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Overcoming the tech skills confidence gap – Chief Learning Officer

The growing use of technology across job functions is designed to streamline processes, boost productivity, improve customer service and reduce costs. Unfortunately, it can also discourage job seekers from applying for positions because of concerns that they lack the right tech skills.

And its not just in tech-specific roles. Today, marketers are expected to have experience in coding, data analytics and UX design; financial services roles increasingly require experience with programming languages such as Python and MATLAB; and even health care professionals benefit when they have a better understanding of Epic, the leading electronic health record system in the U.S.

To prepare the workforce for the jobs of the future and reduce the tech skills confidence gap, companies must play a greater role helping educate emerging talent in schools. This will provide more people with opportunities to build their tech competency, giving them the confidence to pursue careers they might otherwise find out of reach. Even more importantly, it will help build the tech-ready workforce the country desperately needs.

Public-private partnerships can build a strong tech foundation

While many think younger Americans are digital natives with tech skills that go far beyond social media, research shows this often isnt the case.

A Dell study found over a third of Gen Z felt that their school education did not prepare them with the technology skills needed for their planned career and 56 percent received either very basic or no digital skills training. When it comes to the tech workforce specifically, a CompTIA study of 18-34 year-olds revealed a confidence gap that is discouraging them from pursuing tech careers.

In the U.S, K-12 public schools often have limited resources and time to go beyond the basic curriculum of English Language Arts and math, especially in the elementary grades. This is where leading businesses can help fill the tech skills gap.

While companies may not be able to directly influence whats taught in the classroom, there are opportunities to work with school districts to offer access to tech experts, lessons, materials and other resources such as financial assistance.

The following ideas are thought starters to help companies develop programs that work best for their business and community.

Elementary School (Grades K-6)

In the elementary grades, educators often focus on teaching technology basics such as search techniques, password management and digital citizenship. These are all important skills, but theres an opportunity to go further as emerging tech becomes more user-friendly and accessible to everyone.

Coding, artificial intelligence and augmented reality have become more mainstream, making it easier for educators to not only incorporate these disciplines into lesson plans, but teach them to their students.

Corporate learning and development can play a pivotal role by partnering with school districts to provide professional development to teachers so they can work with students each day, offering to run after-school coding classes to reinforce students learning and planning fun tech competitions to give students an opportunity to exhibit what skills they learned.

Because even the youngest students ask why they need to know certain skills, business and learning leaders can also help teachers make the connection between the tech being used in the classroom and whats happening in the real world using their companies as examples.

Finally, theres an opportunity to provide administrators and teachers with informational sessions on everything from how to stay safe from cyber security threats to responsible use of AI chatbots like ChatGPT. As technology rapidly advances, these early introductions in students education can give them a significant head start toward rewarding careers.

Secondary School (Grades 7-12)

Across the U.S., 53 percent of public high schools offer foundational computer science classes, but participation can be unequal, especially among underrepresented groups such as girls and economically disadvantaged students.

One issue is that even young people dont understand that coding programs have become more accessible and intuitive, and that coding skills can be put to many uses from building websites and apps to creating digital art. Another issue is that districts may not advocate coding programs because they dont have enough funds and staff, or administrators dont see the value.

Theres an opportunity for companies to step in to help educate students, parents, teachers and administrators about the computer science field to help overcome these obstacles. They can also connect students with computer science professionals through mentor programs, tech fairs, school assembly presentations and on-site company visits.

In fact, a Gallup report found that students with computer science role models are over 10x more likely to say they will pursue a computer science career than students without.

The journey needs to continue in higher ed

Its not just young job seekers who have a lack of confidence when it comes to workforce preparation. Employers also feel the next generation isnt ready.

While most organizations value a college degree, they also express that higher education institutions need to make more progress in getting students ready for the corporate world, including developing better tech and soft skills.

Students are eager and see the value as well. According to a Cengage study, 66 percent of college graduates want more real world work experiences and believe colleges should prioritize school-sponsored co-ops and internships as well as mentorships and introductions to local businesses.

But the onus shouldnt fall solely on colleges and universities to provide these opportunities. Instead, business and learning leaders can take a few steps to help build better partnerships in higher education by:

Theres a long way to go before the tech skills confidence gap is a thing of the past, and the route to get there can seem overwhelming. But inaction will only allow the problem to grow as tech increasingly becomes more advanced and pervasive in our workplaces.

If corporations want to have a tech-savvy future workforce, theyll need to fully collaborate with educators now to build a solid tech background that starts early in a childs life and continues throughout their educational journey and beyond.

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The Role of Data Mining in Healthcare & Why it Matters: A Brief – Solutions Review

Solutions Reviews Tim King offers a brief on the topic of data mining in healthcare, what it means in practical terms, and why its important.

Data mining plays a crucial role in healthcare by enabling the extraction of valuable insights and knowledge from large and complex healthcare datasets. It involves applying advanced analytical techniques and algorithms to uncover patterns, relationships, and trends within healthcare data. The overall role of data mining in healthcare can be summarized as follows:

Overall, data mining in healthcare plays a vital role in enhancing clinical decision-making, improving patient care, enabling early disease detection, optimizing resource allocation, and advancing medical research. It has the potential to transform healthcare systems by leveraging the power of data to drive evidence-based practices, improve efficiency, and ultimately improve patient outcomes.

The role of data mining in healthcare is significant due to several key reasons:

In summary, the role of data mining in healthcare is vital as it enhances patient outcomes, supports evidence-based medicine, optimizes resource allocation, facilitates early disease detection, combats healthcare fraud, advances medical research, and promotes data-driven decision-making. By leveraging the wealth of healthcare data, data mining empowers healthcare systems to provide more efficient, effective, and personalized care while improving population health.

Tim is Solutions Review's Executive Editor and leads coverage on data management and analytics. A 2017 and 2018 Most Influential Business Journalist and 2021 "Who's Who" in Data Management, Tim is a recognized industry thought leader and changemaker. Story? Reach him via email at tking@solutionsreview dot com.

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Major ocean database that will guide deep-sea mining has flaws … – Nature.com

Researchers have discovered a treasure trove of arthropods, such as these, on the sea floor in the ClarionClipperton Zone, located in the central and eastern Pacific Ocean.Credit: SMARTEX Project, Natural Environment Research Council, UK (smartexccz.org)

A company is expected to request authorization in July, for the first time ever, to mine the ocean floor for metals such as cobalt and nickel. At the same time, researchers warn that a crucial database that maps deep-sea biodiversity and that could factor into the decision to approve such a licence contains errors and data gaps.

Seabed mining is coming bringing mineral riches and fears of epic extinctions

The International Seabed Authority (ISA), a body associated with the United Nations that oversees deep-sea mining in international waters, currently allows only mining exploration. According to its website, it has approved 17 companies and government entities to study the mining potential of the ClarionClipperton Zone (CCZ), a region of the sea floor that spans up to 6 million square kilometres of the central and eastern Pacific Ocean and that holds metal-rich clumps of sediment. Nauru Ocean Resources, a subsidiary of The Metals Company, based in Vancouver, Canada, has been exploring the sea bed, with an eye towards gathering metals needed for electric-vehicle batteries and other electronics. It plans to apply for a commercial mining licence in a month or so. If approved, operations could begin in 2024.

Scientists worry about allowing companies to start mining the sea bed because little is known about deep-sea habitats and biodiversity, so its environmental effects are unpredictable.

The ISA runs a database called DeepData, which is meant to tackle some of these concerns, as well as to enable research projects. The database contains information that the ISA requires contractors to collect during their deep-sea exploration missions. These biological, geochemical and physical data include, for example, the species that they encounter and the chemicals present in the water.

But the analysis of DeepData, published in the journal Database on 30 March1, revealed flaws that worry the researchers who conducted the study.

Contractors would like to mine the sea bed for metal-rich clumps of sediment called polymetallic nodules.Credit: Courtesy of the NOAA Office of Ocean Exploration and Research, 2019 Southeastern U.S. Deep-sea Exploration.

It strikes me as irresponsible to be relying on the database in its current form to assess the impact of mining on the sea-floor environment, says Muriel Rabone, a data scientist at the Natural History Museum in London, who led the analysis. Rabone told Nature that the analysis was performed independently of the ISA, but that the agency cooperated to enable data access. It was also consulted on the scope of the study and an early draft of the manuscript.

The ISA protests some of the findings, however, saying that the report is out of date. On 12 July 2021, the researchers downloaded data collected in the CCZ to run their analysis. Since then, the ISA has made significant improvements to address quality assurance and control issues with DeepData, it says.

Responding to this criticism, Rabone maintains that the database still contains flaws. Even with its faults, its helping to point to thousands of species on the sea floor that had never been seen before results published just this week. There is work to do yet, she says.

Of the 40,518 records that the researchers analysed for the Database study, about one-quarter were duplicates, which could lead to an underestimation of species richness in the deep sea, they say. The scientists think duplicates can arise partially because the database lacks unique codes to identify individual records.

The ISA says that, like any database, DeepDatas features and the quality of its data are improving with the years due to technological advances. It adds that it has identified and corrected duplicate records. Also, it is collaborating with the World Register of Marine Species, which catalogues and classifies marine organisms, and is sharing data with the Ocean Biodiversity Information System a data hub that has helped to clean up the data and make them more widely available.

Brisingid sea stars, like this one, live on the sea floor in areas rich with metals.Credit: Courtesy of the NOAA Office of Ocean Exploration and Research, 2019 Southeastern U.S. Deep-sea Exploration

Looking at the database today, however, Rabone says that some duplicate data still exist, and that many records still do not have a unique identifier.

The team also found that DeepData contained inconsistent information for instance, records that catalogued two species under the same name. And a lot of environmental data were missing. When contractors submit their data, they use a form with fields such as species name and fauna class size. The researchers found that 90% of the total data in various fields were missing.

The ISA says it has already updated its forms to address some of these issues and is designing workshops and training for contractors to ensure that data quality and control are improved.

Scientists track damage from controversial deep-sea mining method

Rabone would like the workshops to be open to the scientific community, which she says can provide feedback on the database. Stefanie Kaiser, a deep-sea ecologist at Senckenberg Research Institute in Frankfurt, Germany, who was not involved with the study, agrees, and says that, if the database were improved, it could be useful for researchers, giving them access to all the information collected by the contractors.

But the ISA says workshops are for only contractors, because they provide the data, although it acknowledges that the academic community has assisted contractors with presentations and preparing annual reports.

Despite the disagreements over DeepData, researchers are already learning from the database. Rabone formed an official partnership with the ISA to lead the first census of metazoan biodiversity on the CCZs sea floor. The endeavour found more than 5,500 species in the region, of which 92% are new to science, including many worms and arthropods. The findings were published on 25 May in the journal Current Biology2.

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Artificial intelligence terms professionals need to know – Thomson Reuters

Artificial Intelligence (AI) has exploded in the public mind over the last year, with many examples of AI-generated text, artwork and even full video. The talk of AI replacing entire industries is back at the front of the conversation with lawyers and accountants often mentioned as vulnerable.

Artificial intelligence is not going to replace experienced professionals right now, but people who can use AI effectively will quickly outpace those who cant. AI is changing how many people work and its here to stay. Your clients will need your AI insights, and your organization may need your AI knowledge.

So, how do you make sure it doesnt leave you behind?

You may find that you actually know more about AI than you thought you did that youve been using AI, every day, for many years. This article will explain the most common AI terminology you need to know to engage in the AI conversation in your workplace.

Artificial intelligence, or AI for short, is broadly defined as a branch of computer science that aims to develop intelligent machines, which can perform tasks that typically require human intelligence. This includes learning, problem-solving, decision making, etc. Examples of AI from everyday life range from maps and navigation to text editors and autocorrect to chatbots and digital assistants, like Siri.

An algorithm is a set of rules to be followed in calculations or other problem-solving operations, especially by a computer. In a math equation, an algorithm is the method we use to solve long division problems. A search engine, like Google, uses algorithms to find the most relevant information for the searcher.

Machine learning (ML) is a subset of AI that can learn without following explicit instructions by inferring patterns in data using statistical models and algorithms. Examples of ML include social media feeds, product recommendations, and image recognition.

Natural language processing (NLP) focuses on generating human language both spoken and written not robotic speech or restrictive text. Natural language processing applies algorithms to extract and analyze language data in a way that computers can process.It is imperative for machines to be able to process enormous amounts of data to be able to mine it and organize it and ultimately, to translate it and output human-seeming content.

Natural language search (NLS) is a type of search method that allows users to interact with a computer system or search engine using everyday language instead of formalized search queries or specific commands.Natural language search means that when youre searching for a new gym by searching for gym, your results will include most places that are focused on fitness, regardless of whether the name of the business actually includes the word gym. From a traditional gym, a CrossFit or yoga studio, Google understands that gym and studio in this instance have a similar meaning.

Your search inquiry doesnt have to be all inclusive (gym and studio and fitness and yoga and CrossFit and health and club) to get all-inclusive results. You get to type like a human, not a robot. And with a little help from machine learning, its going to keep your results local.

As a professional, it also means that when youre searching through research documents and briefs, you dont always have to use exact-match language to find precisely what youre looking for.

Data mining is the process of looking for relationships, correlations, and patterns within large data sets. Technology systems scour data and recognize anomalies within the data at a scale that would be impossible for humans. This analysis helps predict outcomes, finds potential wrongdoings, and notices questionable trends, and that information derived can be useful in a variety of ways.

Your recommendations (hopefully) keep getting better.By analyzing the patterns of people who also buy or are interested in the same products as you, a store can make relevant suggestions based on that data. This same concept plays out in Netflix recommendations or targeted advertisements online.

To put it simply,structureddata isorganizeddata, defined within a particular structure. It may be referred to as quantitative data. It is objective and easy to export to and store in Microsoft Excel or a larger database. The way it is organized is consistent and easily identifiable, which makes data mining better. Structured data is also less complicated to analyze and distill.

On the other hand, unstructured data isnt organized. It has no externally defined structure, and cannot be easily exported, stored, or organized. And its the bulk of what most organizations deal with daily. It includes most text-heavy data, such as reports, Microsoft Word documents, emails, and webpages.

Structured data has made it easy for you to complete searches and inquiries for decades.And because the data is organized and objective, you can be sure that the results you are shown are the most accurate.

For example, transactional data from a sales report e.g. Rep X sold Y units of product Z, for a total revenue this year of $$ is structured data, and easily analyzed. But that same reps detailed listing of feedback from new users of the product during implementation is unstructured, and has traditionally been difficult to examine, analyze, and quantify.

Recent advances in AI, such as Large Language Models and Foundation Models, have largely been about using vast repositories of unstructured data in new ways. See the companion article on machine learning for more details.

Big data refers to the data sets that are too large or complex to be handled by traditional data-processing software. Big data is a combination of structured, semi-structured, and unstructured data. Examples of big data include customer databases, all the information posted on a social media site, or trade data from the New York Stock Exchange.

Everything discussed above is generally well-understood and has been in use in many professional products for years decades in some cases. Remember the first time you typed a plain-language search to find what you needed? That was a type of AI. You should be relatively comfortable with these AI concepts, since youve been using them for years in your daily life, probably without even realizing.

But AI is not standing still. The consumer versions of Large Language Models have been all over the news, specifically ChatGPT and other chatbots. These AI technologies have the potential to disrupt the work of all professionals and you should be in the know. To stay on top of recent AI developments, visit our hub on artificial intelligence and read our companion article on generative AI and chat bots to learn more.

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Top in-demand information technology jobs during hiring freezes – Times of India

In the face of challenging job markets and hiring freezes induced by the Covid-19 pandemic, the tech industry has demonstrated resilience and continued growth. Despite economic downturns, the demand for certain tech jobs remains high, as companies recognize the need for digital transformation, enhanced security measures, and innovation.

Despite the temporary slowdown in recruitment, the need for skilled tech talent persists, underpinned by the long-term vision of companies looking to position themselves for success in the post-pandemic era. As remote work becomes more prevalent and industries strive to meet the demands of a digital-first world, certain tech jobs have not only remained stable but have experienced increased demand.

Take a look into the top tech jobs that are in demand during the freezing hiring-

Cybersecurity professionals:

The increasing threat of cyber-attacks and the shift to remote work arrangements have elevated the demand for cybersecurity professionals. According to a study, the global cybercrime costs are projected to reach $10.5 trillion annually by 2025. This staggering statistic underscores the critical need for skilled cybersecurity analysts, ethical hackers, and information security managers. Organizations across industries are actively seeking experts to safeguard their sensitive data, protect their networks, and mitigate potential risks.

Data scientists and analysts:

In an era driven by data, the demand for data scientists and analysts has continued to soar. According to the U.S. Bureau of Labor Statistics, the employment of data scientists is projected to grow by 31% from 2019 to 2029, much faster than the average for all occupations. The ability to analyze vast amounts of data and derive actionable insights has become a strategic advantage for businesses. Data experts proficient in data mining, machine learning, and statistical analysis are instrumental in helping organizations make informed decisions and gain a competitive edge in the market.

Software developers and engineers:

Software developers and engineers play a crucial role in advancing technology and meeting evolving customer needs. Despite hiring freezes, the demand for these professionals remains strong. The U.S. Bureau of Labor Statistics projects a 22% increase in employment for software developers from 2019 to 2029, much faster than the average for all occupations. As organizations adapt to the digital landscape, there is an ongoing need for skilled developers to create new applications, maintain existing systems, and improve user interfaces. Additionally, expertise in cloud platforms has become increasingly valuable with the rise of cloud-based solutions.

AI and machine learning specialists:

Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized various industries, driving demand for experts in these fields. The World Economic Forum predicts that AI will create 12 million new jobs by 2025. Companies are actively seeking AI and ML specialists to develop algorithms, build predictive models, and automate processes. These professionals are instrumental in driving innovation, improving operational efficiency, and enhancing customer experiences.

IT project managers:

Effective project management is crucial for the successful implementation of technology initiatives. According to the Project Management Institute, organizations waste $122 million for every $1 billion invested in projects due to poor project performance. Skilled IT project managers are in high demand, even during hiring freezes, as companies strive to ensure smooth project execution, manage resources efficiently, and meet deadlines. Project managers with expertise in Agile or Scrum methodologies are particularly sought after, as these frameworks enable flexibility and adaptability in uncertain times.

Conclusion

Despite the economic challenges caused by hiring freeze, the tech industry continues to offer numerous in-demand job opportunities. Cybersecurity professionals, data scientists and analysts, software developers and engineers, AI and ML specialists, and IT project managers are essential to drive innovation, enhance security measures, and ensure successful digital transformations. As you navigate the tech job market, consider acquiring or enhancing your skills in these high-demand areas to increase your employ.

Views expressed above are the author's own.

END OF ARTICLE

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Boeing working to absorb lessons from MAX crashes, improve safety – Lewiston Morning Tribune

SEATTLE As Boeing tries to emerge from the four-year shadow of two deadly 737 MAX crashes, executives on Tuesday described diverse efforts to improve its safety culture and avert future airplane accidents.

The companys Chief Aerospace Safety Officer Mike Delaney outlined progress toward sweeping reforms in how Boeing operates and how it supports airlines.

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Boeing working to absorb lessons from MAX crashes, improve safety - Lewiston Morning Tribune

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Want to become the Head of Analytics? Here are the must-have skill sets – The Economic Times

As data becomes an integral tool for transformation and differentiation for companies across various sectors and sizes, the role of the Head of Analytics/Chief Data Science Officer becomes a critical one. Earlier, analytics used to operate at a functional level. Today, in many cases, it is a strategic imperative. And it starts right with the Head of Analytics/Chief Data Officer roles.Krishna Kumar, Founder and CEO, Simplilearn, says that the skills needed for the role include communication and technical expertise. He lists four areas of focus:Technical proficiency: Strong understanding of data science and analytics techniques, including statistical analysis, data visualisation, machine learning, and data mining. Proficiency in programming languages such as Python, R, or SQL is often required Leadership and management: Ability to lead and manage a team of data scientists or analysts effectively. This includes setting goals, assigning tasks, providing guidance and fostering a collaborative and innovative work environment Strategic thinking: Capability to align data science and analytics initiatives with the organisation's overall goals and strategic vision. This involves identifying opportunities where data can drive business value and formulating data-driven strategies

Communication skills: Excellent communication skills to effectively convey complex technical concepts to both technical and non-technical stakeholders. This includes presenting insights, findings and recommendations in a clear and concise manner

Ability to weave a compelling narrative: Must be a great storyteller, be able to confidently and articulately weave a data narrative in an insightful manner that every key stakeholder, executive and senior leader will be able understand the usability of analytics teams existence or impact on mission-critical goals and business outcomes

Data governance and compliance: An understanding of data governance frameworks, privacy regulations and ethical considerations related to data handling and analysis is increasingly important. The Head of Analytics should be knowledgeable about data protection best practices and ensure compliance with relevant laws and regulations

Analytical thinking: The ability to approach complex business problems analytically, break them down into manageable components, and identify key insights and trends from large data sets. Strong critical thinking and problem-solving skills are essential

Business acumen: Understanding the organisations goals, strategy and industry landscape is essential to align analytics initiatives with business objectives. The Head of Analytics should possess the ability to translate data insights into actionable recommendations for the organisation's growth and decision-making processes. This could result in an analytics strategy and road map for analytics capabilities, prioritise initiatives, and allocate resources effectively

Continuous learning: Given the rapidly evolving field of analytics, a Head of Analytics should have a mindset of continuous learning. Staying updated with the latest advancements, industry trends and emerging technologies is crucial for keeping the analytics function relevant and effective

Kumar of Simplilearn further breaks down some of the technical skill requirements:Programming skills: Proficiency in programming languages such as Python or R, SQL are crucial for data manipulation, analysis and modelling. Strong programming skills enable you to handle large datasets, implement algorithms and automate tasks efficiently.

Statistical analysis: A solid understanding of statistical concepts and methods is essential for interpreting data, drawing meaningful conclusions and making accurate predictions. This includes knowledge of hypothesis testing, regression analysis, probability theory and experimental design.

Machine learning: Familiarity with machine learning techniques is highly valuable. This includes both supervised and unsupervised learning algorithms such as linear regression, decision trees, random forests, support vector machines, clustering and neural networks. Practical experience in applying these algorithms to real-world datasets is important.

As technology and data continue to lead front and centre in the upcoming decades, the role of the Head of Analytics/Chief Data Office will encompass multiple requirements from ethics and privacy to data and business impact.

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Want to become the Head of Analytics? Here are the must-have skill sets - The Economic Times

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Visualizing the Uranium Mining Industry in 3 Charts – Visual Capitalist

When uranium was discovered in 1789 by Martin Heinrich Klaproth, its likely the German chemist didnt know how important the element would become to human life.

Used minimally in glazing and ceramics, uranium was originally mined as a byproduct of producing radium until the late 1930s. However, the discovery of nuclear fission, and the potential promise of nuclear power, changed everything.

Whats the current state of the uranium mining industry? This series of charts from Truman Du highlights production and the use of uranium using 2021 data from the World Nuclear Association (WNA) and Our World in Data.

Most of the worlds biggest uranium suppliers are based in countries with the largest uranium deposits, like Australia, Kazakhstan, and Canada.

The largest of these companies is Kazatomprom, a Kazakhstani state-owned company that produced 25% of the worlds new uranium supply in 2021.

As seen in the above chart, 94% of the roughly 48,000 tonnes of uranium mined globally in 2021 came from just 13 companies.

Frances Orano, another state-owned company, was the worlds second largest producer of uranium at 4,541 tonnes.

Companies rounding out the top five all had similar uranium production numbers to Orano, each contributing around 9% of the global total. Those include Uranium One from Russia, Cameco from Canada, and CGN in China.

The majority of uranium deposits around the world are found in 16 countries with Australia, Kazakhstan, and Canada accounting for for nearly 40% of recoverable uranium reserves.

But having large reserves doesnt necessarily translate to uranium production numbers. For example, though Australia has the biggest single deposit of uranium (Olympic Dam) and the largest reserves overall, the country ranks fourth in uranium supplied, coming in at 9%.

Here are the top 10 uranium mines in the world, accounting for 53% of the worlds supply.

Of the largest mines in the world, four are found in Kazakhstan. Altogether, uranium mined in Kazakhstan accounted for 45% of the worlds uranium supply in 2021.

Namibia, which has two of the five largest uranium mines in operation, is the second largest supplier of uranium by country, at 12%, followed by Canada at 10%.

Interestingly, the owners of these mines are not necessarily local. For example, Frances Orano operates mines in Canada and Niger. Russias Uranium One operates mines in Kazakhstan, the U.S., and Tanzania. Chinas CGN owns mines in Namibia.

And despite the African continent holding a sizable amount of uranium reserves, no African company placed in the top 10 biggest companies by production. Sopamin from Niger was the highest ranked at #12 with 809 tonnes mined.

Uranium mining has changed drastically since the first few nuclear power plants came online in the 1950s.

For 30 years, uranium production grew steadily due to both increasing demand for nuclear energy and expanding nuclear arsenals, eventually peaking at 69,692 tonnes mined in 1980 at the height of the Cold War.

Nuclear energy production (measured in terawatt-hours) also rose consistently until the 21st century, peaking in 2001 when it contributed nearly 7% to the worlds energy supply. But in the years following, it started to drop and flatline.

By 2021, nuclear energy had fallen to 4.3% of global energy production. Several nuclear accidentsChernobyl, Three Mile Island, and Fukushimacontributed to turning sentiment against nuclear energy.

More recently, a return to nuclear energy has gained some support as countries push for transitions to cleaner energy, since nuclear power generates no direct carbon emissions.

Nuclear remains one of the least harmful sources of energy, and some countries are pursuing advancements in nuclear tech to fight climate change.

Small, modular nuclear reactors are one of the current proposed solutions to both bring down costs and reduce construction time of nuclear power plants. The benefits include smaller capital investments and location flexibility by trading off energy generation capacity.

With countries having to deal with aging nuclear reactors and climate change at the same time, replacements need to be considered. Will they come in the form of new nuclear power and uranium mining, or alternative sources of energy?

This article was published as a part of Visual Capitalist's Creator Program, which features data-driven visuals from some of our favorite Creators around the world.

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Visualizing the Uranium Mining Industry in 3 Charts - Visual Capitalist

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Africans Are Pioneering The Bright, Yet Complicated, Green Future Of Bitcoin Mining – Forbes

Produce (Pty) Ltd. farm in Groenfontein, South Africa, on Wednesday, Aug. 24, 2022. Photographer: Guillem Sartorio/Bloomberg 2022 Bloomberg Finance LP

Sometimes, starting fresh with custom solutions can be far more efficient than patching and updating older systems. That's why Africa's underdeveloped electrical infrastructure offers a lucrative opportunity for bitcoin miners using renewable energy and off-grid technologies.

Bitcoin miningthe process that appends transactions to the bitcoin blockchain and secures the overall networkcan offer a way to scale energy storage and demand in lockstep with growing communities. In short, it's easy to turn bitcoin mining hardware on and off to suit demand.

Despite widespread concerns about the prospect of environmental damage caused by bitcoin mining's carbon footprint, industry studies reveal that bitcoin mining may be one of the world's most sustainable tech industry sectors. For example, Q4 2022 data published by the global consortium Bitcoin Mining Council indicated that 58.9% of the global energy consumption associated with bitcoin mining comes from renewables. In my home country of Nigeria, the bitcoin industry offers unique ways to tackle urbanization issues with homegrown solutions.

These solutions are less likely to rely on legacy electrical grids than any North American counterparts. Instead, many hydropower bitcoin miners provide an always-on-demand buyer of first and last resort for energy projects in developing areas.

More than 500 million people in Africa currently lack reliable electricity access, according to the International Energy Agency. As such, one of Africa's most effective bitcoin mining strategies is to build micro grids powered by renewable energy sources in rural communities beyond the reach of main power grids.

This symbiosis between bitcoin miners and remote communities attracts hobbyists and companies that both see vast growth potential for the bitcoin industry across Africa.

Gridless Mining Facility in Kenya

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For example, beyond Nigeria's rural hobbyists, the Kenyan bitcoin mining company Gridless uses similar hydro-powered micro grids (under 1 megawatt capacity) to provide electricity to three rural communities in East Africa. The company raised $2 million in seed funding led by Jack Dorsey's Block and the bitcoin venture capital firm Stillmark earlier this year to expand operations to other rural communities across Kenya.

In an effort to standardize a common approach to sustainable bitcoin mining and encourage collaboration across industry players, Gridless launched the Green Africa Mining Alliance (GAMA) with three other companies, Sukuma Ventures from Kenya, Trojan Mining from Nigeria, and QRB Labs from Ethiopia. Earlier this month, they released the "Blueprint for Bitcoin Mining and Energy in Africa" report with actionable insights for "reducing the electricity-access gap in underprivileged regions" using small, custom grids and bitcoin data centers.

Although bitcoin-savvy entrepreneurs and investors see the lucrative potential for eco-friendly bitcoin mining sites across Africa, opaque regulations still present various challenges.

Many African bitcoin hobbyists and miners prefer to remain anonymous rather than join public-facing corporate ventures like GAMA for fear of government backlash. African governments have neither explicitly forbidden bitcoin mining nor offered clear bitcoin mining regulations. Therefore, some off-grid bitcoin miners see that uncertainty as not worth the risk of drawing attention to themselves.

Nonetheless, those who operate relatively large bitcoin mining operations struggle to receive energy development licenses, not to mention the high cost of importing hardware equipment for bitcoin mining. Regardless of the regulation that may arise as the bitcoin mining industry becomes a more prevalent part of African economies, it's clear that we've only scratched the surface of what is possible when African communities develop their infrastructure solutions.

On the whole, bitcoin usage beyond crypto exchange platforms remains a grassroots movement across Africa. For this reason, organizations like GAMA take a long-term approach to growth rather than merely rushing to replicate bitcoin mining models already popularized in Asia, Europe, and the Americas.

While challenges still need to be addressed and questions answered, such as regulatory requirements for bitcoin-powered companies and the steep hardware costs, Africans are already pioneering the future of sustainable bitcoin mining methods and systems.

I'm a Nigerian Bitcoin Core contributor, CEO of the Bitcoin venture capital firm Recursive Capital, as well as the co-founder of Qala, a bitcoin education program designed to train the next generation of African Bitcoin developers. I also serve as a board member of trust, a nonprofit focused on growing the Bitcoin ecosystem in Africa.

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Africans Are Pioneering The Bright, Yet Complicated, Green Future Of Bitcoin Mining - Forbes

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