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New data from the National Centers for Environmental Protection shows the Earth reaching its hottest temperature since record keeping began, fueling ongoing concerns about both human-induced global warming and the reemergence of El Nio.

Monday saw an average global temperature of 17.01 degrees Celsius (62.62 degrees Fahrenheit), according to an analysis by researchers from the University of Maine of data collected by the National Centers for Environmental Prediction.

Thats the hottest average global temperature ever recorded on any day of the year, according to the data analysis, beating the previous record of 16.92 degrees Celsius, which occurred on both July 24, 2022 and Aug. 14, 2016.

Experts have attributed this to a combination of human-induced climate change and the emergence of El Nio, a weather pattern that occurs every two to seven years due to wind patterns in the Pacific Ocean and is known for bringing increased temperatures worldwide.

The Intergovernmental Panel on Climate Change reports that because of human activity the global surface temperature of the Earth has increased 1.1 degrees Celsius during the period of 2011 to 2020 compared to the period of 1850 to 1900, which has caused increased wildfires, flooding and decreased food availability around the world.

The World Meteorological Association said it expects 2024 to look like 2016, the current hottest year on record, which was so warm because of a double whammy of the last time El Nio occurred and human-induced climate change, a dynamic it expects to play out again.

Though NCEP CFSR (data) only begins in 1979, other data sets let us look further back and conclude that this day was warmer than any point since instrumental measurements began, and probably for a long time before that as well, Robert Rohde, lead scientist for Berkeley Earth, a U.S. non-profit focused on environmental data science and analysis, wrote on Twitter Tuesday. Global warming is leading us into an unfamiliar world.

These rising temperatures have been felt acutely across the southern U.S. A dangerous heat wave has brought triple digit temperatures to a swath of the country spanning from Florida to Arizona for the past three weeks. Tens of millions of Americans were under an excessive heat warning from the National Weather Service Tuesday. In June, at least thirteen were killed by the heat in Texas, where some of the highest temperatures were seen, as well as two in Louisiana, according to the Associated Press.

El Nio Returns: UN Warns Of Upcoming Surge In Global Temperatures And Extreme Heat (Forbes)

July 4 Holiday Forecast: Expect Extreme Heat, Severe Storms In These Cities (Forbes)

New Orleans, Miami, San Antonio Break Heat Records: Heres Where Else Temperatures Are Hitting Record Levels (Forbes)

I am a Chicago-based breaking news reporter at Forbes. Prior to joining Forbes, I wrote for newspapers such as The Times of Northwest Indiana and The Washington Missourian. I also studied journalism at the University of Missouri. Follow me on Twitter @WillSkipworth or get in touch at wskipworth@forbes.com.

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July 3: Earth Experiences Hottest Day On Record - Forbes

The upcoming months of 2023 promise a wealth of learning and networking opportunities for data professionals, with an exciting array of analytics conferences scheduled across the United States. These events will host some of the most brilliant minds in the field of analytics and artificial intelligence (AI), providing a platform to discuss current trends, share valuable insights, and explore what the future holds for the industry.

Here are the top ten analytics conferences to attend from July to December 2023:

MachineCon 2023 (July 21, New York) An exclusive gathering for leaders in the world of analytics and AI, MachineCon explores the transformative potential of advanced AI technologies and innovative analytics solutions that are changing the face of various industries. Organised by AIM Media House, a leading global technology media firm, this conference celebrates those who have mastered the art of turning data into a competitive advantage.

DataConnect Conference (July 20-21, Columbus, OH) As reported by KDnuggets, the DataConnect Conference is a major event in the field of data analytics. It also offers virtual participation, making it accessible to a global audience.

The 2023 International Conference on Data Science (July 24-27, Las Vegas, NV) This conference emphasises the latest developments in data science, serving as a platform for researchers and practitioners to share their discoveries and insights.

Ai4 2023 (August 7-9, Las Vegas, NV) Ai4 2023 is an all-encompassing conference that covers a broad spectrum of topics related to AI and analytics, bringing together business leaders and data practitioners to facilitate the adoption of AI and machine learning technologies.

Chief Data & Analytics Officer (CDAO), Chicago 2023 (August 8-9, Chicago, IL) The Chicago edition of the CDAO conference is a significant event that unites Chief Data Officers and other analytics leaders to deliberate on strategies, trends, and challenges in the data analytics industry.

SAS Explore 2023 (September 11-14, Las Vegas, NV) SAS Explore is a prominent conference that focuses on analytics and data science. The event includes a wide range of sessions and workshops, providing an excellent opportunity for learning and networking.

Chief Data & Analytics Officer (CDAO), Government 2023 (September 19-20, Washington, DC) This iteration of the CDAO conference highlights the use of data analytics in the government sector. It provides a forum for discussion about how data and analytics can be used to enhance government services and operations.

ODSC West 2023 (October 31 November 3, San Francisco, CA) The Open Data Science Conference (ODSC) West is one of the worlds largest applied data science conferences. The event encompasses a wide range of topics, including AI, machine learning, data visualisation, and data engineering. The conference also features a virtual component, making it accessible to attendees worldwide.

Data Science Salon SF: Applying AI & ML in the Enterprise (November 29, San Francisco, CA) This conference focuses on the application of AI and machine learning in enterprise settings. It provides an opportunity for data science professionals to learn about the latest trends, techniques, and best practices in the industry.

Chief Data And Analytics Officers, APEX West (CDAO) (December 5, Arizona City, United States) The CDAO APEX West conference is a significant event for data and analytics officers. It provides an opportunity for these leaders to come together to discuss the latest trends, strategies, and challenges in the field of data analytics.

These ten conferences represent some of the most influential and anticipated events in the data analytics and AI industry for the second half of 2023. Whether youre a data scientist, AI practitioner, or business leader, these events offer a wealth of knowledge, networking opportunities, and a glimpse into the future of data-driven technologies. Be sure to mark your calendars and register in advance to secure your spot.

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Top 10 Analytics Conferences in the USA for the Second Half of 2023 - Analytics India Magazine

ATK New Delhi [India], June 28: In the dynamic field of Data Science, where the demand for skilled professionals is constantly growing, having a strong foundation in an industry-relevant curriculum is essential. Boston Institute of Analytics has solidified its reputation as a leading institution in the Data Science domain, with its course curriculum recently being ranked as the most industry-relevant curriculum by the prestigious Indian Analytics Forum (IAF). This recognition speaks volumes about the institute's commitment to providing students with a comprehensive and up-to-date curriculum that aligns with the evolving needs of the industry.

Catering to Industry Demands: The IAF's ranking of the Data Science course curriculum at Boston Institute of Analytics is a testament to the institute's ability to understand and cater to the demands of the industry. The curriculum is designed in collaboration with industry experts, renowned data scientists, and top organizations to ensure that it addresses the latest trends, technologies, and methodologies. By integrating practical skills, theoretical knowledge, and hands-on experiences, the curriculum prepares students to tackle real-world challenges effectively.

Holistic Coverage of Data Science Concepts: The curriculum at Boston Institute of Analytics offers a holistic coverage of Data Science concepts, encompassing both fundamental and advanced topics. It includes comprehensive modules on statistics, programming languages, data visualization, machine learning, natural language processing, and more. Students gain a deep understanding of the underlying principles and techniques that drive the field of Data Science. This broad-based approach equips them with the necessary skills to handle diverse data-related projects across industries.

Integration of Real-World Case Studies: To bridge the gap between theory and practice, the curriculum at Boston Institute of Analytics incorporates real-world case studies. These case studies expose students to actual industry challenges and provide them with hands-on experience in solving complex data problems. By working with authentic datasets and exploring various analytical methodologies, students gain valuable insights into the practical applications of Data Science. This integration of real-world scenarios ensures that graduates are well-prepared to tackle similar challenges in their professional careers.

Cutting-Edge Tools and Technologies: Boston Institute of Analytics understands the importance of equipping students with proficiency in the latest tools and technologies used in the industry. The curriculum includes dedicated modules on popular tools like Python, R, SQL, Tableau, Power BI, and more. Students learn to leverage these tools effectively for data analysis, visualization, and model building. This hands-on experience with cutting-edge technologies prepares students to handle real-world projects and enables them to remain competitive in the rapidly evolving Data Science landscape.

Industry Collaboration and Networking: The curriculum's industry relevance is further enhanced through Boston Institute of Analytics' strong collaboration with industry partners. The institute actively engages with leading organizations to understand their evolving needs and incorporate industry-specific knowledge and best practices into the curriculum. Additionally, students benefit from networking opportunities with industry professionals through guest lectures, workshops, and industry-driven events. This exposure provides students with valuable insights, enhances their industry awareness, and creates potential avenues for internships and job placements.

The recognition of the Data Science course curriculum at Boston Institute of Analytics as the most industry-relevant curriculum by the IAF underscores the institute's commitment to providing students with a top-notch education that aligns with industry demands. As a validation of their commitment to academic excellence, Boston Institute of Analytics (website: http://www.bostoninstituteofanalytics.org) was recently ranked as the best data science training institute in India by IFC. With leading data scientists from the industry as trainers, industry oriented curriculum and collaborations with 350+ hiring partners, Boston Institute of Analytics (BIA) secured the top spot to become the number one ranked data science and analytics training institute in India in the classroom training space. Recognized as industry's best data science and analytics training program by globally accredited organizations and top multi-national corporates, Boston Institute of Analytics classroom training programs have been training students and professionals on industry's most widely sought after skills, and make them job ready in data science, machine learning and artificial intelligence field.

Through a holistic coverage of concepts, integration of real-world case studies, focus on cutting-edge tools and technologies, and strong industry collaboration, the institute ensures that students are well-prepared to excel in their Data Science careers. By choosing to pursue the Data Science course at Boston Institute of Analytics, students can be confident in acquiring the skills and knowledge needed to make a significant impact in the data-driven world. (Disclaimer: The above press release has been provided by ATK. ANI will not be responsible in any way for the content of the same)

(This story has not been edited by Devdiscourse staff and is auto-generated from a syndicated feed.)

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Data Science course curriculum at Boston Institute of Analytics ranked as the most industry-relevant curriculum by IAF - Devdiscourse

As companies strive to become data-driven, and with the recent explosion of AI technology demanding ever-increasing amounts of training data, the quality of that data is becoming more important. And theres a great deal of time and money invested in data pipelines and other technical aspects of data quality such as data consistency, validity, timeliness, and audibility.

But theres one aspect of data quality thats equally, if not more, important, and is often overlooked in favor of problems that can be solved by technologythat of completeness, or bias.

The best way to address this issue is to have as diverse a data team as possible in terms of gender, ethnicity, age, national background, education, business expertise, and more.

Over the past few years, numerous studies have shown that companies that make data-based decisions make more money. Last year, for example, an IDC survey of over 600 companies showed that mature data practices result in a threefold increase in revenue improvement, almost triple the likelihood of reduced time to market for new products and services, and more than double the probability of enhanced customer satisfaction, profits, and operational efficiency.

And a March survey of business leaders by the Harvard Business Review and Google Cloud showed that data and AI leaders significantly outperformed other companies in operational efficiency, revenues, customer loyalty and retention, employee satisfaction, and IT cost predictability.

Executives are paying attention. A global survey released this spring by Salesforce, of nearly 10,000 business executives, showed that 80% say data is critical to decision-making at their organizations, and 73% say data helps reduce uncertainty and improve accuracy.

Further studies have shown that diversity also leads to better business performance, and that diverse teams are more innovative, make better decisions, and have higher retention. And most companies now understand the value of diversity and inclusion.

In a PwC report released this February, 85% of global companies had diversity, equity, and inclusion as a stated value or priority. Of those, 46% did so in order to attract and retain talent, 20% to achieve business results, 13% to enhance their reputations, and 11% to comply with regulatory requirements.

But few companies are able to live up to their diversity objectives, and data science is one of the worst sectors in this regard.

According to the latest Zippia numbers, only 20% of US data scientists are women. Only 7% are Hispanic, even though 19% of the US population is, and only 4% are African American, despite being 12% of the population.

Without a diverse team, youre less likely to be aware of different lived experiences, says Nika Kabiri, senior director of decision science at Clio, a legal services company.

And its not enough for executives to commit to hiring diverse teams, she adds.

They also need to create space for diverse voices, for individuals to comfortably share their diverse lived experiences in a way that deeply informs product development, she says. Otherwise, executives will only address bias in a superficial way and build products that fall short of what they could be.

This is particularly important today, with the advent of generative AI and large language models (LLM), says Sreekanth Menon, VP and global leader for AI and ML services at Genpact, who says LLMs have a reputation for biases and hallucinations. Its likely this is due to a concentration in the training data. For example, the models do better with English than other languages.

Having a diverse team from different geographies can help remediate such biases, he says. Similarly, diversity in ethnicity, gender, and other characteristics can help create more ethical frameworks for data onboarding, as well as bring in diversity of thought.

On his own team, for example, 20 to 30% come from a pure math or statistics background, he says. The rest come from other areas. I have a bioinformatics guy working for me, he says. That different background helps.

AI has the potential to amplify data bias problems, which could lead to deadly results, says Davi Ottenheimer, VP of digital trust and ethics at Inrupt, a company founded by Tim Berners-Lee to give users control of their data.

For example, he says, early image recognition systems would inhumanely misclassify Black faces, and some AI systems would label black hands as holding gunsbut not white handsdue to a diversity failure on the teams building the systems.

A lack of diversity on a team could get innocent people killed, he says.

Alison Alvarez, cofounder and CEO at BlastPoint, a data company serving financial institutions and utilities, adds: There are so many examples in engineering where the lack of a diverse team can lead to poor outcomes. Like when those sensors came out for people to wash their hands and they didnt recognize dark skin. They didnt have a diverse team building it, and they didnt have a diverse team actually testing it.

But there are more dimensions to diversity beyond just gender, race, or sexual orientation. Diversity can include someones national origin, or whether they have allergies or other health issues, Alvarez suggests.

Diversity can even include a persons rank in a company.

If you dont empower people on the lower level, their observations get downgraded, she says.

For example, the Challenger space shuttle disaster could have been prevented, since working engineers had warned about the reliability of the seals for two years, including on the eve of the launch itself. Its easy to miss things when theres only one set of eyes looking at data, says former Microsoft VP Gavriella Schuster. Today, shes a founding member of Women in Cloud and Women in Technology, advisory board member of the Women Business Collaborative, a board member at Nerdio and Mimecast, and a strategic advisor at Berkshire Partners.

A lot of times, people use data to validate their own assumptions and ignore data that doesnt validate those assumptions, she says. When you have enough eyes looking at a set of data, then you tend to avoid that phenomenon.

But where do you find those eyes?

Schuster recommends that companies look beyond people who, say, have 10 years of data science experience. If you were only looking for people with that level of experience, you tend not to get that diverse a pool of candidates.

Plus, data science is changing quickly, she says, and it could be a disadvantage not to have newer people on the team who might think about AI and data processes in different ways.

In fact, you might not even need a data scientist.

What you really want is people who have some experience in organizing information and thinking through patterns, she says. People with degrees in the biological sciences, or economics, might have the right mindset. There are continuing education programs where you can send someone to have them learn the specific technologies theyll use.

Other candidates could come from other areas of the company, or other departments that use products that the data science team builds. They understand user requirements and business value, and have needed domain expertise.

Discounting people who dont have a computer science background or information systems background really hurts a lot of CIOs, she says. Because then you miss people who understand the business, or understand the industry or the vertical, and can see different information that can be brought in. Ive seen that happen numerous times.

She also recommends having multiple diverse candidates to choose from. If youre looking to hire more women, have at least two women among the finalists.

Otherwise, if you have one person, the bias that people have will naturally come out targeted against that one person, she says.

She also recommends looking for candidates in different geographical regions, and to hire diverse talent, the interview panel itself needs to be diverse.

Finally, leaders looking for team members who have different backgrounds, and different points of view, need to look beyond their existing networks.

People tend to have people like them in their social network, she says. Unless you go outside who you know, you wont get diverse candidates.

Forrester analyst Kim Herrington has a tip for leaders looking to broaden their networks: go on LinkedIn, find five diverse professionals in the field you need tech talent from, and follow them.

Then challenge yourself to do this again as often as possible, following the followers until your feeds are a garden of diverse and brilliant voices, she says.

One place to start is The Algorithmic Justice League on LinkedIn, she says. On the people tab, not only will you find folks of diverse backgrounds, but theyll be smart, passionate, and driven to help you and your teams be more mindful of technology and its pitfalls.

Despite the means to find people and skills shortages headlines, she does hear a lot of companies complaining that they cant find anyone.

When I hear this, I believe you, she says. But then Ive just learned an awful lot about you, your network, your outsized expectations, and your potentially outdated HR systems and policies. Theres no excuse for not having diverse people in your bubbles in 2023 and beyond.

Herringtons top advice for CIOs is to put your metrics where your mouth is.

Thats my personal advice for CIOs and CDOs looking to improve data initiatives and quality, she says. To do this, CIOs can work with fellow data and analytics leaders to ask, How might we as it pertains to measuring and communicating diversity of data teams, retention of diverse employees, number of diverse employees in data roles, candidate diversity demographics, promotion rates, inclusion and belonging levels, pay levels, diversity of leadership, and employee engagement levels.

One way to begin is to start with data that an organization is already gathering, she says. For example, an organization might gather demographic data for its customer base or the locations it primarily serves. Then compare your EEOC [employee] data to see where dissonance exists when viewing percentages, she says.

According to Glassdoors 2023 workplace trends report, 74% of US workers say corporate investment in diversity, equity, and inclusion is very important or somewhat important to them when considering a new job. Young people were particularly interested in diversity, with 72% of workers under 35 saying theyd consider turning down a job offer, or quitting a company, if they didnt think management supported diversity initiatives. And two-thirds would also turn down a job from a company with gender and racial imbalances in its leadership.

One thing I come across in my research is that diversity on teams actually leads to all kinds of improvement in talent attraction, says Gartner analyst Jorgen Heizenberg. And teams with different backgrounds are more successful and more creative, which ultimately leads to higher retention.

One significant benefit to getting diverse voices on a data science team is that there are more opportunities to look beyond purely technical solutions to problems.

Data and AI are very populated with people with the same background, the same education, and dominated by a technology-centric approach, says Heizenberg.

Thats why data teams spend the majority of their budget, time, and people on technology such as data management, data governance, and advanced analytics.

But the primary accelerator and predictor of success is the establishment of a data-driven culture.

Its funny that the number-one thing is often overlooked, and they spend much more time on governance, tools, and technology, he says. And, to a large extent, thats the result of having the same kinds of people with the same kinds of backgrounds and experience, and it becomes very siloed.

According to the Gartner survey, cultural challenges to accepting change are the third-biggest roadblock to success, alongside lack of business shareholder support, after lack of staff and lack of funding.

What Im telling clients is when they work on data and analytics, they need to balance out the technology-centered approaches with more human-centered approaches, says Heizenberg, and do so by building cross-functional and multidisciplinary teams.

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How diverse teams lead to better data - CIO

LAKE TAHOE, Calif. Forests are naturally resilient to the threat of wildfires, invasive species, and disease outbreaks. However, climate change is increasing the severity and frequency of these disturbances, exacerbating disease and insect outbreaks, and putting forest regeneration at risk. Seed collection from wild, native trees is essential to replanting and reforestation.

Adventure Scientists Reforestation: Western U.S. project sends volunteers into national forests to survey conifer species for cone production. Their partner, Mast Reforestation, is seeking location and cone abundance data to inform their follow-up conifer seed collection and reforestation efforts. Mast Reforestation aims to build an accessible seedbank for the conifer forests of the Western U.S. suited for the anticipated seed migration needs of our changing climate.

Due to the limited monitoring season, Adventure Scientists are actively recruiting volunteers in California to venture into the following National Forests: Stanislaus, Eldorado, Sierra, Plumas, Tahoe, Inyo, Sequoia, Lassen, and Modoc.

This project offers flexible weekend or evening opportunities to collect data and only requires a pair of binoculars and a smartphone. Volunteers on the project will gain observation skills and applied experience in the field of natural science. The website offers more information on the project and how to sign up: Reforestation: Western U.S.

I have volunteered for Adventure Scientists on numerous projects, and I really enjoy being able to give back while also being outdoors, said Pam Hoult, from the Bay area and a current volunteer with the Reforestation: Western US project. The Reforestation project is fun and easy, and working with Adventure Scientists gives us the impetus to explore new places as well as our tried and trusted favorites.

Adventure Scientists is a Montana-based nonprofit that mobilizes the outdoor community to collect data for conservation research. Learn more at adventurescientists.org.

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Hike and help forests thrive; nonprofit asks citizen scientist help ... - Tahoe Daily Tribune