Today, there're dozens of data science techniques which we can apply to different solution formats. A business would typically use dashboards for CXO board members for sharing metrics like pipeline, revenue, profits and costs. On a separate note, if you're working to define a use flow on website, you may want to use a decision trees. There's 20+ key data science methods used at work as of today, across globe. Let's talk about Top 3 such methods.

Data visualization

As you can guess, this forms the most widely used method. Reason? A picture is worth a thousand words. Let's say we want to say, that there's are 410 people in a room. 50 of them are looking for an exit, and 125 of the rest 360 are looking to collaborate and plan dinner. We're not sure what the rest of them are doing. Can't we tell the same by the following visualization?

Logistic Regression

What is regression? - Simply put, it's a method to predict of an outcome variable, from one or multiple explanatory variables.

What's logistic regression? - It's a specific method, to describe a binary outcome variable. Let's say you want to launch a business hotel, and you have a list of potential clients. You want to invite a limited number of your list to an even, to maximize your sales via a yearly membership. How to decide that who should you invite? Data on previous events you have would a logical step to predict a person's likelihood of buying, given the information you have on them.

Large businesses typically use these models, to prioritize follow ups. For example, if you own a website that gets 1000 leads per day, and you just have 2-3 sales people, how would they know whom to contact first? Those 1000 can include students, professionals, researches, customers, potential customers, casual browsers etc. Such filters of often done by scoring these records based on their activity, and some profile data. A value is added to each activity which we want to count, and based on a total score, sales would follow up accordingly. Below is an example of this kind of scoring methodology.

You can see that while Lead A visited home page of the website, visited a career page, and then visited contact us page, it's overall score was calculated to be 2. However, Lead B, visited Contact Us, Visited Services Page, Pricing Page, and even filled out the form to Contact - a clear signal of interest, and was score 10 times Lead A. These scores help, when you have large amount of leads. Today, thanks to Big Data, we have businesses which process more than 10 million records on an average. Even a full dedicated team of sales, needs such systems to identify best bets. Besides, notice how thee career page visit actually subtracts 1 from the scores, to accommodate a negative interest. Career page could be good for hiring, but not for a business unit, directly.

Cross-validation

In Machine Learning, Cross-validation is a re-sampling method used for model evaluation to avoid testing a model on the same dataset on which it was trained. This is a mistake, especially that a separate testing dataset is not always available. Ultimately, this leads to inaccurate performance measures (as the model will have an almost perfect score since it is being tested on the same data it was trained on). To avoid this kind of mistakes, cross validation is usually preferred.

Here's a Wikipedia Illustration -

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