Data science and machine learning is a chance to meet others within specialty to network and learn the latest Technology and Applications. It is an opportunity to get understanding from experience professors, experimenter and scientists. Attending this conference will be obliging to expand your knowledge and find solutions to problems and to present your schemes and experimentation work to others.

• It’s a very good opportunity to connect with the hosts and fellow attendees
• Reach the target the group directly and interchange the views of each other
• Wider reach to across the world

Target Audience

Speakers                    

System integrator

Researchers

Analysts

Telecommunication provider

Delegates

Exhibitor

Media representatives

Investment professionals

Sponsors

Students

Track 01: Data science

Data science is the subject of study that integrates domain expertise, programming skills, and knowledge of mathematics and statistics to bring out meaningful insights from data. Data science advocates apply machine learning algorithms to numbers, text, figure, video, audio, and more to manufacture artificial intelligence (AI) systems to perform tasks that ordinarily needed human intelligence. In turn, these systems create insights which analysts and business users can interpret into tangible business value.

Track 02: Machine learning

Machine learning is a supplication of artificial intelligence (AI) that provides systems the ability to automatically learn and better from happening without being exact programmed. Machine learning is center of attention the expansion of computer programs that can approach data and use it to learn for individually. The process of learning starts with observations or data, such as examples, shortest experience, or instruction, in order to look for plan in data and make better decisions in the future founded on the examples that we provide. The primary aim is to allow the computers learn automatically without human interfere and adjust actions accordingly.

Track 03: Artificial intelligence

Automated thinking is the data performed by machines or software decorated by machines, in contrast to the natural intelligence showed by humans and another animal. AI examination is amazingly, particular and focus, and is essentially isolated into subfields that a considerable part of the time hatred to chat with each other. It solidifies mannered Creative capacity, Artificial Neural Structures, Adaptive Systems, Cybernetics and Knowledge sharing

Track 04: Robotics in data science

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! AI 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.

Track 05: Big data analytics

Big data analytics probe and inspect huge amounts of data to i.e., big data - to uncover hidden drawings, unknown co-relations, market trends, customer preferences and other functional details that can help organizations make more-knowledgeable business decisions. Operate and transfer by differentiated analytics systems and software, big data analytics can lay the way to various business benefits, including new income chances, more effective marketing, improved operational ability, competitive advantages and senior customer service

Track 06: 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

Track 07: Data mining and statistical analysis

The real Data mining task is the self-loader or programmed examination of huge amounts of information to separate beforehand unclear, captivating, for example, meetings of information records (group investigation), unusual records (asymmetry discovery), and constrains (affiliation rule mining, consecutive example mining). This normally includes utilizing database methods, for example, dimensional files. These examples would then be able to be viewed as a sort of outline of the information, and may be utilized in further investigation or, for example, in AI and prognostic examination. For example, the information mining step may differentiate different gatherings in the information, which would then be able to be utilized to acquire progressively exact forecast results by a choice emotionally supportive network.

Track 08: Algorithm in data science

The execution of Data Science to any problem requires a set of skills. Machine Learning is an crucial part of this skill set. For doing Data Science, you must know the numerous Machine Learning   algorithms used for responding different types of problems, as a single algorithm cannot be the best for all types of use examples. These algorithms find a supplication in various tasks like prediction, classification, meeting, etc. from the dataset under deliberation.

Track 09: Data visualization and presentation

Data visualization and presentation both are an art and a science. It is seen as a part of describing measurements by a few, yet in inclusion as a grounded theory improvement device by others. Expanded measures of information constructed by Internet movement and an expanding number of detectors in the earth are alluded to as "large information" or Internet of things. Preparing, breaking down and carrying these information present moral and logical difficulties for information visualization. The field of information science and professionals called information examiner help address this test

Track 10: 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. As an aspiring data scientist, your goal with commanding to code will be Read and note data from different derivations. Work on different data types Coding, sometimes called computer programming, is how we transfer with computers. Code tells a computer what activities to take, and writing code is like creating a set of instructs. By learning to write code, you can tell computers what to do or how to act in a much fast way. You can use this skill to create websites and apps, method data, and do plenty of other cool things.

Track 11: Bioinformatics     

Bioinformatics is purifying and inspecting large-scale genomics and other biological datasets to extension biological insights. As such, other phrases are occasionally used as well, such as algorithmic genetics and genomic data science. Data science is a little wider, mostly a broader word whose definition is near to that of bioinformatics without the biological focus clearing and inspecting large-scale datasets to expand insights. And the crucial skills of a data scientist require programming, machine learning, statistics, data quarrelling, data visualization and communication, and data intuition bioinformatics careers is domain specific data processing and quality checking, general data modification and cleansing, applied statistics and machine learning, domain-specific statistical instruments and data visualization and combination, capability to write code, ability to communicate data driven discrimination

 Discover the latest trends in the data science:

Conferences address the main issues and supply in-depth industry news that is relevant to your sector. As an assistant, you will advantage from comprehensive knowledge about the latest news in your industry; where the trends will go and how to adapt to them in the future.

 Meet the best promoter: 

High level events usually have a program filled with highly prestigious national and international speakers. By attending these events, you will meet this industry promoter and learn from their recommendations first-hand.

 Gain unique knowledge and apply it to your business:

Once you have listened to all the presentations, you will have imperative information about all the latest industry developments and professional tips on how to face new challenges. It is also a great opportunity to learn how to apply these strategies to your business.

 Know what your competitors are doing and how to set your company apart:

It’s very common for conferences to cover the success stories and the strategies that have led to company growth; in addition, different tips on how to grow are often discussed and examined. Therefore, you learn what competitor companies are doing and how you can implement these techniques in your business to set you apart from your competitors.

Take networking chances to gain new customers:

Events usually include a dedicated time for networking, which attendees can take advantage of to create a connection with other professionals in the industry and to renew contact with those they already know. This is a great opportunity to generate leads and new potential customers for your business; as well as turn current customers into brand ambassadors and increase customer loyalty.

Skill improvement:

Learning new ideas and approaches in conferences make delegates more effective and efficient at work.

Meeting experts and influencers face-to-face:   

 Conference will offer delegates opportunity to meet business leaders and position as an expert in his/her field.

Network:

This conference will offer delegates the opportunity to mix and mingle, form new relationships and strengthen existing ones.

Explore new ways of working:

A well-run conference will help faculty curate new ideas. Even though there is alot of information on the web, conferences will cut through the clutter to deliver the best content specific.

Break out of comfort zone:

This Conference will force the delegates to break out of comfort zone. Breaking out of comfort zone is just the type of action necessary to break out of old ways of thinking.

Get greater focus:

The flip side of learning new things is relearning classic techniques. This Conference will create greater focus and reflection opportunities that could help delegates take ideas to the next level.

Grow:

This conference provide a unique convergence of networking, learning and fun into a single package.

The Data Science programme market size is projected to grow from USD 95.2billion in 2021 to 322.9 USD billion in 2026, at a Compound annual Growth Rate (CAGR) of 27.7% during the predict period. The Data Science Platform industry is driven by Astonishing growth of big data, however, Rising in assumption of cloud-based solutions, Rising application of the data science platform in various industries and Growing need to take out in-depth insights from voluminous data to gain competitive advantage.

Impact of Covid-19 on Data Science programme market COVID-19 can have three crucial effects on the global economy: COVID-19 can have three crucial effects on the global economy: directly impacting production and command, causing supply chain and market disruption, and having a financial impact on businesses and monetary markets. The COVID-19 breakout has a positive impact on the extension of the Data Science Platform market, as the adoption of Data Science Platform is increased to understand the impact of COVID-19 on the economy.

 The machine learning market expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. Machine learning enabled solutions are being significantly adopted by organizations worldwide to enhance customer experience, ROI, and to gain a competitive edge in business operations. Moreover, in the coming years, an application of machine learning in various industry verticals is expected to rise exponentially. Technological advancement and proliferation in data generation are some of the major driving factors for the market.

The objective of the study has been carried out to define, describe, and forecast the global market on the basis of vertical (BFSI, energy and utilities, healthcare and life sciences, retail, telecommunication, manufacturing, government and defence, others (transportation, agriculture, media and entertainment, and education), services (professional services and managed services), deployment modes (cloud and on-premises), organization sizes (SMEs and large enterprises), and regions (North America, Europe, APAC, MEA, and Latin America). The report also aims at providing detailed information about the major factors influencing the growth of the machine learning market (drivers, restraints, opportunities, and challenges.