Although reading books and watching lectures is a great way to learn analytics – it is best to start doing. However, it can be quite tricky to start doing when it comes to languages such as Python and R if someone does not have a coding background. Not only do you need to know what you are doing in terms of analytical procedures, but you also need to understand the nuances of programming languages which adds onto the list of things to learn to just get started. Therefore, the best middle ground between knowledge acquisition (books, videos, etc.) and conducting advanced analytics (Python, R, etc.) is by using open-source analytics software. These types of software are great for both knowledge acquisition and actually doing analysis as documentation is built into the software and you can start doing relatively complex tasks with only mouse clicks.

Analytics India Magazine got in touch with Anil Arora who is the principal data scientist at SAS. With 11 years of analytics experience, he has worked across areas such as banking, insurance, telecom, retail, e-commerce, utilities, public services industries and more. Analytics India Magazine got in touch with Arora to get an insight on the various kind of analytics and data science tools that are used by analytics practitioners at SAS. Below is the complete Q&A with his detailed insights. AA: SAS provides a cohesive, unified analytics platform in the form of Viya that addresses the complete analytics lifecycle covering data management, data discovery, model building and model deployment. It is the foundation of a suite of offerings, including machine learning and visualisation, to address any analytic challenge.

This page features a rough depiction of different views on the question of whether artificial general intelligence (AGI) will take off in a "hard" way (fast, no time for response or competition) or a "slow" way (more gradual, more time to integrate with society, possibility of competing projects). I plot these views against a very crude estimate of how long each forecaster has worked on commercial software (not counting academic computer science). It would be interesting to slice these predictions along many other dimensions as well. It would furthermore be helpful to gather statistically valid data from surveys of AI experts. My graph here is just something I put together in a few hours based on what I already knew offhand.

This page features a rough depiction of different views on the question of whether artificial general intelligence (AGI) will take off in a "hard" way (fast, no time for response or competition) or a "slow" way (more gradual, more time to integrate with society, possibility of competing projects). I plot these views against a very crude estimate of how long each forecaster has worked on commercial software (not counting academic computer science).

At the start of any machine learning project, you face an important choice: Which language or software should I use? Well, you have many options to choose from. Python, R, SAS, MATLAB… the list goes on. But first, you'll actually need to make another choice: Should I go with open source or commercial software? Open source code is "freely available and may be redistributed and modified."

It is rightfully said that data is money in today's world. Along with the transition to an app-based world comes the exponential growth of data. However, most of the data is unstructured and hence it takes a process and method to extract useful information from the data and transform it into understandable and usable form. Data mining or "Knowledge Discovery in Databases" is the process of discovering patterns in large data sets with artificial intelligence, machine learning, statistics, and database systems. Free data mining tools ranges from complete model development environments such as Knime and Orange, to a variety of libraries written in Java, C and most often in Python.

When we work harder, our hearts beat faster. When we're hot, we sweat. But in the 54 years since British mathematician Alan Turing introduced the notion of artificial intelligence, computer scientists haven't delivered anything close to a self-aware and self-healing computer. That may change soon enough. Researchers in business and government labs are building systems that will challenge what it means to be an IT worker by automating many of the monitoring and maintenance tasks done today by hand.