you a holistic list of data resources. If you can think of any application of these datasets or know of any popular resources which I have missed, please feel free to share them with me in the comments below. The site contains more than 190,000 data points at time of publishing. These datasets vary from data about climate, education, energy, Finance and many more areas. Find data by various industries, climate, health care etc. You can check out a few visualizations. Depending on your country of residence, you can also follow similar websites from a few other websites — check them out. The platform provides several tools like Open Data Catalog, world development indices, education indices etc. This includes several metrics on money market operations, balance of payments, use of banking and several products. A must go to site, if you come from BFSI domain in India. Each dataset includes the data, a dictionary explaining the data and the link to the story carried out by Five Thirty Eight. Huge Datasets — things are getting serious now! You can also analyze the data in the cloud using and Hadoop via. Popular datasets on Amazon include full Enron email dataset, Google Books n-grams, NASA NEX datasets, Million Songs dataset and many more. More information can be found. It comes with pre-computed, state-of-the-art vision features from billions of frames. It is usually the first place to go, if you are looking for datasets related to machine learning repositories. The datasets include a diverse range of datasets from popular datasets like Iris and Titanic survival to recent contributions like that of Air Quality and GPS trajectories. You can use these filters to identify good datasets for your need. They have more than 350 datasets in total — with more than 200 as Featured datasets. While some of the initial datasets were usually present at other places, I have seen a few interesting datasets on the platform, not present at other places. Along with new datasets, another benefit of the interface is that you can see scripts and questions from community members on the same interface. The problem datasets are based on real-life industry problems and are relatively smaller as they are meant for 2 — 7 days hackathons. While practice problems are available to people always, the hackathon problems become unavailable after the hackathons. So, you need to participate on the hackathon to get access to the datasets. Their datasets are classified as Open or Premium. You can access all the open datasets for Free, but you need to pay for the premium datasets. If you search, you still get good datasets on the platform. Archives includes datasets and instructions. Winners are available for most years. They then run online modeling competitions for data scientists to develop the best models to solve them. If you are interested in use of data science for social good — this is the place to be. It includes 60,000 train examples and a test set of 10,000 examples. This serves as typically the first dataset to practice image recognition. This dataset includes character recognition in natural images. The dataset contains 74,000 images and hence the name of the dataset. Image database organised according to the WordNet hierarchy currently only the nouns. Each node of the hierarchy is depicted by hundreds of images. Currently, the collection has an average of over five hundred images per node and increasing. You need to build a classifier classifying the SMS as span or non-spam. The data is in turn based on a Kaggle competition and analysis by Nick Sanders. It has hundreds of thousands of registered users. They conduct online field experiments in MovieLens in the areas of automated content recommendation, recommendation interfaces, tagging-based recommenders. These datasets are available for download and can be used to create your own recommender systems. A really comprehensive list, however some of the sources no longer provide the datasets. So, you will need to apply your own prudence on the datasets and the sources. Datasets are classified neatly in various domains, which is very helpful. However, there is no description about the datasets on the repository itself — which could have made it very useful. Also, it has some interesting datasets and discussions. End Notes I hope that this list of resources would prove extremely useful for people looking out for doing pet projects or side projects. For the starters, this is definitely a gold mine. Make sure you pick a few side projects and continue to work on them. If you can think of any application of these datasets or know of any popular resources which I have missed, please feel free to share them with me in the comments below. Looking forward to hearing from you. You can also read this article on Analytics Vidhya's Android APP Kunal is a post graduate from IIT Bombay in Aerospace Engineering. He has spent more than 10 years in field of Data Science. His work experience ranges from mature markets like UK to a developing market like India. During this period he has lead teams of various sizes and has worked on various tools like SAS, SPSS, Qlikview, R, Python and Matlab. We only publish awesome content. We will never share your information with anyone. We only publish awesome content. 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