Category Archives: Data Science
Altair: A Provider of Simulation, Data Analytics, and High … – Fagen wasanni
Altair (NASDAQ: ALTR) is a company that offers a range of solutions in the areas of simulation, data analytics, and high-performance computing. While Altair heavily promotes its AI capabilities, its history shows average growth and efficiency, suggesting that AI may not provide a significant boost to the companys valuation.
Altair operates in different markets with varying sizes, growth drivers, and competitive dynamics. Its primary market is simulation software, which involves analyzing and optimizing product designs using virtual prototypes. Simulation is increasingly used across industries to improve product quality, lower costs, and reduce time to market.
Altairs market opportunity includes software for high-performance computing (HPC) infrastructure and running simulations. The company is positioned to capture spending related to workload management systems for high-end HPC servers. Altair also has exposure to the Internet of Things (IoT) and analytics markets, which are projected to exceed $110 billion by 2025.
Despite the current hype around AI, Altairs peers are experiencing decelerating growth and indicators suggest softening demand in Altairs markets. The number of job openings mentioning simulation software provider ANSYS has declined, indicating weaker demand. Similarly, job openings mentioning data science requirements continue to decrease.
Altair offers a portfolio of engineering simulation software and services. Its solutions include tools for simulation, high-performance computing, and data analytics. Altairs software optimizes design performance across various disciplines and supports multi-physics simulation. The company also offers AI, visualization, and rendering solutions.
Altair aims to lower the barriers to performing simulations and has invested in SimSolid, a next-generation simulation technology. SimSolid allows structural simulation using CAD models without the need for geometry simplification or meshing. This technology significantly improves the productivity of users and may help Altair penetrate the mid-market and lower end of the market.
Overall, while Altairs business spans multiple markets, its current valuation appears stretched, and the weakening demand environment poses downside risk. The companys focus on simulation, data analytics, and high-performance computing positions it for growth but relies on overcoming industry challenges and delivering innovative solutions.
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Altair: A Provider of Simulation, Data Analytics, and High ... - Fagen wasanni
Top 10 Benefits of Blockchain for Data Science – Analytics Insight
The combination of blockchain and data science technologies has many benefits in different sectors
The power of blockchain and data science is evident in their impact on different sectors of the economy, such asfinance, healthcare, and supply chain management. They can improve the accuracy and speed of decision-making andpredictive analytics, primarily when blockchain technology supportsdata science. The data is stored and validated by blockchain, and data science applies this data to gain insights into different data segments. Theblockchainsdecentralized nature makes the data consistent across the network, allowing data science to generate predictions and take decisions from the data effectively.
The following are the leading 10 advantages of utilizing Blockchain and Information Science together:
One of the advantages of blockchain for information science is that it empowers information recognizability. This implies that you can constantly know where your information came from and where it went to. When you want to guarantee your researchs accuracy and dependability, the blockchain also ensures that no one has altered your data.
Blockchain is a brand-new technology that is changing the way businesses operate. It tends to be applied to any industry and can change how we work, live, and connect. As a conveyed record, blockchain gives a method for people who dont have any idea or entrust each other to impart data with certainty. Exchanges are put away in blocks that are connected in sequential requests within chains.
Blockchain has made a better approach to overseeing information. This kind of data is put away in blocks, and each block has a timestamp, which makes the information carefully designed. The blockchain keeps information from being altered or eradicated, so it tends to be utilized for future investigation and examination.
By utilizing a decentralized record and hourly updates, blockchain innovation makes an issue-liberated universe of information. Blockchain is a morally sound computerized record that stays refreshed on the fly and contains a record of every exchange that has at any point occurred, giving it a huge measure of trust. Blockchain innovation guarantees top-notch information feed.
Blockchain-empowered information trustworthiness is a cutting-edge innovation that will fundamentally impact how we carry on with work. This is a decentralized record framework that guarantees information cant be altered or changed without leaving a permanent record of the change.
The blockchain gives an unchanging record of exchanges between two gatherings without the requirement for a focal power to check the exchange. This implies that whenever something has been kept in the blockchain, it cant be modified or deleted.
Each block in the blockchain contains data about its past block (parent), which makes it conceivable to follow any exchange back to its starting point.
Blockchain has many use cases that have been investigated. However, one of them is its capacity to fabricate trust. Blockchain can assist with making a more straightforward framework that depends on the local area more than any single individual inside it. The blockchain gives users control over their information and access to the data they want to see.
Associations data is normally put away in information lakes. Blockchain stores data in a particular block using a particular cryptographic key and makes use of the datas source. Blockchain is a protected, straightforward, and quick method for guaranteeing that anything of significant worth can be exchanged proficiently. Blockchain takes into consideration the exchange of proprietorship without depending on a confided-in outsider.
Blockchain information, very much like different kinds of information, can be broken down to uncover significant bits of knowledge into ways of behaving and drifts and, as such can be utilized to anticipate future patterns. It very well may be applied to themes, for example, supply chains, property the board, and web-based promoting.
By lowering costs associated with brokers, intermediaries, and third parties, has contributed to cost reduction. It additionally helps in speeding up and straightforwardness of exchanges, which lessens costs related to consistency.
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Top 10 Benefits of Blockchain for Data Science - Analytics Insight
Research Engineer, Data Analyst job with NATIONAL UNIVERSITY … – Times Higher Education
Job Description
In this position, you will be working on end-to-end data pipeline implementation from understanding research objectives, data collection using cameras and wearable sensor technology, exploratory data analysis, cleaning and pre-processing of raw data, modelling (using Machine Learning/Deep Learning techniques) and sharing of insights to stakeholders using visualizations. The goal is to find a relationship between qualitative and quantitative data in order to understand passengers preferences and improving the passengers inflight experience. You will work closely with hardware engineers, design researchers and project manager to successfully collect data from sensors in a cabin stimulator, leverage predictive modelling and provide meaningful insights.
Requirements
Covid-19 Message
At NUS, the health and safety of our staff and students are one of our utmost priorities, and COVID-vaccination supports our commitment to ensure the safety of our community and to make NUS as safe and welcoming as possible. Many of our roles require a significant amount of physical interactions with students/staff/public members. Even for job roles that may be performed remotely, there will be instances where on-campus presence is required.
Taking into consideration the health and well-being of our staff and students and to better protect everyone in the campus, applicants are strongly encouraged to have themselves fully COVID-19 vaccinated to secure successful employment with NUS.
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Research Engineer, Data Analyst job with NATIONAL UNIVERSITY ... - Times Higher Education
The Future of Internet Technology: Predictive Analytics in South … – Fagen wasanni
Exploring the Future of Internet Technology: The Rise of Predictive Analytics in South & Central America
The future of internet technology is rapidly evolving, and one of the most promising developments is the rise of predictive analytics. This technology, which uses historical data to predict future events, is becoming increasingly prevalent in South and Central America. As these regions continue to embrace digital transformation, predictive analytics is poised to play a pivotal role in shaping their future.
Predictive analytics is a powerful tool that can help businesses and governments make more informed decisions. By analyzing past trends and patterns, it can provide insights into what might happen in the future. This can be particularly useful in areas such as finance, healthcare, and retail, where understanding future trends can have a significant impact on decision-making.
In South and Central America, the adoption of predictive analytics is being driven by a combination of factors. Firstly, there is a growing recognition of the value of data. Businesses and governments alike are beginning to understand that data is not just a byproduct of their operations, but a valuable resource that can be harnessed to drive growth and innovation.
Secondly, there is an increasing availability of data. With the proliferation of internet-connected devices, businesses and governments are able to collect and analyze more data than ever before. This is creating a wealth of opportunities for predictive analytics.
Thirdly, there is a growing demand for more efficient and effective decision-making. In an increasingly competitive global economy, businesses and governments are under pressure to make the right decisions at the right time. Predictive analytics can help them do this by providing insights into future trends and patterns.
Despite these promising developments, there are also challenges to the adoption of predictive analytics in South and Central America. One of the main challenges is the lack of skilled data scientists and analysts. While there is a growing interest in data science and analytics, there is still a shortage of professionals with the necessary skills and expertise.
Another challenge is the lack of data infrastructure. While the availability of data is increasing, many businesses and governments lack the necessary infrastructure to store, manage, and analyze this data. This can make it difficult to fully leverage the potential of predictive analytics.
However, these challenges are not insurmountable. With the right investment in education and infrastructure, South and Central America have the potential to become leaders in the field of predictive analytics. Already, there are signs of progress. For example, in Brazil, there is a growing number of startups and companies specializing in data science and analytics. Similarly, in Mexico, the government is investing in data infrastructure and education to foster a data-driven economy.
In conclusion, the future of internet technology in South and Central America is looking bright, with predictive analytics playing a key role. While there are challenges to overcome, the potential benefits are significant. By harnessing the power of predictive analytics, businesses and governments can make more informed decisions, drive innovation, and shape a better future.
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The Future of Internet Technology: Predictive Analytics in South ... - Fagen wasanni
Machine learning: The saviour of data science triumph – Times of India
In the vast realm of data science, industry professionals often find themselves engrossed in the exciting pursuit of extracting valuable insights from massive volumes of data. However, they often encounter a formidable obstaclemanual Exploratory Data Analysis (EDA). A significant amount of time is dedicated to meticulously scrutinizing data, uncovering patterns and unlocking its hidden secrets. This process can be captivating yet arduous, leaving a sense of yearning for an efficient way to navigate the depths of data exploration. Little is it known that the answer lies within the realm of machine learning, eagerly waiting to revolutionize the world of EDA and propel them toward unparalleled efficiency.
In todays data-driven world, data scientists play a pivotal role in uncovering valuable insights and driving innovation. Armed with their insatiable curiosity and unwavering passion for unearthing concealed truths, they hold the key to transforming raw data into actionable intelligence. However, a significant challenge lies in the tedious and time-consuming process of manual Exploratory Data Analysis (EDA), which can impede progress and introduce subjective bias.
In the face of overwhelming manual EDA challenges, an industry-transforming solution emerged: machine learning. Recognizing its potential to liberate data scientists from the burdensome task of manual exploration, technical experts eagerly embraced this new paradigm. Immersed in this innovative solution, professionals have discovered a realm teeming with unprecedented automation and enhanced efficiency.
The emergence of machine learning algorithms has revolutionized the industry by harnessing its immense power to automate multiple stages of Exploratory Data Analysis (EDA). What was once a labor-intensive task, data preprocessing has now become a seamless experience as algorithms proficiently manage missing values, identify outliers, and normalize data with exceptional accuracy. Moreover, the field of data visualization has undergone a significant transformation with the guidance of machine learning models that adeptly recognize intricate patterns and convert complex datasets into visually captivating representations. Additionally, the introduction of automated feature engineering has put an end to the taxing manual transformation of raw data, providing professionals with effortless access to valuable insights. These advancements have empowered industry practitioners to unlock and leverage crucial information with unprecedented ease.
Empowered by machine learning-powered recommendations, the journey through EDA has reached unprecedented heights and evolved into the Data Science Studio. These recommendations serve as guiding beacons, illuminating uncharted avenues and paving the way for innovative analysis, fueling an unquenchable thirst for knowledge. With the liberation from manual EDA, a future filled with possibilities has been embraced, where the harmonious synergy between data scientists and machine learning algorithms propels the industry towards new frontiers of discovery.
This narrative stands as a testament to the industry-wide transformation, transitioning from a labor-intensive landscape dominated by manual Exploratory Data Analysis (EDA) to a realm of enhanced efficiency driven by the remarkable power of machine learning. The extensive efforts previously dedicated to manual exploration now pale in comparison to the boundless possibilities that automation has brought forth. Contemplating this transformative journey instills a revitalized sense of purpose and deep gratitude for the harmonious fusion of human expertise and machine learning capabilities. With collective strength, we are positioned to reshape the data science landscape, unlocking its full potential and ushering in an era characterized by unparalleled insights and groundbreaking innovation.
Views expressed above are the author's own.
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Machine learning: The saviour of data science triumph - Times of India
How Python and R Dominate the Data Science Landscape? – Analytics Insight
Know how Python and R language are powerful data science languages
Its critical to monitor the market trends as we navigate the ever-changing data science landscape. The popularity and usage of Python and R, two important data science languages, will be examined in this article as of July 2023.
The TIOBE index for July 2023 emphasizes Pythons hegemony in the programming industry. Python maintains its top spot with a rating of 13.42% despite a tiny decline of 0.01% from the previous month.
Pythons success is due to and supported by its expanding use in data science and artificial intelligence, which is made possible by its user-friendliness, huge library, and robust community support. By the way, the Datacamps Guide on how to learn Python outlines some of the primary reasons why Python is so popular these days. Read it if youre interested in learning more. The time frames needed to master the languages we adore, Python and R, have also been estimated by Datacamp.
From the standpoint of a newcomer, the learning curves for Python, R, and even Julia are identical.
Another language used frequently in the data science community is the specialized R language, renowned for its statistical computing capabilities. R now holds the 19th position in TIOBE with a rating of 0.87%, up 0.11% from the previous month. R continues to have a significant place in data science, especially among statisticians and academics that require complex statistical analysis or the construction of aesthetically pleasing data visualizations, even though it may not be as popular as Python.
Interestingly, the TIOBE index also observes that C++ is advancing and may soon exceed C. Its an intriguing trend that JavaScript has risen to an all-time high at position #6, indicating a growing interest in web development languages.
Python continues to keep the top spot with a share of 27.43%, according to the PYPL index as of July 2023, produced by examining how frequently language tutorials are searched on Google. This is true despite a minor decline of 0.2% over the previous year. This solidifies Pythons position as the preferred language for many in the data science community because of its usability and the robust tools it provides for data manipulation and analysis. Accept the truth that it is what it is.
R is presently ranked seventh with a share of 4.45%, a rise of 0.1% from the previous year. R is still a favorite among data scientists, especially those who work in statistical analysis and data visualization, as shown by this.
Some of the other languages included in the PYPL index are interesting trends to keep an eye on. Python is followed in the rankings by Java (16.19%), JavaScript (9.4%), and C# (6.77%), in that order. Newer languages are also gaining popularity, with TypeScript, Swift, and Rust showing a notable rise of 0.6% over the previous year.
Approximately 14% of all inquiries on Stack Overflow in July 2023 were linked to Python, a consistent percentage for this website. This percentage was down from the start of the year. This decline in the emergence of AI solutions like ChatGPT has diminished individuals need to ask for assistance on Stack Overflow. On the other hand, between 3.00% and 3.30% of the queries were related to R, which is nearly the same as the previous month. The entire year, the same trend.
Additionally, StackOverflow has made available the findings of their Developer Survey 2023, which ranks Python third and R 21st in popularity. This year, professional developers used Python more frequently than SQL, thanks to its continued popularity.
In conclusion, the data scientists toolbox still must include Python and R. Despite the advent and expansion of other languages, Python and R remain unrivaled for data science applications due to their strength, flexibility, and usability.
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How Python and R Dominate the Data Science Landscape? - Analytics Insight
Fourteen things you need to know about collaborating with data scientists – Nature.com
Think of your relationship as a partnership, rather than as a transaction, say data scientists.Credit: Morsa Images/Getty Images
Data science is increasingly an integral part of research. But data scientists can wear many hats: interdisciplinary translator, software engineer, project manager and more. Fulfilling all these roles is challenging enough; but this difficulty can be exacerbated by differing expectations and, frankly, an undervaluing of data scientists contributions.
For example, although our primary role is data analysis, researchers often approach data scientists for help with data acquisition and wrangling as well as software development. Although in one sense this is technical work, which perhaps only a data scientist can do, thinking of it as such overlooks its deep connection with reproducible research. The work also involves elements of data management, project documentation and adherence to best practices. Solely emphasizing a projects technical requirements can lead collaborators to view the work as a transaction rather than as a partnership. This misunderstanding, in turn, poses obstacles to communication, project management and reproducibility.
As data scientists with a collective 17 years of experience across dozens of interdisciplinary projects, we have seen at first hand what does and doesnt work in collaborations. Here, we offer tips for how to make working relationships more productive and rewarding. To our fellow data scientists: this is how we strike the balance. To the general audience: these are the parts of data science with which everyone on the team should engage.
Set boundaries and norms for how communication will happen. Do members want to meet virtually or in person? When, how often, and on what platform should they meet? Decide how you will record tasks, project history and decisions. Make sure all members of the team have access to the project records so that everyone is kept abreast of its status and goals. And identify any limitations due to IT policies or privacy concerns. For example, many US government agencies restrict employees to an approved list of software tools.
Err on the side of over-communicating by including everyone on communications and making the projects repositories available to all members of the team. Involve collaborators in technical details, even if they are not directly responsible for these aspects of the project.
Different disciplines can attach very different meanings to the same term. Map, for example, means different things to geographers, geneticists and database engineers. When discrepancies arise, ask for clarification. Learn about the other disciplines on your team and be prepared to learn their jargon and methods.
Questions from people outside your domain can reveal important workflow difficulties, illuminate misunderstandings or expose new lines of enquiry. Dont allow questions to linger; if you need time to consider the answer, acknowledge that it was asked and follow it up. Address all questions with respect.
Diagrams, screenshots, process descriptions, and summary statistics can serve as a unifying language for team members and emphasize the bigger picture, avoiding unnecessary detail. Use them when you can.
Before starting the research, identify the goals and expected outputs of the collaboration. As a team, create a project timeline with concrete milestones, making sure to allow time for project set-up and data exploration. Ensure all team members are aware of the timeline and address any concerns before proceeding.
One potential pitfall of working collaboratively is that a projects scope can easily expand. To guard against this, when new ideas emerge, decide as a team if the new task helps you to meet the original goal. You might need to set the idea aside to stay on target. Perhaps this idea is the source of the next collaboration or grant application. A clear red flag is the question, You know what would be cool?
Agree early on about how and where the team will share files. This might involve your own servers, cloud storage, shared document-editing platforms, version-control platforms or a combination of these. Everyone should have appropriate levels of access. If theres a chance that the project will produce code or data for public use, develop a written plan for long-term storage, distribution, maintenance, and archiving. Discuss licensing early.
Develop a data-processing pipeline that extends from raw data to final outputs, avoiding hard-to-reproduce graphical interfaces or ad hoc steps whenever possible in favour of coded alternatives written in languages such as Python, R and Bash. Use a version-control system, such as git, to track changes to the project files, and an environment manager, such as conda, to track software versions.
Be proactive about documenting technical steps. Before you begin, write draft documentation to reflect your plan. Edit and expand the documentation as you progress, to clarify details. Maintain the documentation after the project concludes so that it serves as a reference. Write in plain language and keep jargon to a minimum. If you must use jargon, define it.
Although you cant anticipate all project outputs in advance, discuss attribution, authorship and publication responsibilities as early as possible. This clarity provides a point of reference for reassessing participants roles if the project direction changes.
Collaborating with people who have diverse backgrounds and skill sets often sparks creativity. Be open to ideas, but be willing to put them on the back burner or discard them if they dont fit the project scope and timeline. Working with domain experts in one-on-one advice sessions, incubator projects, and in-the-moment data-analysis sessions often surfaces new data sources or potential modelling applications, for example. More than a few of our current grant projects have their roots in what was at first an improvisational exercise.
Disciplines are vast, and knowing when to defer to others expertise is essential for project momentum and keeping contributions equitable. Striking this balance is especially important around project infrastructure. Not everyone needs to write or run code, for example, but learning how to use technical platforms, such as code repositories or data storage, rather than relying on others to do so, balances the workload. If collaborators want to be involved in technical details, or if the project will be handed over to them in the long term, data scientists might need to teach collaborators as well.
Recognize when a project has run its course, whether it has been successful or not. Ongoing requests for work such as new analyses often weigh unequally on those responsible for project infrastructure. If the project didnt achieve its stated goals, look for a silver lining: it doesnt mean failure if there are insights, results or new lines of enquiry to explore. Above all, respect the timeline and the fact that you and your collaborators have other responsibilities.
Interdisciplinary collaborations that integrate data science can be challenging, but we have found these guidelines to be effective. Many involve skills that you can develop and refine over time. Thoughtful communication, careful project organization and equitable working relationships transform projects into genuine collaborations, yielding research that would not otherwise be possible.
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Fourteen things you need to know about collaborating with data scientists - Nature.com
10 Best Data Science Communities to Join in 2023 – Analytics Insight
Weve created a list of the finest 10 best data science communities in 2023
Asdata science gets more popular, so does the number of groups and resources committed to it. Whether youre just starting or have been working in the area for years, several resources are available for help, education, and cooperation.
1. Reddit: Redditis one of the webs largest and most activedata analytics communities. Its a terrific place to ask questions, exchange thoughts, and stay current on the latest news and advancements in the area, and it has over 1.5 million members.
2. Kaggle: Kaggle is an excellent place to begin. Kaggle is one of the largest online data science slack channels, with over 1.5 million users. You can discover datasets, code samples, and discussion forums for any data science topic imaginable.
3. IBM Data Community: There are several ways to participate in the data analytics forum, and IBM Data Community is an excellent resource for data scientists of all levels of expertise. The community provides a variety of materials, such as blogs, articles, webinars, and online courses.
4. Tableau: Tableau Public is one of these. If you use Tableau to visualize data, this is the community for you.TableauPublic is a free online platform where you can share your visualizations with the rest of the world. You may also look at other data scientists work, get comments on your own, and engage in challenges and contests.
5. Stack Overflow: You may discover solutions to queries regarding coding, data analysis, machine learning, and other topics on Stack Overflow. You can also ask your questions and receive responses from the community. Furthermore, Stack Overflow provides various tools for data scientists, such as articles, tutorials, and courses.
6. Open Data Science: If youre searching for a comprehensive data science community, Open Data Science is a beautiful place to start. It is one of the largest online groups for data scientists, with over 30,000 members. In addition to a jam-packed Slack channel, Open Data Science provides many resources such as publications, courses, events, and employment.
7. Data Science Central: Its one of the largest online groups for data scientists, with over 600,000 members. Several resources are available on the site, including articles, tutorials, webinars, and an active community where users may ask questions and discuss their work. Data Science Central is an excellent resource in your toolbox at any level of your data science journey.
8. Dataquest: Dataquest is a fantastic resource. They provide articles, webinars, and courses on various topics ranging from machine learning to deep learning. Its also a terrific location to compete in data science challenges and learn from the finest in the industry if you prefer a more hands-on approach.
9. Driven Data: Driven Data is among the most well-known data science communities. Driven Data organizes challenges and contests that are available to anybody with an interest in data science. This is a terrific opportunity to exercise your coding muscles and put your problem-solving abilities to the test.
10. Data Community DC: Data Community DC is a Washington, DC-based professional network for data scientists. The group provides its members with various services and activities, such as monthly gatherings, an online forum, and a mentorship program.
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10 Best Data Science Communities to Join in 2023 - Analytics Insight
The First Half of 2023: Data Science and AI Developments – KDnuggets
A lot has happened in the first half of 2023. There have been significant advancements in data science and artificial intelligence. So much that its been hard for us to keep up with them all. We can definitely say that the first half of 2023 has shown rapid progress that we did not expect.
So rather than talking too much about how were all wood by these innovations, lets talk about them.
Im going to start off with the most obvious. Natural Language Processing (NLP). Something that was building in the dark, and in the year 2023 has come to light.
These advancements were proven in OpenAIs ChatGPT, which took the world by storm. Since their official release earlier on in the year, ChatGPT has moved from GPT-4 and now were expecting GPT-5. They have released plugins to improve people's day-to-day lives, and workflows for data scientists and machine learning engineers.
And we all know after ChatGPT released, Google released Bard AI which has proven to be successful amongst people, businesses, and more. Bard AI has been competing with ChatGPT for the best chatbot position, providing similar services such as improving tasks for machine learning engineers.
In the midst of the release of these chatbots, we have seen large language models (LLM) drop out of thin air. Large Model Systems Organization (LMSYS Org), an open research organization founded by students and faculty from UC Berkeley created ChatBot Arena - a LLM benchmark to make models more accessible to everyone using a method of co-development using open datasets, models, systems, and evaluation tools.
So now people are getting used to chatbots that answer questions for them and make their work and personal life much easier - what about data analysts and machine learning specialists?
Well theyve been using AutoML - a powerful tool for data professionals such as data scientists and machine learning engineers to automate data preprocessing, hyperparameter tuning, and perform complex tasks such as feature engineering. With the advancements in data science and AI, naturally we have seen a high demand for data and AI specialists. However, as the progress is moving at a rapid rate, we are seeing a shortage of these AI professionals. Therefore, being able to find ways to explore, analyze, and predict data in an automated process will improve the success of a lot of companies.
Not only will it be able to free up time for data specialists, but organizations will have more time to expand and be more innovative on other tasks.
If you were around for the outburst of chatbots, you would have seen the words Generative AI being thrown around. Generative AI is capable of generating text, images, or other forms of media based on user prompts. Just like the above advancements, generative AI is helping different industries with tasks to make their lives easier.
It has the ability to produce new content, replace repetitive tasks, work on customized data, and pretty much generate anything you want. If generative AI is new to you, you will want to learn about Stable Diffusion - it is the foundation behind generative AI. If you are a data scientist or data analyst, you may have heard of PandasAI - the generative AI python library, if not it is an open-source toolkit which integrates generative AI capabilities into Pandas for simpler data analysis.
But with these generative AI tools and softwares being released, Are Data Scientists Still Needed in the Age of Generative AI?
Deep Learning is continuing to thrive. With the recent advancements in data science and AI, more time and energy is being pumped into research of the industry. As a subset of machine learning concerned with algorithms and artificial neural networks, it is widely being used in tasks such as image classification, object detection, and face recognition.
As were experiencing the 4th industrial revolution, deep learning algorithms are allowing us to learn from data the same way humans do. We are seeing more self-driving cars on the roads, fraud detection tools, virtual assistants, healthcare predictive modeling, and more.
2023 has proven to show the works of deep learning through automated processes, robotics, blockchain, and various other technologies.
With all these that are happening, you must think these computers are pretty tired right? In order to meet the advancements of AI and data science, companies require computers and systems that can help to support them. Edge computing brings computation and data storage closer to the sources of data. When working with these advanced models, edge computing provides real-time data processing and allows for smooth communication between all devices.
For example, when LLMs were getting released every two seconds, it was obvious that organizations would require effective systems such as edge computing to be successful. Google released TPU v4 this year - computing resources that can handle the high computational needs of machine learning and artificial intelligence.
Due to these advancements, we are seeing more organizations move from the cloud to edge to fit their current and future requirements.
A lot has been happening, and its been happening in a short period of time. Its becoming very difficult for organizations such as the government to keep up. Governments from around the world are raising the question of how do these AI applications affect the economy and society, and what are the implications?.
People are concerned about the bias and discrimination, privacy, transparency, and security of these AI and data science applications. So what are the ethical aspects of AI and data science, and what should we expect in the future?
We already have the European AI Act pushing a framework that groups AI systems into 4 risk areas. OpenAI CEO Sam Altman testified about the concerns and possible pitfalls of the new technology at a US Senate committee on Tuesday the 16th. Although there are a lot of advancements happening in a short period of time, a lot of people are concerned. Over the next 6 months we can expect a few more laws getting passed and regulations and frameworks being put into place.
If you havent been keeping up with AI and data science in the last 6 months, I hope this article has provided you with a quick breakdown of whats been going on. It will be interesting to see over the next 6 months how these advancements get embraced whilst being able to ensure responsible and ethical use of these technologies.Nisha Arya is a Data Scientist, Freelance Technical Writer and Community Manager at KDnuggets. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.
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The First Half of 2023: Data Science and AI Developments - KDnuggets
#HackInSAS: Utilizing AI/ML and Data Science to Address Today’s … – EPAM
In April, EPAMs data science experts participated in SASs 2023 Global Hackathon and received multiple awards: as a global technology winner for natural language processing (NLP) and as a regional winner for EMEA. With global participation from the brightest data scientists and technology enthusiasts, SAS hackathons look to tackle some of the most challenging, real-world business and humanitarian issues by applying data science, AI and open source cloud solutions. Teams have just one month to define a problem, collect the data and deliver a POC. This year, two EPAM teams participated in the event:
Lets dive deeper into these two exciting innovations
Social Listening for Support Services in Case of Disasters
EPAM Senior Data Scientists Leonardo Iheme and Can Tosun partnered with Linktera to create a tool to help decision makers in disaster relief coordination centers make data-driven, informed decisions. The solution harnesses the power of NLP and image analysis to turn the disruption of a natural disaster into actionable insights for coordination centers.
Just prior to the hackathon, Turkeys magnitude 4.9 earthquake struck resulting in serious questions about how to improve response to natural disasters. We wanted to help and worked with Linktera in Turkey to do just that. The goal is to streamline the decision-making process by providing data-backed decisions, so that resources can be allocated effectively for rapid response to critical situations.
Mining, Categorizing & Validating Social Media with NLP
This concept mines social media platforms, like Twitter, for real-time data, transforming this wealth of information into insights for disaster relief. In todays connected world, social media has become an essential tool for communication, where people share their experiences, seek help and stay informed. The system features advanced algorithms to filter out misinformation and validate crucial details. The goal is to not only provide verified information to coordination centers but also paint a clearer picture of the situation on the ground.
Applying NLP, we analyzed 140,000 tweets from Turkeys earthquake to identify the intent of help requests and classify them into relevant categories. To pinpoint the location of those in need, we used named entity recognition to extract addresses from tweets. We then used the Google Maps API to convert these textual descriptions into precise coordinates for mapping.
Assessing Infrastructure Damage with Machine Learning
After the earthquake, satellite image providers quickly made their highly valuable resources publicly available. This is a vital data source to validate and enrich complex social media data, which can help to understand the full extent of the disaster, the infrastructure damage, areas impacted, collapsed buildings and blocked routes that would otherwise hinder emergency response. Using advanced CV techniques, we compared satellite imagery before and after the earthquake. This methodology involves precise location identification, preprocessing of the satellite images and identifying damaged structures using advanced object detection and segmentation models.
To identify buildings within the geospatial data, we utilized a pretrained, deep learning object detection model, like the open source YOLO V5 architecture. This offers high accuracy and efficiency in detecting structures. Additionally, the team leveraged the latest segmentation model from Meta AI to delineate buildings and assess the degree of damage. This empowers stakeholders to make informed decisions with information from our satellite image processing that displays the locations and percentage of damage to detect buildings and identify blocked roads.
Building Data Visualizations for Disaster Relief
You need the right tools to make sense of the data. The PoC has a comprehensive and user-friendly dashboard for the disaster coordination centers to streamline their decision-making process and facilitate effective communication among various teams. The dashboard gathers data from the NLP and image analysis techniques and aggregates it into a single platform with an interactive map that pinpoints the locations of the affected areas, specific requirements and relevant labels to allow decision makers to quickly assess and prioritize their response. It also includes a layer dedicated to satellite imagery to visualize and assess the extent of the damage.
We hope that this design concept extends beyond Turkeys earthquake to all natural disaster relief efforts.
Mobility Insights Heidelberg A Digital Twin to Model Urban Traffic Flow
I joined EPAM Data Scientists Denis Ryzhov and Dzmitry Vabishchewich, alongside Digital Agentur Heidelberg and Fujitsu, to develop a digital twin of Heidelberg, Germany. Heidelberg is a popular tourist destination, which attracts 14 million visitors annually, almost 100 times its population. Predicting traffic and pedestrian flow by using data from IoT monitoring devices while considering the impact of weather is key to run the city smoothly and effectively. These predictions can enhance tourist experiences and safety, prevent accidents, improve planning for road closures for major events and help with future city planning and development. This is an active area of interest among many cities but dont have their own data science and technology experience in-house to accomplish this task alone.
Impacting Weather on Traffic Flow
For the first modeling initiative, the team wanted to understand and predict how weather patterns will impact the flow of traffic in the city. IoT sensor data included a central traffic light control system, cameras on traffic lights, parking garage sensors, bicycle count sensors and pedestrian count sensors. The team used time, weather and city event data to generate a decision tree and further improve the model by partitioning and using gradient boosting.
Predicting Parking Space Availability
For the teams second modeling initiative, the goal was to add a predictive parking availability function to guide motorists to parking spaces. By the end of the hackathon, the models were available on the citys website to provide long-term prediction of parking availability. It was modeled in parallel with the Python and SAS model builder using random forests for modeling. The short-term model learns patterns of occupancy from lag features. Further improvement was achieved by extrapolating a curve in response to unprecedented patterns. The model was further improved when weather and event data were added.
Forecasting Short-Term Traffic
For the third modeling initiative, the team looked at traffic flows in the city and generated short-term traffic predictions for the city to forecast traffic within the next three hours. The team performed a successful experiment in creating a model to predict traffic at each sensor location within the city, demonstrating the possibility of attaining high quality models from multiple locations. This stream was particularly challenging due to some of the gaps in the data, which we overcame by carefully selecting the analysis tools and techniques and filling gaps where necessary. The team used a light gradient boosting machine (LGBM) tree-based model for time series problems, so lagging features and rolling windows statistics are the best and quickest model to implement.
Conclusion
EPAMs proud of our hackathon teams and their award recognition from SAS. The teams delivered exciting PoC innovations using the latest AI technologies and data science practices to deliver on todays most challenging, real-world business and humanitarian issues. As always, we hope to inspire and foster technology innovations, and look forward to another great competition in next years #HackInSAS.
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#HackInSAS: Utilizing AI/ML and Data Science to Address Today's ... - EPAM