Today's Tech Digest

7 Steps to Mastering Data Preparation with Python

For our purposes, however, we will separate the data preparation from the modeling as its own regimen. As Python is the ecosystem, much of what we will cover will be Pandas related. For the uninitiated, Pandas is a data manipulation and analysis library, is one of the cornerstones of the Python scientific programming stack, and is a great fit for many of the tasks associated with data preparation. Data preparation can be seen in the CRISP-DM model shown above (though it can be reasonably argued that "data understanding" falls within our definition as well). We can also equate our data preparation with the framework of the KDD Process -- specifically the first 3 major steps -- which are selection, preprocessing, and transformation. We can break these down into finer granularity, but at a macro level, these steps of the KDD Process encompass what data wrangling is.

The Difference Between Entrepreneur and Executive

Entrepreneurs must understand that their business(es) should run without them. Systems and structure must be executed by management and each member of an enterprise should know his/her role. When venture capitalists and bankers invest in a new start-up, it is the first thing they look for – business structure. The passionate nature of the founder may get them to the table, but it is true day-to-day business management they look for. Look at Ray Kroc, founder of McDonalds. ... Executives, on the other hand, should take a page from the entrepreneur by looking beyond the numbers and going with their gut. When Mazda introduced the Miata, all the marketing data out there said nothing about a little convertible sports car. It was the last thing on the American consumers’ mind. But Mazda did the unthinkable – they put passion back into driving with a fun and affordable roadster that brought back the days of British MG Midgets and weekends in the country.

Great Data Scientists Don’t Just Think Outside the Box, They Redefine the Box

The data scientists didn’t wait until someone developed a better Machine Learning algorithm. Instead, they looked at the wide variety of Machine Learning and Deep Learning tools and algorithms available to them, and applied them to a different, but related use case. If we can predict the health of a device and the potential problems that could occur with that device, then we can also help customers prevent those problems, significantly enhancing their support experience and positively impacting their environment. ... One of a data scientist’s most important characteristics is that they refuse to take “it can’t be done” as an answer. They are willing to try different variables and metrics, and different type of advanced analytic algorithms, to see if there is another way to predict performance. This graphic measures the activity between different IT systems. Just like with data science, this image shows there’s no lack of variables to consider when building your Machine Learning and Deep Learning models!

This NYC Startup Supercharges Advisors With AI and NLP

By focusing on data that is often overlooked or misclassified such as tickers, instrument names, strategies, investment goals and many other financial entity types, we’re able to provide “4K NLP for financial data” as an input into our engine. Its robust platform includes three new configurable APIs; the first, Personalized Insights, curates personalized stories of “what to say”, the second – Client Prioritization API helps answer the question of “who to talk to” by providing a prioritized list of clients to call, with the reasons for out reach. The company’s third API, Expert Conversation, is a natural language interface with data aggregation, curation and linking capabilities. It is focused on question answering for market, ETF, mutual fund and equities research. It’s a smarter, faster way to get answers to questions that are buried in research reports or sits behind many screens.

Hackers create 'ghost' traffic jam to confound smart traffic systems

The attack manipulates the mechanism I-SIG uses to manage queues, by spoofing the attack vehicle's predicted arrival time and the requested phase of the traffic lights (I-SIG lets vehicles request a green light for their arrival, and decides whether or not to grant it based on the queue it's created of all the incoming requests). “The attacker can change the speed and location in its BSM [Basic Safety Message – El Reg] message to set the arrival time and the requested phase of her choice and thus increase the corresponding arrival table element by one”, the paper said. The attack, they claimed, has a 94 per cent success rate, and on average, would increase delays by 38.2 per cent. The best defence against these and other attacks, the researchers say, is a combination of more robust algorithms, better performance in the roadside units that give the system its realtime feedback, and better validation of vehicle-originated messages.

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