Customer Segmentation with RFM Analysis. Finally a clustering model you won’t have to spending time explaining.
Any good data scientist is (or at least should be) adept at taking complex mathematical and statistical models and explaining them in a simple and concise manner. In the end, our job is to create value for our company or client. Even if we have a model with 99.9999999% accuracy, management is unlikely to use it to make decisions unless they understand (at the very least) the basics of the model.
A large part of any business is built around understanding the company’s clients, and ensuring their needs and wants are being satisfied. This helps us ensure our clients are actually using the products we’re creating/providing and that we’re spending our own resources in optimal business segments. A common approach to understanding our clients is to segregate them into distinct groups. Instead of trying to understand and develop products for hundreds of thousands of individual people or companies, we can instead focus our efforts on a few distinct groups which represent our underlying clients. This allows us to make more informed, targeted decisions that will have a greater impact. In short, it ensures we see the forest and not the trees.
If you’ve been around machine learning over the past few years, you’re brain has automatically already switched into unsupervised learning mode and you’re already thinking of coding up a k-means or nearest-neighbor model. I can’t deny that I’m not usually right there with you. But let’s take a step back. Is there a simpler method? One that requires almost zero explanation to management? One that’s much less computationally expensive?
Learn why marketing analytics often fails marketers and how data scientists can fix the problem.Industry insiders have always claimed that the Great Recession was a good thing for marketing analytics.
Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
We provide an updated list of best online Masters in AI, Analytics, and Data Science, including rankings, tuition, and duration of the education program.
For Big Data Analytics, the challenges faced by businesses are unique and so will be the solution required to help access the full potential of Big Data.
A data scientist/analyst in the making needs to format and clean data before being able to perform any kind of exploratory data analysis.