In case you haven't heard yet, Data Science is all the craze. Courses, posts, and schools are springing up everywhere. However, every time I take a look at one of those offerings, I see that a lot of emphasis is put on specific learning algorithms. Of course, understanding how logistic regression or deep learning works is cool, but once you start working with data, you find out that there are other things equally important, or maybe even more. I can't really blame these courses.

Why: 72% of individual investors -- especially next-generation/Millennial investors -- believe that companies benefit when they focus on sustainability. About TruValue Labs: TruValue Labs is the first technology company to apply artificial intelligence (AI) and machine learning to the 80% or more of financial information about public companies obscured in unstructured data. TruValue Labs' flagship product, Insight360, makes Environmental, Social, & Governance (ESG) characteristics easy to understand and communicate. Offered as a SaaS subscription, Insight360 monitors thousands of public companies in tens of thousands of sources and analyzes ESG data to provide actionable investment insights in real-time.

BENGALURU HYDERABAD: Apple's recent acquisition of Indian machine-learning startup Tuplejump offers further evidence of a revival in the interest of global technology giants in the country's startup space, especially in areas such as artificial intelligence, cloud infrastructure and automation. Enthusiasm for tech startups in India had waned in the last two years with fewer exits, a funding crunch and an inability to scale up. That seems to be changing with Intel, Apple and Nutanix shopping around for companies and the people who work there. Intel bought Soft Machines, a Silicon Valley chip designer with offices in Hyderabad, for 300 million in September. The company was cofounded by former Intel veteran Mahesh Lingareddy.

Decision Trees are a graphic and intuitive method of predicting the outcome of a given input. They attach a weightage to the input variables and help you clearly detect what really influences your outcome. Building a Decision Tree is a tedious procedure, as they have the tendency to overfit. That's where Random Forests come into the picture. Random Forests use an ensemble of Decision Trees, this reduces the complexities without compromising on the advantages.

Next-generation cognitive computing is redefining how we live and work as more businesses are using all the data available to them to improve performance and customer service, and drive innovation and revenue. Today's business challenges have never been more complex, and the critical insights that can help address these challenges are often buried in an avalanche of data. Previously, these insights were beyond the capabilities of conventional computing solutions – programmable systems based on mathematical principles that harken back to the 1940s. But IBM Watson has changed the game. IBM Watson is built upon a new foundation called cognitive computing – a system that learns and reasons from interactions with humans, files, online interactions and its environment.

Google's DeepMind AI division is famous for defeating the Go world champion and for performing medical research with the UK's NHS. But the team has developed a very impressive new technology, that allows the AI, or deep neural network, to mimic human speech. Talking to our robots, and them answering us back, has been a sort of dream for the field of artificial intelligence. In recent years the technology has gotten better, as most of you know from Siri, Cortana or Google Now voice interactions. But even our powerful digital assistants still rely on pre-recorded human voices, or they quickly turn cold and robotic. However, the work done by the DeepMind engineers may help change that forever.

The legal sector is more advanced in use its use, understanding and application of AI technologies, a New CenturyLink study suggests. Before 2015, the legal sector was one of the least technologically ept professions. But since then it has undergone an automation transformation. In history and practice, law has largely relied on lawyers and legal aids sifting through copious documents seeking evidence or precedents. A precedent is defined as'an earlier event or action that is regarded as an example or guide to be considered in subsequent similar circumstances', according to Google.

We can use unsupervised machine learning to identify which drugs are associated with which adverse events. Specifically, machine learning can help us to create clusters based on gender, age, outcome of adverse event, route drug was administered, purpose the drug was used for, body mass index, etc. This can help for quickly discovering hidden associations between drugs and adverse events. Clustering is a non-supervised learning technique which has wide applications. Some examples where clustering is commonly applied are market segmentation, social network analytics, and astronomical data analysis.

Click here to discover dozens of free data science and machine learning related books. Also, most of the upcoming Data Science 2.0 book is available for free here. An earlier version, Data Science 1.0 (also free, somewhat outdated) can be found here. How can data scientists stay abreast and viable with all the tools, languages, skills, machine learning etc..? Each of these can be specializations taking decades to master.

Online Web Based Business showed first retail, and then, many other sectors, that Internet'location' was rapidly becoming more important than the physical location in attracting business. As Internet based business expands and transforms with IoT then understanding the concept of'interactions', rather than the format of IT based'Transactions', becomes necessary. The Enterprise orchestrates IoT event'Interactions' from the market into an the fastest optimum response using its internal IoT assets is the new competitive winner. Each stage of development of Internet based capabilities has increased the focus on winning business through smarter competitive Internet connected interactions. The recognition of the differences between activities and technologies of the so-called'Front Office' versus the'Back Office' become even more apparent with the introduction of IoT.