by jsendak | Apr 24, 2025 | Art
Make Earth Day Every Day
April 22nd is celebrated worldwide as Earth Day, a time dedicated to raising awareness about the pressing environmental issues we face and promoting sustainable practices. On this day, people gather to reflect on the state of our planet and take action towards a more eco-friendly future. However, the importance of caring for our environment goes far beyond a single day of the year. It is essential that we embrace the message of Earth Day every day, not only in our personal lives but also in our communities and globally.
Throughout history, the world has witnessed numerous environmental crises that have shaped our understanding of the impact human activities have on the Earth. From the industrial revolution to the present day, the consequences of our unsustainable practices have become increasingly evident. Deforestation, pollution, climate change, and loss of biodiversity are just a few of the challenges we face.
Today, as we stand at a critical juncture, it is more important than ever to address these issues head-on. We must acknowledge the interconnectedness of social, economic, and environmental systems and recognize that our actions have far-reaching consequences. In the face of accelerating climate change and the urgent need for transition to renewable energy sources, it is essential that we promote sustainability and prioritize environmental protection in all aspects of our lives.
Environmental Activism: Past and Present
Environmental activism has a rich history of inspiring change and mobilizing people to protect our planet. Starting with the conservation movements of the 19th century, individuals such as John Muir and Theodore Roosevelt laid the groundwork for the preservation of natural resources in the United States. Their efforts led to the creation of national parks and the establishment of environmental regulations.
In more recent times, powerful voices like Rachel Carson and her seminal book “Silent Spring” sparked a global awakening to the dangers of pesticides and the need for environmental regulation. The grassroots movement that emerged in response to her work eventually led to the creation of the Environmental Protection Agency in the United States.
Today, we find ourselves in an era defined by the global climate movement. Activists like Greta Thunberg and organizations like Extinction Rebellion have captured the world’s attention, demanding urgent action on environmental issues. Their calls for change have resonated with millions, leading to increased awareness and renewed commitment to preserving the environment for future generations.
The Power of Earth Day: A Global Call to Action
Earth Day serves as a poignant reminder that our collective actions can make a significant impact on the world we inhabit. It is a time for individuals, communities, and governments to come together and reflect on the changes we need to make to ensure a sustainable and thriving future for all life on Earth.
In recent years, Earth Day has evolved beyond local gatherings and now reaches worldwide audiences through iconic locations like New York Times Square, London Piccadilly, and The Netherlands. By bringing powerful environmental messages to these symbolic landmarks, Earth Day organizers seek to engage people of all backgrounds and inspire action on a global scale.
We have a shared responsibility to protect our planet, and it is through collective action that we can drive significant change. Each of us can contribute to a sustainable future by adopting environmentally-conscious practices, supporting renewable energy initiatives, advocating for policy changes, and educating others about the importance of environmental stewardship.
“In the end we will conserve only what we love, we will love only what we understand, and we will understand only what we are taught.” – Baba Dioum
As we celebrate Earth Day and honor the rich legacy of environmental activism, let us remember that the message of caring for our planet extends far beyond a single day. Let us make Earth Day every day, dedicating ourselves to preserving the environment and ensuring a sustainable future for generations to come.
‘Make Earth Day Every Day’ brings powerful environmental messages to New York Times Square, London Piccadilly and The Netherlands.
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by jsendak | Apr 24, 2025 | DS Articles
[This article was first published on
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1. A Passion for the Past
Since I was a teenager, History has been one of my passions. I was very lucky in high school to have a great History teacher whom I could listen to for hours. My interest was, of course, driven by curiosity about all those dead humans in historical plots that exist no more except in books, images, movies, and — mostly — in our imagination.
However, what really triggered my passion was realizing how different texts can describe the same event from such varied perspectives. We are able to see the same realities in different ways, which gives us the power to shape our lives — and our future — into something more meaningful, if we so choose.
2. First Encounters with R
When I began my master’s in public policy at the Hertie School in Berlin, Statistics I was a mandatory course for both management and policy analysis, the two areas of concentration offered in the course. I began the semester certain I would choose management because I’d always struggled with mathematical abstractions. However, as the first semester passed, I became intrigued by some of the concepts we were learning in Statistics I. Internal and external validity, selection bias, and regression to the mean were concepts that truly captured my interest and have applications far beyond statistics, reaching into many areas of research.
The Hertie School Building. Source: Zugzwang1972, CC BY 3.0, via Wikimedia Commons
Then came our first R programming assignments. I struggled endlessly with function syntax and felt frustrated by every error — especially since I needed strong grades to pass Statistics I. Yet each failure also felt like a challenge I couldn’t put down. I missed RStudio’s help features and wasted time searching the web for solutions, but slowly the pieces began to click.
3. Discovering DataCamp
By semester’s end, I was eager to dive deeper. That’s when I discovered that as Master candidates, we had free access to DataCamp — a platform that combines short, focused videos with in-browser coding exercises, no software installation required. The instant feedback loop—seeing my ggplot chart render in seconds—gave me a small win every day. Over a few months, I completed courses from Introduction to R and ggplot2 to more advanced statistical topics. DataCamp’s structured approach transformed my frustration into momentum. Introduction to Statistics in R was one of my first courses and helped me pass Stats I with a better grade. You can test the first chapter for free to see if it matches your learning style.
DataCamp Method. Source: AI Generated.
tips_and_updates
The links to DataCamp in this post are affiliate links. That means if you click them and sign up, I receive a small share of the subscription value from DataCamp, which helps me maintain this blog. That being said, there are many free resources on the Internet that are very effective for learning R without spending any money. One suggestion is the HTML free version of “R Cookbook” that helped me a lot to deepen my R skills.:
R Cookbook
4. Building Confidence and Choosing Policy Analysis
Armed with new R skills, I chose policy analysis for my concentration area—and I’ve never looked back. Learning to program in R created a positive feedback loop for my statistical learning, as visualizations and simulations gave life to abstract concepts I once found very difficult to understand.
5. Pandemic Pivot
Then the pandemic of 2020 hit, which in some ways only fueled my R learning since we could do little besides stay home at our computers. Unfortunately, my institution stopped providing us with free DataCamp accounts, but I continued to learn R programming and discovered Stack Overflow — a platform of questions and answers for R and Python, among other languages — to debug my code.
I also began reading more of the official documentation for functions and packages, which was not as pleasant or easy as watching DataCamp videos, which summarized everything for me. As I advanced, I had to become more patient and persevere to understand the packages and functions I needed. I also turned to books—mostly from O’Reilly Media, a publisher with extensive programming resources. There are also many free and great online books, such as R for Data Science.
Main Resources Used to Learn R. Source: Author.
6. Thesis & Beyond
In 2021, I completed my master’s degree with a thesis evaluating educational policies in Brazil. To perform this analysis, I used the synthetic control method—implemented via an R package. If you’re interested, you can read my thesis here: Better Incentives, Better Marks: A Synthetic Control Evaluation of Educational Policies in Ceará, Brazil.
My thesis is also an example of how you can learn R by working on a project with goals and final results. It also introduced me to Git and GitHub, a well known system for controling the versions of your coding projects and a nice tool to showcase your coding skills.
7. AI as a resource to learn programming
Although AI wasn’t part of my initial learning journey, I shouldn’t overlook its growing influence on programming in recent years. I wouldn’t recommend relying on AI for your very first steps in R, but it can be a valuable tool when you’ve tried to accomplish something and remain stuck. Include the error message you’re encountering in your prompt, or ask AI to explain the code line by line if you’re unsure what it does. However, avoid asking AI to write entire programs or scripts for you, as this will limit your learning and you may be surprised by errors. Use AI to assist you, but always review its suggestions and retain final control over your code.
Key Takeaways
- Learning R as a humanities major can be daunting, but persistence pays off.
- Embrace small, consistent wins — DataCamp’s bite‑sized exercises are perfect for that.
- Visualizations unlock understanding — seeing data come to life cements concepts.
- Phase in documentation and books when you need to tackle more advanced topics.
- Use AI to debug your code and explain what the code of other programmers does.
- Join the community — Stack Overflow, GitHub, online books and peer groups bridge gaps when videos aren’t enough.
Ready to Start Your Own Journey?
If you’re also beginning or if you want to deepen your R skills, DataCamp is a pleasant and productive way to get going. Using my discounted link below supports Coding the Past and helps me keep fresh content coming on my blog:
What was the biggest challenge you faced learning R? Share your story in the comments below!
Continue reading: My Journey Learning R as a Humanities Undergrad
Implications and Future Developments in Learning R Programming
The story of the author’s journey to learn R programming lends itself to key insights on the importance of persistence, the availability of resources, and the valuable role of technology, specifically AI, in the world of programming. Furthermore, these points have specific long-term implications and hint at possible future developments in the field of learning R programming.
Persistence in Learning Programming
One of the key takeaways from the author’s story is the significance of patience and persistence in learning programming. Encountering challenges and making mistakes are inherent parts of the learning process. As for the future, it is reasonable to predicting an increased emphasis and new learning strategies focused on nurturing this persistence.
Actionable Advice: Embrace setbacks as learning opportunities rather than reasons for giving up. Aim to cultivate an attitude of persistence and curiosity when learning new programming concepts.
Role of Available Resources
Another critical factor in the author’s journey is the effective use of available resources, such as DataCamp, Stack Overflow, and various online books. In the future, there is likely to be a continued proliferation of such platforms to support different learning styles.
Actionable Advice: Utilize online resources — platforms, forums, and digital books — that best suit your learning style. Experiment with several resources to find the best match.
Impact of AI in Programming
The author also highlights the valuable role of AI in learning programming and debugging code. As AI technologies continue to evolve, their role in education, and specifically in teaching and learning programming, is likely to expand.
Actionable Advice: Explore the use of AI technologies to assist with learning programming, but avoid relying solely on AI. It’s crucial to retain control and a deep understanding over your code.
Study R through Real Projects
Working on practical projects, such as the author’s thesis, is a fantastic way to apply and consolidate R skills. As this hands-on approach to learning grows in popularity, future educational programs are likely to emphasize project-based work.
Actionable Advice: Regularly apply newly learned R concepts to real-world projects. This consolidates understanding and provides tangible evidence of your growing abilities.
Conclusion
The journey of learning R or any other programming language doesn’t necessarily have to be a difficult uphill battle. With a persistent attitude, a good balance of theory and practice, the help of online resources and AI, learners can make significant strides in their programming skills. Future advances in learning trends and tech will only make resources more readily available and diverse, making it a promising field for those wishing to get started.
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by jsendak | Apr 24, 2025 | DS Articles
Many organisations look at your LinkedIn before getting that initial call back. Therefore, you need to ensure it is optimised.
LinkedIn Optimization: Future Developments and Long-Term Implications
In a digital-forward era where recruiters regularly reference the LinkedIn profile of a potential candidate before reaching out, having an optimized LinkedIn is critical. Not only can this increase your chances of getting a call back, but can significantly influence your professional marketability. Let’s delve deeper into the implications and potential developments of LinkedIn optimization.
Long-Term Implications of LinkedIn Optimization
A well-optimized LinkedIn profile can offer myriad benefits in the long run. These include:
- Increased Visibility: An optimized profile ascends in LinkedIn’s search rankings, thereby increasing the chances of it being viewed by recruiters, potential partners, colleagues or clients.
- Expanded Network: Through a well-rounded profile, professionals can also attract like-minded individuals in their industry, further expanding their network.
- Professional Credibility: Having a LinkedIn profile that effectively showcases skills, experience, and accomplishments can serve as a digital validation of a person’s professional competence.
- Job Opportunities: An optimized LinkedIn profile is likely to attract more job offer developments, magnifying the career opportunities available.
Future Developments in LinkedIn Optimization
LinkedIn, like any other digital platform, is continually evolving. Here are a few potential future developments that could reshape LinkedIn optimization strategies:
- Artificial Intelligence (AI): LinkedIn might integrate more AI features to refine profile searches. An understanding of these can lead to better profile optimization.
- Videos and Interactive Content: As multimedia content continues to take center stage, the value of videos and interactive content in LinkedIn profiles may rise in the near future.
- Development in Analytics: LinkedIn may also improve its analytics tools allowing users to better understand their profile performance and adjust their optimization strategies accordingly.
Actionable Advice for LinkedIn Optimization
Given these insights and potential trends, here are some tips to optimize your LinkedIn profile:
- Complete Your Profile: Include all relevant information, from educational qualifications to professional experience to skills.
- Use Relevant Keywords: Include industry-related keywords in your profile to increase its searchability.
- Add Multimedia Materials: Consider adding videos, presentations, or images to supplement your profile.
- Stay Active: Regularly share and engage with content relevant to your industry.
- Leverage Analytics: Use LinkedIn’s analytics to track the performance of your profile and adjust strategies as needed.
Remember, your LinkedIn profile serves as your digital business card. Invest the time and effort to keep it updated and optimized. After all, your next career opportunity might just be a LinkedIn search away!
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by jsendak | Apr 24, 2025 | DS Articles
Discover how Geometric Deep Learning revolutionizes AI by processing complex, non-Euclidean data structures, enabling breakthroughs in drug discovery, 3D modeling, and network analysis.
Geometric Deep Learning: Revolutionizing the Field of AI
The integration of Geometric Deep Learning (GDL) into Artificial Intelligence (AI) supports the ability to handle and process complex, non-Euclidean data structures. This groundbreaking advancement provides several promising opportunities, paving the way for notable improvements in various fields such as drug discovery, 3D modeling, and network analysis.
The Implications of Geometric Deep Learning
GDL’s profound ability to process irregular data structures could have remarkable long-term implications. Traditional AI methods often necessitate data to be structured in tabular or Euclidean formats. However, this requirement often inhibits the comprehension and processing of complex, irregular data sets involved in many modern scientific and technological processes.
With GDL, this barrier is effectively obliterated. The technology affords the ability to handle and dissect complex unstructured data. Consequently, it presents the possibility of making significant strides in various scientific fields such as drug discovery, where complex structures of chemical or genetic compounds need to be understood and manipulated.
Possible Future Developments
The advancement in Geometric Deep Learning technology promises exciting future developments within the AI sector. From a potential revolution in drug discovery processes to enhancements in 3D modeling and network analysis, the integration of GDL into traditional algorithms could provide an unprecedented depth and scope of analysis.
Specifically, in the field of drug discovery, GDL could potentials help in interpreting complex molecular structures and interactions. It can also expedite the process by presenting more accurate predictions of how new drugs might interact with a variety of biological systems.
Within the realm of 3D modeling, GDL could offer significant improvements in the manipulation and representation of data. This could ultimately aid architectural planning, video game designs, and other fields that require 3D modeling.
Additionally, for network analysis, GDL may provide a more exhaustive understanding of how data points within a network connect and interact. This could prove invaluable for improving the efficiency of transport systems, optimizing computer networks, or analyzing social networks.
Actionable Advice
Based on these insights, it is worthwhile for organizations dealing with complex, non-Euclidean data structures to consider integrating Geometric Deep Learning into their AI systems. Doing so could provide them with a competitive edge by allowing them to interpret and manipulate complex data more efficiently.
- Drug discovery organizations: Consider leveraging GDL to expedite and enhance the drug discovery process through better understanding of complex biological systems and molecular interactions.
- 3D modeling businesses: Utilize GDL to improve the accuracy and efficiency of your 3D modeling processes, potentially leading to significant time and cost savings.
- Companies dealing with network analysis: Implement GDL to gain deeper insights into network interactions and improve the efficiency of your systems.
In conclusion, the advent of Geometric Deep Learning presents immense potential and opportunities within the realm of AI. Stakeholders across various industries should consider leveraging this technology to optimize their operations and research capabilities.
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by jsendak | Apr 24, 2025 | AI
arXiv:2504.16635v1 Announce Type: new Abstract: In an environment of increasingly volatile financial markets, the accurate estimation of risk remains a major challenge. Traditional econometric models, such as GARCH and its variants, are based on assumptions that are often too rigid to adapt to the complexity of the current market dynamics. To overcome these limitations, we propose a hybrid framework for Value-at-Risk (VaR) estimation, combining GARCH volatility models with deep reinforcement learning. Our approach incorporates directional market forecasting using the Double Deep Q-Network (DDQN) model, treating the task as an imbalanced classification problem. This architecture enables the dynamic adjustment of risk-level forecasts according to market conditions. Empirical validation on daily Eurostoxx 50 data covering periods of crisis and high volatility shows a significant improvement in the accuracy of VaR estimates, as well as a reduction in the number of breaches and also in capital requirements, while respecting regulatory risk thresholds. The ability of the model to adjust risk levels in real time reinforces its relevance to modern and proactive risk management.
The article “arXiv:2504.16635v1” addresses the challenge of accurately estimating risk in today’s volatile financial markets. Traditional econometric models, such as GARCH, struggle to adapt to the complexity of current market dynamics. To overcome these limitations, the authors propose a hybrid framework for Value-at-Risk (VaR) estimation that combines GARCH volatility models with deep reinforcement learning. By incorporating directional market forecasting using the Double Deep Q-Network (DDQN) model, the authors create an architecture that allows for dynamic adjustment of risk-level forecasts based on market conditions. Empirical validation on daily Eurostoxx 50 data demonstrates significant improvements in the accuracy of VaR estimates, a reduction in breaches, and lower capital requirements while still adhering to regulatory risk thresholds. This model’s ability to adjust risk levels in real-time highlights its relevance to modern and proactive risk management.
Reimagining Risk Estimation: A Hybrid Framework for Value-at-Risk
In today’s ever-changing financial landscape, accurately estimating risk has become a daunting challenge. Traditional econometric models, such as GARCH and its variants, have proven to be insufficient in adapting to the complexity and volatility of the current market dynamics. To overcome these limitations, a hybrid framework for Value-at-Risk (VaR) estimation that combines GARCH volatility models with deep reinforcement learning is proposed. This innovative approach incorporates directional market forecasting using the Double Deep Q-Network (DDQN) model, treating the task as an imbalanced classification problem.
One of the major limitations of traditional econometric models is their reliance on rigid assumptions that do not adequately capture the intricacies of market behavior. The proposed hybrid framework addresses this drawback by leveraging the power of deep reinforcement learning, which enables the dynamic adjustment of risk-level forecasts according to prevailing market conditions.
The architecture of the hybrid framework allows for real-time adjustment of risk levels, offering a proactive approach to risk management that is essential in today’s fast-paced financial markets. By combining GARCH volatility models with deep reinforcement learning, the proposed framework enhances the accuracy of VaR estimates and reduces the number of breaches, as well as the capital requirements, while still adhering to regulatory risk thresholds.
Empirical validation of the hybrid framework using daily Eurostoxx 50 data, encompassing periods of crisis and high volatility, demonstrated a significant improvement in the accuracy of VaR estimates. This finding highlights the potential of the hybrid framework to better capture market dynamics and provide more reliable risk estimations.
The ability of the hybrid framework to adapt to changing market conditions and adjust risk levels in real time is a game-changer in the field of risk management. Traditional models often fail to account for shifts in market dynamics, resulting in inaccurate risk estimations that may lead to substantial losses. The integration of deep reinforcement learning into the risk estimation process offers a more robust and flexible approach that better aligns with the complexities of today’s financial markets.
As financial markets continue to evolve, embracing innovative solutions becomes imperative for effective risk management. The proposed hybrid framework for VaR estimation, combining GARCH volatility models with deep reinforcement learning, offers a forward-thinking approach that can enhance risk management practices. By leveraging the power of artificial intelligence and machine learning, financial institutions can achieve more accurate risk estimations, reduce breaches, and ensure compliance with regulatory requirements.
In conclusion, the hybrid framework presented in this article provides a fresh perspective on risk estimation in volatile financial markets. By incorporating deep reinforcement learning with GARCH volatility models, the proposed framework enables dynamic adjustment of risk-level forecasts and offers real-time risk management capabilities. This innovative solution holds great promise for improving the accuracy of VaR estimates and strengthening risk management practices in the face of evolving market dynamics.
The paper titled “A Hybrid Framework for Value-at-Risk Estimation using GARCH and Deep Reinforcement Learning” addresses the challenge of accurately estimating risk in volatile financial markets. The authors argue that traditional econometric models like GARCH are often too rigid to adapt to the complexity of current market dynamics. To overcome these limitations, they propose a hybrid framework that combines GARCH volatility models with deep reinforcement learning.
The incorporation of deep reinforcement learning into the estimation of Value-at-Risk (VaR) is an interesting approach. By using the Double Deep Q-Network (DDQN) model, the authors aim to incorporate directional market forecasting into the framework. They treat the task as an imbalanced classification problem, which allows for dynamic adjustment of risk-level forecasts based on market conditions.
The empirical validation of the proposed framework using daily Eurostoxx 50 data covering periods of crisis and high volatility is a significant contribution. The results show a significant improvement in the accuracy of VaR estimates, as well as a reduction in the number of breaches and capital requirements, while still respecting regulatory risk thresholds.
One of the key strengths of this hybrid framework is its ability to adjust risk levels in real-time. This is particularly relevant in modern risk management practices, where proactive risk mitigation is crucial. By incorporating deep reinforcement learning, the model can adapt to changing market dynamics and provide more accurate risk estimates.
However, it is important to note that the paper does not discuss potential limitations or challenges of implementing this hybrid framework in real-world scenarios. It would be valuable to explore how the model performs in different market conditions and whether it can be effectively used by financial institutions for risk management purposes.
Overall, the proposed hybrid framework for VaR estimation shows promising results in improving accuracy and reducing breaches and capital requirements. It provides a novel approach to incorporating machine learning techniques into risk management practices. Future research can focus on further validating the framework with different datasets and exploring its practical implementation in financial institutions.
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