Data Science is one of the best careers you could be getting into right now. Companies are hiring legions of data scientists at excellent salaries, and the work is as challenging as it is enjoyable. It's no surprise, then, that we've seen a blossoming of books, courses, and entire educational programs aimed specifically at training data scientists. But there are many people, myself included, who like to do part or all of their learning from books. Being able to re-read important sections, pause to think over a problem, and circle back around to earlier chapters combine to make for a very effective way to climb the learning curve.
The area burned by wildfires each year across the Western United States has increased by more than 300 percent over the past three decades, and much of this increase is due to human-caused warming. Warmer air holds more moisture, and the thirsty air sucks this from plants, trees, and soil, leaving forest vegetation and ground debris drier and easier to ignite. Future climate change, accompanied by warming temperatures and increased aridity, is expected to continue this trend, and will likely exacerbate and intensify wildfires in areas where fuel is abundant. Park Williams, a Lamont-Doherty Earth Observatory associate research professor and a 2016 Center for Climate and Life Fellow, studies climatology, drought, and wildfires. He has received a $641,000 grant from the Zegar Family Foundation that he'll use to advance understanding of the past and future behavior of wildfires.
When Alan Turing invented the first intelligent machine, few could have predicted that the advanced technology would become as widespread and ubiquitous as it is today. Since then, companies have adopted AI for pretty much everything, from self-driving cars to medical technology to banking. We live in the age of big data, an age in which we use machines to collect and analyze massive amounts of data in a way that humans couldn't do on their own. In many respects, the cognition of machines is already surpassing that of humans. With the explosion of the internet, AI has also become a critical element of web design.
We are excited to announce a third series of GeoAI workshops at SIGSPATIAL 2019, in Chicago, IL. GeoAI2019 aims to continue bringing together geoscientists, computer scientists, engineers, entrepreneurs, and decision makers from academia, industry, and government to discuss the latest trends, successes, challenges, and opportunities in the field of deep learning for geographical data mining, to provide actionable intelligence and power new geographic scientific discoveries. Through the workshop, attendees will be able to exchange the latest information on techniques and workflows used in artificial intelligence for spatial research. With a combination of geo-computational methods and geographic research, we invite you to join us at GeoAI2019. The workshop will be interactive to engage in discussions, shape the research directions, and disseminate state-of-the-art solutions.
Travel & tourism is on its rise nowadays. This may be explained by the fact that it has become more affordable to a broader audience. But, in today's fast-paced world, finding time to travel to a ticket office and get your tickets is a luxury few can afford. Like any other industry, machine learning, AI and big data analytics have changed the travel & hospitality industry as well. In this post, we will discuss the major applications and future scopes of machine learning, AI & big data analytics in the travel & hospitality industry – across the globe and India. Additionally, we will also have a look at how machine learning, AI and big data analytics are reshaping the hospitality job market. Due to rapid digital transformation, over 500 billion dollars ($564.87 billion) was made in the travel & hospitality sector in the year 2016 alone. The number is expected to reach $817.54 billion by 2020.
Businesses that work with artificial intelligence (AI) and machine learning (ML) are set to grow their usage of the technology in the next few years, according to a new Gartner report. The analyst firm found that most top businesses currently have four AI or ML projects running today on average, and expect to add six more projects within the next 12 months. Within three years, businesses expect to add another 15 AI / ML projects, meaning that by the time we hit 2022, many companies will have an average of 35 AI / ML projects in place. Businesses use AI and ML mostly to improve their customer experience, but they find the technology super useful internally, to support decision-making and give employees valuable recommendations. The second most important project type seems to be task automation, as 20 per cent of respondents claimed it was their number one motivator.
To perform a better assessment of the value that is brought through analytics, we asked respondents exactly what they used the data and analytics in their organisation for. A compelling 98% of all respondents believed that analytics did play a role in their organisation. Its deployment, however, varied from case to case. When asked about the role that analytics played in their system, 39% of respondents believed that analytics was used for making both tactical and strategic decisions across the organisation.
At Etsy, the search challenge is particularly tough. The site's stock in trade is not the sort of mass-produced goods that can be neatly categorized. Instead, 75% of the 60 million items that its 2 million merchants offer are handmade and therefore one of a kind. Even if they speak deeply to a shopper, they may do so for reasons that are difficult to divine from search terms and the information in product listings. "We don't have merchandisers entering the descriptions of the blue shirts in the pallets in the warehouse," says Mike Fisher, Etsy's CTO.
Data scientists are expected to know a lot -- machine learning, computer science, statistics, mathematics, data visualization, communication, and deep learning. Within those areas there are dozens of languages, frameworks, and technologies data scientists could learn. How should data scientists who want to be in demand by employers spend their learning budget? I scoured job listing websites to find which skills are most in demand for data scientists. I looked at general data science skills and at specific languages and tools separately.