Advanced analytics: A step ahead

Python is just the stepping stone towards never-ending possibilities of conducting advanced data analysis. There is more to it than we know. Gartner defines advanced analytics as the autonomous or semi-autonomous examination of content or data using sophisticated techniques and tools, typically, beyond those of traditional business intelligence, BI, to discover deeper insights, make predictions, or generate recommendations. Advanced analytic techniques include data or text mining, machine learning, pattern matching, forecasting, semantic analysis, network and cluster analysis, multivariate statistics, graph analysis, simulation, complex event processing, neural networks, et cetera. As data science is multidisciplinary, all of these techniques have overlapping concepts, but the end goal may be a tad bit different. Let us see how they are different.

Data science is an umbrella of several techniques that are used for extraction, cleansing, preparation, and final analysis of data. Using these, data applications and products are created that deal with the data in a way that conventional systems are unable to do.

This is a field intended to provide computers with the abilities to learn about newer datasets with being programmed with explicit logic. It’s the process to create and use algorithms that apply statistical methods on the data to gain knowledge from data. In the process, it lets computers find the apparently hidden insights without any explicit logic. This is a process of garnering information from huge datasets that were previously incomprehensible and unknown, and then using that information to make relevant business decisions. We can say that data science focuses on the science of the data while data mining is concerned with the process of extracting insights from the big datasets.

This helps in creating the graphical representations of the underlying dataset in such a way that intuitive inferences can be made by looking at the visuals so that you can understand the data deeply and perform data manipulations and feature engineering before some of the machine learning algorithms can be applied to the data. This is one of the fundamental skills for data analytics as various data mining procedures are based on some data assumptions around data distributions, data scaling, data skews, et cetera, and application of statistical methods using computer programs and algorithms to the underlying data. It should be noted that all the advanced data analytics techniques at its core will utilize some or all of the techniques from the areas described above.

Pause the video here to understand the applications better.

Now that we have the general idea about various advanced analytics techniques, let’s understand some of these areas in a little more detail and understand the business applications.