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3 Practical Strategies for Data Scientists

Posted on 6.30.2013

Thanks to the growing proliferation of big data into enterprise-level businesses, small businesses and everything in-between, the role of the “data scientist” is becoming increasingly important to the day-to-day operations of many companies, but particularly government organizations, consulting firms, scientific organizations and, of course, tech companies.

If you’re unfamiliar with the term, a data scientist is someone (or some group) that is in charge of managing databases that were previously thought to be too hard to handle because of the large volumes of mostly unstructured data they held.

Of course, when the very definition of your job is to handle the analysis and implementation of a bunch of data that was at one time considered to be too difficult to work with, it certainly helps to go in with a plan. That’s why we’ve compiled these three practical strategies that seasoned and rookie data scientists, alike, can use to improve their efficiency on the job.

Start by assessing your riskiest or most important areas. There’s a lot of data to deal with, and trying to tackle it all at once is a surefire way to overwhelm yourself and dramatically decrease your efficiency. When it comes to practicing data science, the best way to get started is by separating your data into different chunks, analyzing them and then assessing your key drivers/and or riskiest areas. Those are the places where you should begin your practice, so that your efforts will have immediate and valuable results, while also taking care of the most important aspects of your job right off the bat.

Be willing to experiment. One of the most important things to remember when performing analytics and dealing with a lot of data is that you have to be willing to follow the ebb-and-flow of what the data is giving you. That means not trying to find a single tool or platform to answer all of your questions, but rather try different tools or methods to answer a specific question or solve a particular problem. And if ones doesn’t work, that isn’t the end of the world; simply scrap the project and try something else. Remember, these tools are here to help you, not enslave you. Use them as needed and feel free to experiment.

Make use of the data after the fact. Many companies have started profiting off of their data after they’ve already put it to good use in their business strategies by packaging, licensing and reselling it to others. Meanwhile, other companies use the insights they glean to launch brand new, information-based products or services. So, while you’re working with data, also keep in mind (or document, if you can) different ways that the information can later be reused or repackaged to maximize its value and make the most of the time you spend with it.

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