This event, presented by the Global Big Data Conference in Seattle, features discussions, case studies and in-depth workshops to help industry leaders initiate and expand AI practices in their organizations. Specialty areas of interest at the conference include IoT, data mining, data analytics, representation learning, cognitive computing and speech recognition.
Businesses today are leveraging ever-increasing volumes of data for competitive advantage. That means employing emerging technologies in data science, artificial intelligence, machine learning and even deep learning to prepare and organize big data and develop the machine learning algorithms and predictive models that support business intelligence applications used by analysts and information workers. Here's a look at 10 data science and machine learning startup companies with leading-edge products in the data science and machine learning arena that solution providers should be aware of.
This post is to share with you the recent publication of the book: "Data Science for Economics and Finance: Methodologies and Applications", by Sergio Consoli, Diego Reforgiato Recupero, and Michaela Saisana. The use of data science and artificial intelligence for economics and finance is providing benefits for scientists, professionals and policy-makers by improving the available data analysis methodologies for economic forecasting and therefore making our societies better prepared for the challenges of tomorrow. This book is a good example of how combining expertise from the European Commission, universities in the U.S. and Europe, financial and economic institutions, and multilateral organizations, can bring forward a shared vision on the benefits of data science applied to economics and finance; from the research point of view to the evaluation of policies on the other hand. It showcases how data science is reshaping the business sector. It includes examples of novel big data sources and some successful applications on the use of advanced machine learning, natural language processing, networks analysis, and time series analysis and forecasting, among others, in the economic and financial sectors. At the same time, the book is making an appeal for further adoption of these novel applications in the field of economics and finance so that they can reach their full potential and support policy-makers and the related stakeholders in the transformational recovery of our societies.
Artificial intelligence is a promising technology, that has made significant changes in the 21st century. Starting from self-driving cars and robotic assistants to automated disease diagnosis and drug discovery, the stronghold of artificial intelligence is no joke. Along with artificial intelligence, data science has also shifted the way we live and work. With the demand for data science and artificial intelligence spiralling, the job market is opening its door to AI and data science jobs. The tech sphere has ensured that artificial intelligence jobs and data science jobs provide limitless opportunities for professionals to explore cutting edge solutions.
Elena works in the field of Natural Language Processing. She graduated with a degree from Saint-Petersburg State University in Russia first and then acquired PhD from Macquarie University in Sydney, Australia, where she works currently. Now she applies theoretical concepts developed in the field of Natural Language Processing to solve business problems of different big and small enterprises. As an early adopter of BigData tools and concepts she finds existing BigData frameworks to be attractive means of working with data. She started using such tools and advising other people to adopt BigData concepts way before Hadoop, Spark and other related technologies became "must to know" tools for many IT professionals.
Uplatz provides this frequently asked list of Data Science Interview Questions and Answers to help you prepare for the Data Scientist and Machine Learning Engineer interviews. This comprehensive list of important data science interview questions and answers might play a significant role in shaping your career and helping you get your next dream job. You can get into the mainstream of the Data Science world learning from this powerful set of Data Science interview questions. Data Science can be defined as multidisciplinary blend of trends prediction, data inference, algorithm development, and technology to solve analytically complex problems. At the core of data science is nothing but data.
Hello!!!, Nice to see you again!! & Welcome to our ArtificaLab Medium Page!! Today, let's dicuss about the Hottest topics right now, Industry 4.0. So, we all see in newspaper, social media articles, facebook posts about the current Industry 4.0, their technologies in these days?? right…. In simplest terms, Industry 4.0 refers to the Unified Communication of Machines & Technologies (such as Cloud, IOT, AI, Big Data) in order to produce effective output, qualified products to consumers than ever before. In fact, "Industry 4.0" has a long history, in which it is called "Fourth Industrial Revolution". This has been back to the previous ages before, where it started from Industry 1.0 "First Industrial Revolution".
As someone working in data science for over a decade, it is frustrating to see people prophesying on how the field will get extinct in 10 years. The typical reason given is how emerging AutoML tools will eliminate the need for practitioners to develop their own algorithms. I find such opinions especially frustrating because it dissuades a beginner from taking data science seriously enough to excel in it. Frankly, it is a disservice to the data science community to see such prophecies about a field where the demand is only going to increase even further! Why would any sane person invest their finite time and energy in learning something that will become extinct soon?