Whether you are looking to build machine learning models, or use natural language processing for sentiment analysis or predict market trends, you would need data to test the algorithms and theories. This is why DataStock helps you download clean and ready-to-use data-sets instantly, once you sign up. The data-sets are ideal for analyzing large data together, or for deriving insights that are invisible to the human eye or for training machine learning algorithms whose performance needs to be tested and verified. Multiple domains like retail, healthcare, and jobs are covered in the data-sets present on the website and hence it is a treasure for anyone in the industry looking to delve in data. Here are ten things that you can do with some of our data-sets

Market Research on fashion-products using data from websites like victoriassecret.com, gap.com and more- 

In case you want to conduct market research on online stores, their inventory as well as prices, DataStock has data-sets covering multiple websites such as Gap, Victoria’s Secret, Forever21, J.Crew, and more. The fashion industry usually runs on local and global trends and by analyzing a massive amount of data spread gathered from multiple online fashion websites, we might be able to get a better picture of market trends as well as price brackets of different subcategories and bestsellers.

Analysing job data using Data-sets from websites like Naukri, Indeed and Monster-

DataStock contains job data scraped from several websites like Naukri, Indeed, Monster, Dice, CareerBuilder, and more. By analyzing this data, one can create a sector-wise study of the job market to find out which positions are most in-demand in each sector. Other data-points can be used to compute the median pay for a specific role at a specific experience level. The data-set can be used to help candidates negotiate salaries when they are applying for jobs. Since the data-sets present in DataStock are from different places across the globe, a comparative study of job markets in different countries can also be performed.

Natural Language Processing on Controversial and Hateful Tweets-

Natural Language Processing is a subset of Machine Learning or data-science that is usually performed on unstructured textual data sets, to tag them or convert them into a structured format or to understand the feelings behind them. For example, a company can use an NLP based system to compute how many of its reviews are positive, negative and neutral. Similarly, a study can be done on the data-set of controversial and hateful tweets present on DataStock to find-

a) Which problems have caused the most tweets,
b) What are the most common words in hateful tweets,
c) What percentage of the tweets are targeted at specific individuals,
d) What percentage are aimed at institutions and government, and more.

Similarly, once you have a list of words gathered from all the hateful tweets, you create a screening system that will screen tweets with those words and download them for you.

Use the data on movies from the Marvel Cinematic Universe to create a Blog Post for the Fans-

We all love Marvel and its movies. Suppose you create blog posts based on popular Marvel Movies. The “Marvel Cinematic Universe Movies on IMDB” data-set is a free data-set available on DataStock that anyone can download and use to create a blog, a video, or even a statistical analysis to woo Marvel fans and increase organic hits to your website.

Use Walmart product data from USA and Canada to compile a list of items that you may want to sell at your store-

DataStock contains Walmart’s list of product data in the USA as well as Canada. If you are a shop owner in the USA or Canada, the lists can benefit you widely. Things that can be done with the data-set include-

a) Finding the items that are sold in both countries.
b) Finding items that are specific to a single country.
c) Making a list of the bestsellers in each country, and more

These findings can then be used by you to stock up on products that you know customers are more eager to buy because you have already seen the demand for the item at Walmart.

Create a price comparison chart and identify trends in hotel prices using data from Booking.com, Stayzilla, MakeMyTrip, and Goibibo-

Whenever we book a hotel, we usually sit and compare prices of hotel rooms across different websites. Also booking hotels in particular months might be cheaper than booking them in other months. To get a price comparison of hotels across different websites and to create a prediction engine that would predict the prices of hotels on a given day in a year, the hotel prices data-sets on DataStock can be used. These data-sets contain thousands of hotel listings across multiple websites and different geographical locations.

Use historical data on flight fares from Easmytrip.com to build a machine learning model and predict future prices-

Predicting prices of flights is another tough nut to crack, but the flight data from EaseMyTrip in DataStock might just help you do that. Also, you will be able to understand which routes are most “in-demand” and usually remain fully-booked. For these routes, one might have to book their tickets earlier than usual to get cheaper flights.

Use lawyer and doctor profiles on Avvo.com to build an interactive map for people to find doctors and lawyers closest to their location along with their locations-

It’s always difficult to sit with the YellowPages and search for doctors or lawyers who offer their services nearby. You can do the same on Google but it would still take you some time. Hence you could build an interactive map using the data from Avvo.com. The data-set contains details, reviews and numerous other data points of doctors and lawyers across the country. All this can be accumulated to provide a service where you can view the ones nearest to you on a map, and also view the other information related to them, which might help you decide whether or not to avail of their services.

Analyse data from “Books on Amazon” to find the best selling genres and titles and use the data for your online bookstore or use the NLP on the reviews to build your book-recommendation website-

In case you want to create an online bookstore or a blog on books, you can use the “Books on Amazon” data-set present on DataStock to your advantage. The massive data-set with its reviews and ratings can help you know which books to stock on your website. You can also use NLP on the reviews to gather data on each book and you can use this data on your blog posts or build your book-recommendation engine.

Use the “Per capita ethanol consumption for states in the US [1977-2016]” Data-Set to decide which drinks to serve or where to open your pub-

In case you own a pub or a bar, or you are thinking of opening a new one, it’s highly recommended that you take a data-driven approach for this. How? Well, you can use the above-mentioned data-set to find which states prefer wine, which prefers beer, which has the highest per capita alcohol consumption, and more. By opening your bar in a city where people drink more, you are more likely to succeed in your venture. Opening a brewery in a city where beer is the most popular beverage will help build your brand. The data can also be used for research purposes or to draw other correlations and comparisons.

Conclusion

Finding the right data-set and cleaning up the data takes a lot of time, so if you are getting a ready-to-use data-set from any source, that would reduce more than half the time for your data science project. DataStock is just one of the solutions that our team at PromptCloud offers to companies and individuals to enable them to make data-backed decisions.


1 Comment

AffiliateLabz · February 16, 2020 at 12:21 am

Great content! Super high-quality! Keep it up! 🙂

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