data science red pill

In todays world with a variety of algorithms to solve specific business related data problems, how do you choose the right data science approach? Does such a thing even exist? Experts don’t spend too much time on this nowadays because you can try several different techniques simultaneously at not too much of a computational cost. However, interpretation costs are usually ignored.

right data science approach - red pill for data science

For example, some bivariate analysis methods for data science can reduce high intensity statistics. There are quite a few advantages that come with this – probably the most cost-effective advantage is that you don’t need a Master’s degree in statistics to make sense of the analysis.

What is the “right data science approach”?

The “right” data science approach refers to the process of exploring large volumes of data with the goal of

  • Detecting patterns and relationships between the different variables within the data
  • Extracting key insights from the data that can translate into something of business value

Traditional statistics, for all its strengths faces two challenges when it comes to democratizing business analytics: it requires an expert to recognize the best choice of technique for your problem and then to translate statistical insights into the language of business.

Data Science with a simple bivariate analysis can overcome these limitations. Given the data,

1. All patterns can be automatically extracted

2. Dimensionality of the problem can be quickly reduced

3. Key parameters of interest can be quickly identified

These are just the right initial steps or what can be called the “right data science approach” to get started with handling business data problem. The complete solution requires agility in switching algorithms if the answers are not intuitive for business. It also requires ability to interpret the solutions to convince business stakeholders that the methods are effective.

Originally posted on Wed, Oct 06, 2010 @ 05:55 PM

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