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.

From the media to articles to job postings to the words of top leaders of big companies, the term that seems to be everywhere is "data science". So, if you are familiar with technology and/or interested in learning new things about technology, then you have likely pondered this: What is data science? How does one become a data scientist? Well, the answers will be given right here. Data science is a field that deals with the extraction of meaningful insights which include usage, trends, customer behaviour, etc. from raw data by using complex tools and algorithms, machine learning processes, mathematics, statistics, and other similar areas.

Applying Data Science in HR and extracting actionable data intelligence can not only help companies keep their employees happy and retain valuable talent but it can also help in reducing costs and driving revenues. Critical HR processes like recruiting, training & development, work performance analysis, employee dissatisfaction management, and retention management, etc, require everyday analysis to generate insights for proactive action. These activities consume a lot of time, resources and investments of the company. The time and investments made in hiring get wasted if the company doesn't pick the right candidate for the right job at the right time. When a company publishes a vacancy for a role, the applications are received from various sources in large numbers.

How has the newer data science technology such as Watson Studio, Watson Machine Learning and Watson OpenScale been received by the business partner community? I mentioned in our previous blog that I was pleasantly surprised at how many IBM Business Partners have established a Data Science practice. The new data science technology has been very well received by our partner community. The partners closed out a very strong Q4 2019 demonstrating the value that they and their customers see in Watson Studio, Watson Machine Learning and Watson OpenScale. This is encouraging and demonstrates that we are building products that resonate in the market with our partners and their customers.

Also, terms like data science, analytics, statistics, databases etc are ones that most people associate with the professional world exclusively. However, all these and many of their associated ones aren't meant for data science professionals only. In reality, most people experience the role of data science in their day-to-day lives and in almost every situation. From new friend suggestions by Facebook to Google's help to complete a search phrase to television shows predicted by Netflix in accordance to your preferences, and many more -- data science is being used by common people in almost every situation. We often praise the high-tech device or the high-end technology once we leverage its abilities, failing to acknowledge the role of data science in making them happen.

From minimizing accidents, traffic management, ticketing and preventive maintenance of fleets, AI has the potential to transform the transportation sector. The adoption of new technologies has helped the transportation sector innovate and evolve over the years. Today it is time for the industry to leverage Artificial Intelligence ( AI). AI, a technology that provides machines the ability to think like humans, is transforming the industry. The application of AI in transportation can help the industry in several areas including passenger safety, traffic management, and energy efficiency, amongst others.

Applying data science technologies like AI, NLP, and machine learning algorithms can help banks in several areas like fraud detection, risk management, customer sentiment analysis, and personalized marketing. Data science is disrupting the banking sector like never before. Banks are sitting on piles of data and harnessing the volumes of data is helping banks in various ways, from process automation, process improvements to exploring new delivery models and introducing new services. Every year financial institutions are spending billions against fraud detection applications, as it may hurt the company's brand and reputation. Data science plays a key role in collecting, summarizing, and predicting the customer database to detect fraudulent activities.

Data intelligence is driving the next wave of revolution in healthcare. As providers adopt a more holistic data-centric approach, it will lead to better treatment outcomes, personalized treatment, and preventive interventions. One of the disruptive trends to watch in recent times is the way data is being democratized in the healthcare industry. From a siloed approach to the use of technology and data, we are now witnessing data-driven value creation across the ecosystem. As new data technologies with advanced intelligence capabilities emerge healthcare companies now have an opportunity to better capitalize on data, innovate patient care and drive profitability while managing growing risks in patient privacy and data security.

Work with other developers and data scientists to code proof-of-concept projects on large scale data sets. Develop data processing and system integration applications. Construct web based user interfaces and visualizations. Quickly ingest new technologies to consider applicability to current or future needs. Utilize statistics and predictive analytics to create innovative solutions to business problems.

Summary: For those of you traditional data scientist who are interested in AI but still haven't given it a deep dive, here's a high level overview of the data science technologies that combine into what the popular press calls artificial intelligence (AI). We and others have written quite a bit about the various types of data science that make up AI. Still I hear many folks asking about AI as if it were a single entity. AI is a collection of data science technologies that at this point in development are not even particularly well integrated or even easy to use. In each of these areas however, we've made a lot of progress and that's caught the attention of the popular press.