One of the most exciting challenges I have at Hitachi as the Vice-Chairmen of Hitachi’s “Data Science 部会” is to help lead the development of Hitachi’s data science capabilities. We have a target number of people who we want trained and operational by 2020, so there is definitely a sense of urgency. And I like urgency because it’s required to sweep aside the inhibitors and resistors to change.

I started this assignment with a blog titled “…

Hope you all enjoying all the banking services available at your fingertips. Imagine living in the same world without banks and their functions. What would be the best alternative for banking and financial systems?

Many business giants around the world proved Trust-based financial systems results in the gaining active user base. Later it helps your business regarding sales and trade.…

Yes, I know, this has been tried a few times and no one listens.... At least not yet. Despite several studies showing otherwise, teams still punt more than they should. Admittedly, some of these studies have been less than rigorous, and often times, assumptions are made that warrant scrutiny (assuming a 50% success rate on all 4th down attempts for example). But I don't think it is the lack of scientific rigor that keeps change at bay. I think the failure to adopt a novel strategy has a lot…

Deep neural nets typically operate on “raw data” of some kind, such as images, text, time series, etc., without the benefit of “derived” features. The idea is that because of their flexibility, neural networks can learn the features relevant to the problem at hand, be it a classification problem or an estimation problem.  Whether derived or learned, features are important. The challenge is in determining how one might use what one learned from the features in future work (staying…

I was at crossroad to choose my next leap. Cloud, AWS, Agile, Blockchain and many other were the words I heard from the experts and how its boom in the industry. I was confused on what subject and technology should I consider for comprehensive study for my professional growth. I have been working on Business Intelligence tools for quite some time and had inclination towards data and facts that could be supported by data, understanding what data means and uncovering the…

In 1992, I entered the job market and landed a job as an advertising copywriter for McDonald’s. I was tasked with ideating radio, TV and print advertisements to curb burger, fries and soft drink sales. The internet did not exist in the public domain back then, and my first laptop was actually a mechanical type writer. Around 2000, I became a freelance marketing manager, working for small and mid sized businesses. At that time, my English was not good enough to work for companies outside of…

For any business, the worst scenario is getting out of product inventory when customers are ready to buy your product. Keeping a stock of every item in the store is another burden to carry for every business. This trade off has been even more problematic in current times, when manufacturing firms are flooding with SKUs (Stock Keeping Unit) ranging from product sizes, flavours, styles etc. To cater personalised demand companies are customising…

Several years ago, my company faced a significant challenge: A large swath of small new entrants relying heavily on data and artificial intelligence provided services faster, cheaper, and more flexibly than we could. They were not slowed down by legacy information systems, archaic business processes, and an outdated workforce. To add insult to injury, the new entrants would use our customer-facing transparency opportunistically to pick up the low hanging fruit, and gradually started to…

General Data Protection Regulation (GDPR) came into force in European Union (EU) member states from 25^th May 2018. It has far reaching ramifications for businesses and organizations given that data is ubiquitous and all businesses today rely on customer data to remain competitive in their industry and relevant to their customers.

In this blog we will examine some of the challenges that businesses in certain industries can face and what businesses can do about it. GDPR…

You can also fork the Jupyter notebook on Github here!

The goal of this post/notebook is to go from the basics of data preprocessing to modern techniques used in deep learning. My point is that we can use code (Python/Numpy etc.) to better understand abstract mathematical notions! Thinking by…

Summary:  Remember when we used to say data is the new oil.  Not anymore.  Now Training Data is the new oil.  Training data is proving to be the single greatest impediment to the wide adoption and creation of deep learning models.  We’ll discuss current best practice but more importantly new breakthroughs into fully automated image labeling that are proving to be superior even to hand labeling.

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The insurance industry – one of the least digitalized – is not surprisingly one of the most ineffective segments of the financial services industry. Internal business processes are often duplicated, bureaucratized, and time-consuming. As the ubiquity of machine learning and artificial intelligence systems increases, they have the potential to automate operations in insurance companies thereby cutting costs and increasing productivity. However, organizations have plenty of reasons to resist…

Objective

Congestive heart failure (CHF) has been called an "epidemic" and a "staggering clinical and public health problem" (Roger, 2013). It can be defined as the impaired ability of the ventricle to fill or eject with blood.  Consequences include difficulty breathing, coughing fits, leg swelling, decreased quality of life, and ultimately death.  As life expectancy increases globally, we can only expect to see this syndrome more frequently.  Fortunately, the advents of…

Last time, I wrote on wrangling data from a pdf file to assemble a data set of D1 college athletic performance in the Learfield Directors' Cup competition. In this blog, I embellish that data, calculating individual school ranks from scores…

The release of Hadoop 3 in December 2017 marked the beginning of a new era for data science. The Hadoop framework is at the core of the entire Hadoop ecosystem, and various other libraries strongly depend on it.

In this article, we will discuss the major changes in Hadoop 3 when compared to…

Artificial Intelligence with Python

By Prateek joshi

Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you

What you will…

This blog is to give brief introduction about Hadoop for those who know next to nothing about this technology. Big Data is at the foundation of all the megatrends that are happening today, from social to the cloud to mobile devices to gaming. This blog will help to build the foundation to take the next step in learning this interesting technology. Let's get started:

1. What's Big Data?

Ever since…

In last part we have seen the basics of Artificial intelligence and Artificial Neural Networks. As mentioned in the last part this part will be focused on applications of Artificial neural networks. ANN is very vast concept and we can find its…

Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture of the week.

Featured Resources and Technical Contributions 

• Statistical Tests - When to use Which…

I wrote about this long ago (see here in 2014), and so did many other practitioners. This new post shows more maturity I think, a more coherent view about the various data scientist roles in the Industry (now that things are getting more clear for most hiring managers), and how these scientists interact between themselves and with other teams. It is also a short…