As 2020 has clearly shown us, nobody can actually predict the future. But there are some people who come pretty close, and their profession may surprise you. Data scientists (yes, you read that right) can practically predict the future of certain industries using big data and a coding language called Python. Knowing that, it's not hard to see why Glassdoor named data scientists the third most desired job in the US, with over 6,500 openings, a median base salary of $107,801, and a job satisfaction rate of 4.0. If you're looking for a new career path with a handsome salary and the ability to basically predict the future, check out this e-book and course bundle to get started.

Web Scraping is a popular methodology to extract data from websites. This is often done to derive insights for Sentiment Analysis, Predicting User preferences, Cross-Selling products, etc. Some of the real-life examples of web scraping include – extracting data for pricing analysis, user ratings for movie sentiment analysis, corporate admin tasks to read and classify log files in an HTML, search bots trying to make sense of a results page. While web scraping activity does not provide intelligence of its own, as we have seen above the data extracted can be useful in multiple ways. A more common use case would be a start-up eCommerce website trying to set a price on its products based on market research on competitors.

Differential privacy has become an integral way for data scientists to learn from the majority of their data while simultaneously ensuring that those results do not allow any individual's data to be distinguished or re-identified. To help more researchers with their work, IBM released the open-source Differential Privacy Library. The library "boasts a suite of tools for machine learning and data analytics tasks, all with built-in privacy guarantees," according to Naoise Holohan, a research staff member on IBM Research Europe's privacy and security team. "Our library is unique to others in giving scientists and developers access to lightweight, user-friendly tools for data analytics and machine learning in a familiar environment–in fact, most tasks can be run with only a single line of code," Holohan wrote in a blog post on Friday. "What also sets our library apart is our machine learning functionality enables organizations to publish and share their data with rigorous guarantees on user privacy like never before."

DuPont has a rich history of scientific discovery that has enabled countless innovations and today, we're looking for more people, in more places, to collaborate with us to make life the best that it can be. DuPont Pioneer is aggressively building Big Data and Predictive Analytics capabilities in order to deliver improved services to our customers. We seek a strong data scientist with a background in math, statistics, machine learning and scientific computing to join our team. This is a critical position with the potential to make immediate, significant impact on our business. The successful candidate will have an extensive background in statistical computing and machine learning through courses or thesis/dissertation, and proven experience validating models against experimental data.

This calls for an increase in budgetary allocation increase and more computing power (that can be leveraged) to be added from outside core IT. AIOps bridges the gap between service management, performance management, and automation within the IT eco-system to accomplish the continuous goal of IT operation improvements. AIOps creates a game plan that delivers within the new accelerated IT environments, to identify patterns in monitoring, service desk, capacity addition and data automation across hybrid on-premises and multi-cloud environments.

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Principal Component Analysis (PCA) is a great tool for a data analysis projects for a lot of reasons. If you have never heard of PCA, in simple words it does a linear transformation of your features using covariance or correlation. I will add a few links below if you want to know more about it. Some of the applications of PCA are dimensional reduction, feature analysis, data compression, anomaly detection, clustering and many more. The first time I learnt about PCA, it was not easy to understand and quite confusing.

Since its advent, machine learning has altered the world of technology one industry vertical at a time. Starting from the predictive analytics engines that generate recommendations to the artificial intelligence technology used in a myriad of antivirus applications, this is all machine learning at play. But what happens when these systems get confused or, worse, get attacked and purposefully manipulated into making wrong decisions? Thus, like any other technology, it is crucial to analyze machine learning's advancing canvas and the potential risks of misuse that comes with it. First, let's answer the question, "What is machine learning?"

This calls for an increase in budgetary allocation increase and more computing power (that can be leveraged) to be added from outside core IT. AIOps bridges the gap between service management, performance management, and automation within the IT eco-system to accomplish the continuous goal of IT operation improvements. AIOps creates a game plan that delivers within the new accelerated IT environments, to identify patterns in monitoring, service desk, capacity addition and data automation across hybrid on-premises and multi-cloud environments.

In an increasingly competitive world, we should have a deep understanding of the business in which we operate, how it is evolving, and the new innovations that we could embrace or build to remain competitive and conquer new market segments. To do this, we must be able to develop a clear vision of transformation that takes us to another level of performance. By embracing Digital Transformation, we will deal with artificial intelligence, machine and deep learning, virtual reality, and a lot of other innovative technologies. At first sight, it might even sound fearful to lead the business in such a complex and intricate direction. With this in mind, we will consider some strategies to better understand and take competitive advantage of the huge streaming of data in the current era of the digital revolution.