DATASCIENCE CONGRESS 2024

Data Science Conference 2024, Machine learning conferences, Data Science 2024 conferences, Data science congress, Artificial Intelligence conferences, Domain Expertise, Humanoid Machines, Cybernetics, Analytic System, Applied statistics, Internet Business

Renowned Speakers

Gaurav Pawar

Mobisy Technologies Pvt Ltd India

Mr. Åukasz Augustyniak

WrocÅ‚aw University of Science and Technology Poland

Shabir Momin

Zenga TV Singapore

Caio Moreno

Complutense University of Madrid Spain

Samuel Rochette,

OCTO Technology, France France

Abdul Rasheed

CMR Institute of Technology, Bengaluru India

Mr. Alec John Burns

University College London UK

Semakula Abdumajidhu

Makerere University Uganda

Recommended Global EEE & Engineering Webinars & Conferences

Europe & UK

Asia Pacific & Middle East

Canada

Big Data 2025, Canada

About Conference

DATASCIENCE CONGRESS 2024 is a fantastic event that will bring together people from various domains of data science and machine learning, such as experimenters, analysts, academicians, and others, to discuss topics related to data science, machine learning, artificial intelligence, algorithms, and bioinformatics, Robotics in data sciences, big data analytics, data visualization, and presentation.

We invite all honorable speakers, delegates, exhibitors, sponsors, researchers, experts, experimenters, officials, moderators, experimenters, and students to attend the DATASCIENCE CONGRESS 2024 on science and machine learning, which will be held on February 08-09, 2024 in Madrid, Spain. The event invites all researchers to participate in this data science and machine learning conference.

Session & Tracks

Track 01: Data Science

Data science is defined as a "concept that unifies statistics, data analysis, informatics, and their related methods" in order to "understand and analyze actual phenomena" using data. It employs techniques and theories from a variety of disciplines, including mathematics, statistics, computer science, information science, and domain knowledge.

As a result, data science differs from both computer science and information science. During his acceptance speech, Award winner Jim Gray described data science as a "fourth paradigm" of science (empirical, theoretical, estimation, and now data-driven), claiming that "everything about science is changing because of the impact of information technology" and the data deluge.

Track 02: Big Data Analytics

Enormous information investigation challenges incorporate capturing information, information capacity, information investigation, look, sharing, exchange, visualization, questioning, overhauling, data security, and information source. Huge information was initially related with three key concepts: volume, assortment, and speed. The examination of enormous information presents challenges in testing, and in this way already permitting for as it were perceptions and examining. In this way a fourth concept, veracity, alludes to the quality or insightfulness of the information.

Without adequate speculation in mastery for enormous information veracity, at that point the volume and assortment of information can deliver costs and dangers that surpass an organization's capacity to make and capture esteem from enormous information. Current utilization of the term enormous information tends to allude to the utilize of prescient analytics, client behavior analytics, or certain other progressed information analytics strategies that extricate esteem from enormous information, and rarely to a specific measure of information set.

Track 03: Information Technology

Data innovation (IT) is the utilize of computers to form, handle, store, recover and trade all sorts of information and data. IT shapes portion of data and communications innovation (ICT). The term data innovation in its present day sense to begin with showed up in a 1958 article distributed within the Harvard Trade Audit; creators Harold J. Leavitt and Thomas L. Whisler commented that "the unused innovation does not however have a single set up title. We might call it data innovation (IT)."

Track 04: Artificial Intelligence

Manufactured insights (AI) is intelligence—perceiving, synthesizing, and deducing information—demonstrated by machines, as contradicted to insights shown by non-human creatures and people. As machines ended up progressively able, assignments considered to require "insights" are regularly expelled from the definition of AI, a marvel known as the AI impact. For occurrence, optical character acknowledgment is regularly prohibited from things considered to be AI, having ended up a schedule innovation.

Track 05: Robotics in data science

Information Science like Mechanical technology is an intrigue field where logical strategies, insights, numerical calculations, and computer frameworks come together to work on gigantic sets of organized and unstructured information to infer perhaps a arrangement to an continuous issue, find a future shopping slant, anticipate conceivable future trade dangers and so on. Information Science, once more like Mechanical technology, depends on Fake Insights and Machine Learning broadly to come at their ‘actionable insights’ for an cluster of applications. Future doesn’t basically need to be a diversion of chance and can be based on data-driven truths.

Track 06: Cloud computing

Cloud computing is the on-demand access to computing resources—applications, servers (physical and virtual), data storage, development tools, networking capabilities, and more—hosted at a remote data centre managed by a cloud services provider via the internet (or CSP). The CSP makes these resources available for a monthly subscription fee or charges a usage fee. Data science is defined as a "concept that unifies statistics, data analysis, informatics, and their related methods" in order to "understand and analysis actual phenomena" using data. It employs techniques and theories from a variety of disciplines, including mathematics, statistics, and computer science, information science, and domain knowledge. Data science, on the other hand, is distinct from computer science and information science.

Track 07: Machine Learning

Machine learning (ML) may be a field of request given to understanding and building strategies that "learn" – that’s, strategies that use information to make strides execution on a few set of errands. It is seen as a portion of manufactured insights. A subset of machine learning is closely related to computational measurements, which centers on making expectations utilizing computers, but not all machine learning is factual learning. The consider of scientific optimization conveys strategies, hypothesis and application domains to the field of machine learning. Information mining could be a related field of consider, centering on exploratory information investigation through unsupervised learning.

Track 08: Cyber security

Computer security, cybersecurity (cyber security), or data innovation security (IT security) is the assurance of computer frameworks and systems from assault by pernicious performing artists which will result in unauthorized data revelation, burglary of, or harm to equipment, computer program, or information, as well as from the disturbance or confusion of the administrations they provide. Cybersecurity is one of the foremost noteworthy challenges of the modern world, due to both the complexity of data frameworks and the social orders they bolster. Security is of particularly tall significance for frameworks that administer large-scale frameworks with far-reaching physical impacts, such as control dissemination, races, and fund.

Track 09: Artificial neural network

Counterfeit neural systems (ANNs), ordinarily essentially called neural systems (NNs) or neural nets, are computing frameworks propelled by the natural neural systems that constitute creature brains. A fake neuron gets signals at that point forms them and can flag neurons associated to it. The "flag" at an association may be a genuine number, and the yield of each neuron is computed by a few non-linear work of the whole of its inputs.

Track 10: Human-Robot Interaction

Human–robot interaction has been a subject of both science fiction and scholastic hypothesis indeed some time recently any robots existed. Since much of dynamic HRI advancement depends on natural-language preparing, numerous viewpoints of HRI are continuations of human communications, a field of investigate which is much more seasoned than mechanical technology. Human robots (machines which copy human body structure) are superior depicted by the biomimetics field, but cover with HRI in numerous inquire about applications. Illustrations of robots which illustrate this slant incorporate Willow Garage's PR2 robot, the NASA Robonaut, and Honda ASIMO. Be that as it may, robots within the human–robot interaction field are not constrained to human-like robots: Paro and Kismet are both robots outlined to inspire passionate reaction from people, and so drop into the category of human–robot interaction.

Market Analysis

Technological advancements are happening quickly because to increased investment in research and development. The need for technology that can boost businesses' productivity and efficiency is increasing as they expand. Technologies like artificial intelligence (AI), machine learning (ML), and the internet of things (LOT) are everywhere, and their acceptance is accelerating the use of platforms and software. Modern data handling systems and solutions play a significant role in the expansion of businesses as data volumes increase daily. One such piece of software that is currently being used extensively by industries is the data science platform. This programme is made up of a number of different technologies for machine learning and advanced analytics. It gives data scientists the ability to create methods, draw conclusions from data, and share those experiences throughout a project in a solitary setting. Various tools created for each stage of the data modeling process are used in data science initiatives.

Data science platforms are currently being used more and more. The programme offers open-source tools significant flexibility and scalability of computing resources. Additionally, it is simple to align with different data architectures. In addition, the platform supports version control, enabling the data science team to collaborate on projects without losing recently completed work. Such advantages significantly aid market expansion. The assumptions established by the businesses throughout the data analysis are being revised. New cycles are developing. Current market developments include the replacement of data, changes in traffic, and an emphasis on supply chains connected to healthcare. The healthcare industry has been most negatively impacted by the epidemic. Healthcare industry experts are concentrating on using data from nations that were previously affected by the epidemic to make more accurate conclusions.

To Collaborate Scientific Professionals around the World

Conference Date February 08-09, 2024

For Sponsors & Exhibitors

sponsor@conferenceseries.com

Speaker Opportunity

Poster Opportunity E-Poster Opportunity

Useful Links

Past Conference Report

DATASCIENCE CONGRESS 2023 Conference

Supported By

All accepted abstracts will be published in respective Conference Series International Journals.

Abstracts will be provided with Digital Object Identifier by

Data Science and Machine Learning

February 08-09, 2024 Madrid, Spain

A Creative Perspective On The New Developments In Data Science

DATASCIENCE CONGRESS 2024 | Machine learning Congress | Data science Congress | Machine learning conferences | Robotics in Data Science Conference | Upcoming Meetings on Technologies| Madrid | Spain | Middle East | Asia Pacific

Conference Series Conferences gaining more Readers and Visitors