Abstract Submission Deadline
December 7-9,2023
Madrid, Spain
Swathi Arulguppe Nagendra
BetterMeal AI, USA
Johannes Castner
Ancoris, UK
Boumegnane Abdelkrim
Casablanca, Morocco
GREETINGS!!!
It is a great pleasure and honor to invite everybody to participate in " 8th International Conference on Data Science and Machine Learning" which will be held on December 07-09,2023 in Madrid, Spain.
Data science conferences 2023 conference brings scientists, researchers, tech professionals and, industries, top universities, and educational centers from all over the world to make this event unforgettable in a great way and also this year we came up with the new theme “Machine intelligence is the last invention that humanity will ever need to make”.We welcome you to join us and be a part of our knowledge and respect to our conference.
Data science conferences 2023 going to be a three days conference that offers the chance to attend a fantastic keynote presentation conducted by the top speakers with the addition of superb workshops and symposiums by professionals. It is a best top opportunity for the employees of the universities and also other organization to team up with foreign scholars, as well as for business candidates looking to expand their career in the global market in world trade.
It’s a great aspect of gathering many researchers and industry people to be part of the discussion and presentation by top experts. Young researchers will deserve better opportunities to explore their ideas by attending the conference. Take this is the best opportunity to build your career in future. Make a note in your calendar to participate with the speakers..
I add my advance best wishes to entire team for a successful conference and thanks to speakers. Keep in touch with us till entire conference.
We are looking forward to welcoming you to Madrid, Spain.
BEST REGARDS
Data science 2023
The Data science 2023 is going to bring together academics, researchers, and others from various fields related to Data science conferences 2023and machine learning. We are going to discuss the topics such as artificial intelligence and machine learning, data structures and algorithms, bioinformatics, and scientific computing.
In combination with a keynote forum, workshops meet organizational, a young researcher’s symposium, poster presentations, and panel discussions, the Data science 2023 will also include a presentation discussion board. Mode of participation will be speaker, delegate, exhibitor, sponsor . On December 07-09, 2023, the Data science 2023 conference will take place. We respectfully ask that each of you support the success of our event by attending.
Benefits of attending the conference
Advantages of Participating in our Conference
Benefits of Participation for Speaker
The benefit of the Association for Collaborators
Session 1: Data Science
Data science is the study that combines domain knowledge, programming abilities, and math and statistics understanding to extract useful insights from data. Machine learning algorithms are used with numbers, text, photos, video, audio, and other data to create artificial intelligence (AI) systems that can execute jobs that would normally need human intellect. As a result, these systems produce insights that analysts and business users may employ to create meaningful commercial value.
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Related Societies and Association:
Special Interest Group on Management of Data (SIGMOD) | Web Analytics Association (WAA) | Special Interest Group on Knowledge Discovery in Data and Data Mining (SIGKDD) | SF Bay ACM Data Mining SIG | European Knowledge Discovery Network of Excellence (KDNet) | IEEE International Conference on Data Mining (ICDM) | Data Mining Section of INFORMS | Association of Data Scientists (ADASci)
Session 2: Machine Learning
Machine learning is a branch of artificial intelligence (AI) that allows computers to learn and develop on their own without having to be explicitly programmed. Machine learning is concerned with the creation of computer programmes that can access data and learn on their own.
The learning process starts with observations or data, such as examples, direct experience, or instruction, so that we may seek for patterns in data and make better judgments in the future based on the examples we offer. The fundamental goal is for computers to learn on their own, without the need for human involvement, and to change their behaviour accordingly.
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Related Societies and Association:
Special Interest Group on Management of Data (SIGMOD) | Web Analytics Association (WAA) | Special Interest Group on Knowledge Discovery in Data and Data Mining (SIGKDD) | SF Bay ACM Data Mining SIG | European Knowledge Discovery Network of Excellence (KDNet) | IEEE International Conference on Data Mining (ICDM) | Data Mining Section of INFORMS | Association of Data Scientists (ADASci)
Session 3: Artificial intelligence
In contrast to the natural intelligence displayed by humans and other animals, automated thinking is data generated by machines or software produced by computers. AI research is quite specific and focused, and it is fundamentally divided into subfields that frequently dislike interacting with one another. It establishes well-behaved Creative capacity, Artificial Neural Structures, Adaptive Systems, Cybernetics, and Knowledge Sharing.
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Related Societies and Association:
Special Interest Group on Management of Data (SIGMOD) | Web Analytics Association (WAA) | Special Interest Group on Knowledge Discovery in Data and Data Mining (SIGKDD) | SF Bay ACM Data Mining SIG | European Knowledge Discovery Network of Excellence (KDNet) | IEEE International Conference on Data Mining (ICDM) | Data Mining Section of INFORMS | Association of Data Scientists (ADASci)
Session 4: Big Data
Big Data is a collection of statistics that is large in volume yet grows rapidly over time. It is a statistic of such enormous length and complexity that no ordinary statistics control equipment can effectively store or process it. Big statistics are similar to statistics, except they are much longer. Experimentation and inquiry are required to effectively exploit the benefits of Big Data. The researcher can cover Big Data drivers, features, kinds, problems, and applications of Hadoop in Big Data during this session.
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Related Societies and Association:
Special Interest Group on Management of Data (SIGMOD) | Web Analytics Association (WAA) | Special Interest Group on Knowledge Discovery in Data and Data Mining (SIGKDD) | SF Bay ACM Data Mining SIG | European Knowledge Discovery Network of Excellence (KDNet) | IEEE International Conference on Data Mining (ICDM) | Data Mining Section of INFORMS | Association of Data Scientists (ADASci)
Session 5: Data Analytics
Data Analytics examines and analyzes huge amounts of data, i.e Big data to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can help companies make more informed business decisions. Software can pave the way for Data Analytics to deliver various business benefits, including new revenue opportunities, more effective marketing, improved operational efficiencies, competitive advantages, and better customer service
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Related Societies and Association:
Special Interest Group on Management of Data (SIGMOD) | Web Analytics Association (WAA) | Special Interest Group on Knowledge Discovery in Data and Data Mining (SIGKDD) | SF Bay ACM Data Mining SIG | European Knowledge Discovery Network of Excellence (KDNet) | IEEE International Conference on Data Mining (ICDM) | Data Mining Section of INFORMS | Association of Data Scientists (ADASci)
Session 6: Information Technology
Information technology (IT) is the use of computers to cause, process, store, recover, and exchange all kinds of electronic data and information. IT is generally used within the factor of business operations as opposed to personal or entertainment technologies IT is considered to be a subset of information and communications technology (ICT). An information technology system (IT system) is generally an information system, a conveying system, or, more clearly speaking, a computer system including all hardware, software, and outlying equipment. The word is commonly utilized as an equivalent word for PCs and PC systems; however it also includes other data allocation advances, for instance, TV and phones. A few things or administrations interior an economy are related with data alteration, including PC equipment, programming, hardware, photonic, web, and internet business.
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Related Societies and Association:
Special Interest Group on Management of Data (SIGMOD) | Web Analytics Association (WAA) | Special Interest Group on Knowledge Discovery in Data and Data Mining (SIGKDD) | SF Bay ACM Data Mining SIG | European Knowledge Discovery Network of Excellence (KDNet) | IEEE International Conference on Data Mining (ICDM) | Data Mining Section of INFORMS | Association of Data Scientists (ADASci)
Session 7: Scientific Computing
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Related Societies and Association:
Special Interest Group on Management of Data (SIGMOD) | Web Analytics Association (WAA) | Special Interest Group on Knowledge Discovery in Data and Data Mining (SIGKDD) | SF Bay ACM Data Mining SIG | European Knowledge Discovery Network of Excellence (KDNet) | IEEE International Conference on Data Mining (ICDM) | Data Mining Section of INFORMS | Association of Data Scientists (ADASci)
Session 8: Natural Language Processing
Related Societies and Association:
Special Interest Group on Management of Data (SIGMOD) | Web Analytics Association (WAA) | Special Interest Group on Knowledge Discovery in Data and Data Mining (SIGKDD) | SF Bay ACM Data Mining SIG | European Knowledge Discovery Network of Excellence (KDNet) | IEEE International Conference on Data Mining (ICDM) | Data Mining Section of INFORMS | Association of Data Scientists (ADASci)
Session 9: Cloud Computing
Cloud computing makes computer resources and services available on demand. Users may quickly create the infrastructure they need, including computing instances and cloud-based storage resources, link cloud services, upload datasets, and run analytics in the cloud. Users can commit virtually unlimited resources to the public cloud, use them for as long as they're needed, and then ignore the environment, paying only for the resources and services they actually use.
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Related Societies and Association:
Special Interest Group on Management of Data (SIGMOD) | Web Analytics Association (WAA) | Special Interest Group on Knowledge Discovery in Data and Data Mining (SIGKDD) | SF Bay ACM Data Mining SIG | European Knowledge Discovery Network of Excellence (KDNet) | IEEE International Conference on Data Mining (ICDM) | Data Mining Section of INFORMS | Association of Data Scientists (ADASci)
Robotics may be a field that deals with manufacturing humanoid machines which will act like humans and perform some steps like citizenry. Now, robots can act like humans in firm circumstances, but can they trust like humans as well, this is frequently where AI comes in! Artificial intelligence allows robots to act discreetly in firm situations. These robots could also be ready to work out problems during a restricted sphere or perhaps learn in controlled environments.
Related: Data Science conferences | Machine learning Congress | Machine learning conference | Data Science Congress | Machine Learning Summit | Data Science Forum | Machine Learning Forum | Data Science Summit
Related Societies and Association:
Special Interest Group on Management of Data (SIGMOD) | Web Analytics Association (WAA) | Special Interest Group on Knowledge Discovery in Data and Data Mining (SIGKDD) | SF Bay ACM Data Mining SIG | European Knowledge Discovery Network of Excellence (KDNet) | IEEE International Conference on Data Mining (ICDM) | Data Mining Section of INFORMS | Association of Data Scientists (ADASci)
Session 11: Deep Learning
Deep learning is a sort of Machine Learning training model that simulates human brain's decision-making process. By "brain," I mean algorithms that are more complex. Multiple sophisticated layers are employed for processing instead of a single one. A neural network is a system that permits layers to communicate with one another. Because it is automated, this technique is closer to unsupervised learning.
Related: Data Science conferences | Machine learning Congress | Machine learning conference | Data Science Congress | Machine Learning Summit | Data Science Forum | Machine Learning Forum | Data Science Summit
Related Societies and Association:
Special Interest Group on Management of Data (SIGMOD) | Web Analytics Association (WAA) | Special Interest Group on Knowledge Discovery in Data and Data Mining (SIGKDD) | SF Bay ACM Data Mining SIG | European Knowledge Discovery Network of Excellence (KDNet) | IEEE International Conference on Data Mining (ICDM) | Data Mining Section of INFORMS | Association of Data Scientists (ADASci)
Session 12: Netural Networks
A neural network is an artificial intelligence strategy for teaching computers to analyse data in the same manner that the human brain does. Deep learning is a form of machine learning technique that employs linked nodes or neurons in a layered structure to mimic the human brain.A neural network is a set of algorithms that attempts to detect underlying relationships in a batch of data using a method that mimics how the human brain works. In this context, neural networks are systems of neurons that might be biological or artificial.
Related: Data Science conferences | Machine learning Congress | Machine learning conference | Data Science Congress | Machine Learning Summit | Data Science Forum | Machine Learning Forum | Data Science Summit
Related Societies and Association:
Special Interest Group on Management of Data (SIGMOD) | Web Analytics Association (WAA) | Special Interest Group on Knowledge Discovery in Data and Data Mining (SIGKDD) | SF Bay ACM Data Mining SIG | European Knowledge Discovery Network of Excellence (KDNet) | IEEE International Conference on Data Mining (ICDM) | Data Mining Section of INFORMS | Association of Data Scientists (ADASci)
Session 13: Computer Vision
Computer vision is a combination of Computer science and artificial intelligence. Almost every device we use in our day-to-day life has computer-vision technology. Google uses this technology to search objects and scenes. One of the best applications of Computer Vision is Facial recognition. Many popular companies like Apple and Facebook use this technology. To know more about the session, kindly join Data Science 2023
Related: Data Science conferences | Machine learning Congress | Machine learning conference | Data Science Congress | Machine Learning Summit | Data Science Forum | Machine Learning Forum | Data Science Summit
Related Societies and Association:
Special Interest Group on Management of Data (SIGMOD) | Web Analytics Association (WAA) | Special Interest Group on Knowledge Discovery in Data and Data Mining (SIGKDD) | SF Bay ACM Data Mining SIG | European Knowledge Discovery Network of Excellence (KDNet) | IEEE International Conference on Data Mining (ICDM) | Data Mining Section of INFORMS | Association of Data Scientists (ADASci)
Session 14: Data Science and Coding
Coding and Data Science can be used for structuring websites, data analysis, machine learning, structuring data pipelines, visualization, and much more.Your aim with commanding to code as a prospective data scientist will be to Read and take notes on facts from various derivations. Work with various data types Coding, often known as computer programming, is the method through which we communicate with computers. Writing code is similar to writing a set of instructions since it informs a machine what actions to do. You can teach computers what to do or how to respond considerably more quickly if you learn to write code. You can use this talent to make websites and apps, manipulate data, and a whole lot more.
Related: Data Science conferences | Machine learning Congress | Machine learning conference | Data Science Congress | Machine Learning Summit | Data Science Forum | Machine Learning Forum | Data Science Summit
Related Societies and Association:
Special Interest Group on Management of Data (SIGMOD) | Web Analytics Association (WAA) | Special Interest Group on Knowledge Discovery in Data and Data Mining (SIGKDD) | SF Bay ACM Data Mining SIG | European Knowledge Discovery Network of Excellence (KDNet) | IEEE International Conference on Data Mining (ICDM) | Data Mining Section of INFORMS | Association of Data Scientists (ADASci)
Session 15: Algorithm of Data Science
A set of abilities is required to apply Data Science to every challenge. A critical component of this skill set is machine learning. You must be familiar with the many Machine Learning algorithms used to respond to various sorts of issues in order to undertake Data Science, as a single method cannot be the best for all types of use cases. These algorithms extract a request from the dataset under consideration for various tasks like as prediction, classification, and meetings.
Related: Data Science conferences | Machine learning Congress | Machine learning conference | Data Science Congress | Machine Learning Summit | Data Science Forum | Machine Learning Forum | Data Science Summit
Related Societies and Association:
Special Interest Group on Management of Data (SIGMOD) | Web Analytics Association (WAA) | Special Interest Group on Knowledge Discovery in Data and Data Mining (SIGKDD) | SF Bay ACM Data Mining SIG | European Knowledge Discovery Network of Excellence (KDNet) | IEEE International Conference on Data Mining (ICDM) | Data Mining Section of INFORMS | Association of Data Scientists (ADASci)
Session 16: Data Warehousing and Cybersecurity
Cybersecurity will always be a top priority for businesses. Cybersecurity is largely concerned with data privacy protection. For diverse purposes, they employ data mining techniques. Database analysis, text analysis, and other techniques are among them. We also offer a number of online tools, like Rapid Miner, orange, NTLK, and others. Malware detection and fraud detection are two examples of applications. Come to Data Science 2023 with us.
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Related Societies and Association:
Special Interest Group on Management of Data (SIGMOD) | Web Analytics Association (WAA) | Special Interest Group on Knowledge Discovery in Data and Data Mining (SIGKDD) | SF Bay ACM Data Mining SIG | European Knowledge Discovery Network of Excellence (KDNet) | IEEE International Conference on Data Mining (ICDM) | Data Mining Section of INFORMS | Association of Data Scientists (ADASci)
Session 17: Information Science
Related Societies and Association:
Special Interest Group on Management of Data (SIGMOD) | Web Analytics Association (WAA) | Special Interest Group on Knowledge Discovery in Data and Data Mining (SIGKDD) | SF Bay ACM Data Mining SIG | European Knowledge Discovery Network of Excellence (KDNet) | IEEE International Conference on Data Mining (ICDM) | Data Mining Section of INFORMS | Association of Data Scientists (ADASci)
The true Data mining work is a self-loader or programmed examination of massive volumes of data to separate out ambiguous, fascinating information, such as meetings of information records (group research), odd records (asymmetry discovery), and constraints (affiliation rule mining, consecutive example mining). This usually entails using database techniques like dimensional files. These samples might then be used as a kind of information overview for subsequent research or, for example, in Artificial Intelligence and prognostic analysis. For example, the information mining process may identify diverse groups of data, which could subsequently be used by a choice emotionally supporting network to provide increasingly precise forecast findings.
Related: Data Science conferences | Machine learning Congress | Machine learning conference | Data Science Congress | Machine Learning Summit | Data Science Forum | Machine Learning Forum | Data Science Summit
Related Societies and Association:
Special Interest Group on Management of Data (SIGMOD) | Web Analytics Association (WAA) | Special Interest Group on Knowledge Discovery in Data and Data Mining (SIGKDD) | SF Bay ACM Data Mining SIG | European Knowledge Discovery Network of Excellence (KDNet) | IEEE International Conference on Data Mining (ICDM) | Data Mining Section of INFORMS | Association of Data Scientists (ADASci)
Session 19: Neurocomputing
Neurocomputing is the branch of science and engineering, which is based on human like intelligent behaviors of machines. It is a vast discipline of research that mainly includes neuroscience, machine learning, searching and knowledge representation. The traditional rule-based learning is now appears to be inadequate for various engineering applications because it is incompetent to serve increasing demand of machine learning when dealing with large amount of data.
Related: Data Science conferences | Machine learning Congress | Machine learning conference | Data Science Congress | Machine Learning Summit | Data Science Forum | Machine Learning Forum | Data Science Summit
Related Societies and Association:
Special Interest Group on Management of Data (SIGMOD) | Web Analytics Association (WAA) | Special Interest Group on Knowledge Discovery in Data and Data Mining (SIGKDD) | SF Bay ACM Data Mining SIG | European Knowledge Discovery Network of Excellence (KDNet) | IEEE International Conference on Data Mining (ICDM) | Data Mining Section of INFORMS | Association of Data Scientists (ADASci)
Related Societies and Association:
Special Interest Group on Management of Data (SIGMOD) | Web Analytics Association (WAA) | Special Interest Group on Knowledge Discovery in Data and Data Mining (SIGKDD) | SF Bay ACM Data Mining SIG | European Knowledge Discovery Network of Excellence (KDNet) | IEEE International Conference on Data Mining (ICDM) | Data Mining Section of INFORMS | Association of Data Scientists (ADASci)
Data Science Market Analysis
During the projection period, the Data Science Platform market is expected to increase from USD 95.3 billion in 2021 to USD 322.9 billion in 2026, with a Compound Annual Growth Rate (CAGR) of 27.7%. The Data Science Platform industry is driven by the astounding growth of big data, but also by the rising adoption of cloud-based solutions, the growing use of data science platforms in various industries, and the growing need to extract in-depth insights from voluminous data to gain a competitive advantage.
Due to the rise of social media, IoT, and multimedia, which have produced an overwhelming flow of data in either structured or unstructured format, the volume of data gathered by enterprises is constantly expanding. For example, in the last two years alone, about 90% of the world's data has been created. Machine-generated as well as human-generated data is growing at a rate ten times faster than traditional commercial data. Machine data, for example, is growing at a 50-fold quicker rate than human data. Consumer data is generally consumer-driven and oriented. The majority of the world's data is generated by consumers, who are increasingly 'always-on.' Most people these days spend 4–6 hours per day using a range of gadgets and (social) applications to consume and generate data. New data is created in a database somewhere around the world with every click, swipe, or communication. Because everyone now has a smartphone in their pocket, the amount of data created is staggering.
Machine Learning Market Analysis
The global Machine Learning Market is estimated to grow over the forecast period due to the rising usage of technological developments in various industries such as healthcare, automotive, retail, and manufacturing. This information is published in a research named "Machine Learning (ML) Market, 2022-2029" by Fortune Business InsightsTM. Machine learning (ML) market size was USD 15.44 billion in 2021, according to the analysis. During the projection period, the market is estimated to grow at a CAGR of 38.8%, from USD 21.17 billion in 2022 to USD 209.91 billion in 2029.
Machine learning is a method of data analysis that automates the creation of analytical models. During the forecast period, the market is estimated to be driven by increasing adoption of artificial intelligence and machine learning technologies. Deep learning is a subset of artificial intelligence that is expected to lead the industry in the next years as learning capabilities improve.
