Prediction using Neural Networks: In the first part of this post, we discussed what neural networks prediction are, what the “artificial” component in them is, and how they are used in data science. Today we look at how they are used in predictive analytics. We will also a
Why are Artificial Neural Networks important in data science? Artificial Neural networks are an extremely complicated piece of research/technology being developed for decades. They use man-made computing power to try and replicate the way the human brain calculates, and
Why Machine Learning Data Catalogs (MLDCs) are becoming popular In part one of this blog post we had discussed what data catalogs are, and why there is an increase in their use by enterprises over the last two years. In this second and final part of that post, we look at
AI For Data Cleaning: How AI can clean your data (AI data cleaning) and save you man-hours and money? Dirty data is the bane of the analytics industry. Almost every organization that deals with data have had to deal with some degree of unreliability in its numbers. Table
Machine Learning in Data Analytics: How It Works for Your Business? In this post, we will try and answer the question – how does machine learning in data analytics work for your business? In part one of this blog post, we looked at how artificial intelligence (AI) can hel
How Artificial Intelligence turns data into useful information? We are often asked – explain AI from the viewpoint of a data lifecycle, or just how does artificial intelligence (AI) convert data into output that`s beneficial to a business? Machine learning (ML), which ca
A minuscule percentage of those in the business of providing BI solutions have adopted NLP and adapted it to generate results for Enterprise clients. While the figure may be small today, advancements in the field are bound to push the number up.
Readers of this blog may have realized that Natural Language Processing (NLP) was missing from our ‘5 Data Analytical Trends To Watch For in 2018’ post. Our in-house team of predictive data analysts say it lost out to the other trends by a narrow margin. But that in no way takes away from the importance of NLP and its growing influence in the world of big data analytics. The loser by a whisker surely deserves an honorable mention, hence this 2-part post.
Customers leave behind an incomprehensible amount of data while they go about shopping. Making sense of that data and reacting in real time are the two things that will keep companies one-step ahead of their customers (and competition) in the present-day customer-centric world.