Here is part two of our blog about data science opportunities. As you know, here at Capsidea, we offer easy to use data analytics and a robust analytics platform. For those of you who are self-employed developers or simply those passionate about data science, we’re sharing a few more ways to get more involved in analyzing data.
Compete in Stand-Alone Data Competitions and Contests: Kaggle
Making a name for yourself in any business is an important step that you should always have in the back of your mind in regards to every industry. Data science is no different. By entering into competitions, you’ll learn how you stack up in the industry, meet fellow data analytics peers, and start to get people to know your name.
One great way to start on this path is by joining Kaggle. Kaggle is a site that offers a community and competitions for data scientists all over the world. Complete with forums, job opportunities, and open competitions that can span from 3 months to a few weeks, Kaggle is an amazing resource for data enthusiasts to make a career out of their projects.
Often, companies will post offers along with their competitions, and offer money and sometimes jobs along with the prize. For example, Kaggle has a popular competition going on right now:
The Acquire Valued Shoppers Challenge asks participants to predict which shoppers are most likely to repeat purchase. To aid with algorithmic development, we have provided complete, basket-level, pre-offer shopping history for a large set of shoppers who were targeted for an acquisition campaign. The incentive offered to that shopper and their post-incentive behavior is also provided.
This challenge provides almost 350 million rows of completely anonymised transactional data from over 300,000 shoppers. It is one of the largest problems run on Kaggle to date.
For this challenge, there is a $30,000 prize and it is spread over 55 days. If you are an active data scientist, it would benefit you to get involved in such comptetions and get your name out there. You never know, you could win or at least meet other peers who share you passion who you could work with in the future.
Start Your Own Project
If you want to start analyzing your own data and just get it out there, there are many avenues and paths to take. It’s easy with programs online that can visualize your input data for you. IKANOW has a great site about getting started with your own data, site, and visuals.
Analysis projects usually start off with an untested ideas, so it’s always a good idea to try to keep costs down by looking for free data sets to combine with your proprietary data…If you’re looking for topic specific data sets, non-governmental organizations and universities are some of the best places to find free data. Getting a topic specific data set usually doesn’t require much more than time, and these organizations have personnel resources and reasons to cultivate the data. It’s usually also made available to the public to advance a cause or promote research, so this is often the first place to look for new data sets.
Once you have this information, decide the message you’d like to convey. Have a strong idea of the final missive you’re discussing and make sure the data works to support it. Parse it so that it’s easy to understand and cut any additional data that is not necessary. Work with a data visualization site or platform, such as Capsidea, to make sure the information you’re sharing is easily digestible and can appeal to as wide of an audience as possible.
Summary
With these easy steps, you can start to expand your data science passion or career. There are many resources online for starting in on a wide variety of competitions and contests, which can introduce your name into the field and perhaps net you revenue as well. Starting your own project as well gives you freedom of your project, while also giving you the opportunity to analyze a wide range of data from any source you prefer. Universities and non-government sources have large, shared, public databases to pull from, which can help you in your search. Try to cut down and make your information accessible and informative, and use other resources like Capsidea to make your data science projects easy to share and interesting.
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