Data Science Data Science

What is Data Science?

Mathematical and algorithmic methods optimized to efficiently identify complex patterns. The science of data analysis methods, formed at the intersection of mathematics, computer science and business, includes the construction of complex analytical models based on data to extract new knowledge.

Data Science is a set of specific disciplines from different directions responsible for analyzing data and finding optimal solutions based on them. Previously, only mathematical statistics were engaged in this, then they began to use machine learning and artificial intelligence, which, as methods of analyzing data, added optimization to matstatistics and computer science (that is, computer science, but more broadly than is customary to understand in Russia)^[1]

Project Data Science Structure

Data Science - how does it work?

Traditional Risks Data Science Projects

• The high cost of the project will lead to financial losses (will not pay off)
• Lack of detailed project reporting will prevent reporting of funds spent or correct decision to proceed with the project
• Implementing a closed algorithm or program ("Black Box") will make it impossible to further modify or upgrade the project with external or internal resources

Big Data≠Data Science

Big Data is:

• ETL\ELT
• Storage technologies for large amounts of structured and non-structured data
• Technologies for processing such data
• Data Quality Management
• Technologies for providing data to the consumer

Data Science is:

• Video recognition
• Text recognition
• Speech recognition
• Building Recommendation Models
• Segmentation
• Clustering, etc.

Data Science in the realities of production

• Complex and time-consuming process
• Deep understanding of the subject area is required
• Different frequency of data collection and not all digitized
• No end-to-end monitoring and recording of process engineering events
• Trust in the model by technologists and operators
• Model checks require real-time experiments in production

Data News and Trends

2023

"Artificial intelligence from pain to effects" - a look from Data experts

Reksoft Consulting, a transformation and strategic consulting division of the Reksoft group, has released a study on the problems faced by Data specialists of Russian companies during the development and implementation of digital solutions based on artificial intelligence (AI) technologies. The material also contains an overview of possible ways to overcome the difficulties that arise. This was announced by "Reksoft" on November 28, 2023.

Reksoft Consulting conducted in-depth interviews with experts, namely technical directors, CDOs, field and team leaders, Data Science Data Science specialists who develop and implement digital solutions based on AI in various to industries economies understand what problems they face today. Representatives of,, industries medicine financial sectors, and companies took part retail in IT the survey.

According to the results of the interview, 5 key areas were identified, in which the main difficulties are concentrated, which do not allow effective implementation of AI solutions in Russian companies:

1. Interaction of data specialists with the business customer.
2. Data
3. Manage development and technology
4. Commissioning and support of AI solutions.
5. Search, retention and development of Data Science specialists

Among the most common reasons for difficulties in the interaction of Data specialists with the business customer are: high business expectations, unwillingness of the business to transform, unadapted corporate culture. They are most acute if the business invests in AI, but does not achieve the effect and has difficulty with the engraftment of decisions. To successfully implement them, business customers need to be ready to transform their operating model.

The data block includes root causes such as insufficient level of automation of business processes, low level of maturity of the data infrastructure, poor quality of source data and a long process for obtaining them, data collection and management processes that are not adapted for digital solutions based on AI. Data difficulties always cover not only AI development, but the entire company due to the lack of uniform requirements and customized processes. Data issues are characterized by the thesis "new problems, old solutions" - before starting Data Science, you need to debug and adapt the processes associated with data management.

The technology stack for developing AI-based solutions is constantly changing and evolving. Here Data specialists distinguish the lack of AI development standards and a flexible approach when prototyping AI solutions, as well as the lack of a formed approach to working with external developers of AI solutions.

In the segment of commissioning and support of AI solutions, experts note the lack of a built-in process of commissioning and clear criteria for accepting decisions, as well as the fact that information security approaches are not adapted to the implementation of AI solutions and assessing its risks. To minimize the barriers that companies face when scaling pilot AI solutions, it is necessary to agree on success criteria in advance and think through a support model. It is critical that before the start of the project, we determine and agree on an approach to assessing the economic effect with all stakeholders, as well as build a long-term system of motivation for the employees involved in order to avoid difficulties with the adoption of decisions.

Of particular concern to the surveyed experts is the task of finding, retaining and developing Data Science specialists. The existing HR processes in many Russian companies for finding, hiring, adapting and retaining personnel are not adapted for Data specialists. The lack of T-shape specialists exacerbates the gap between business and Data Science. HR in this situation does not understand how to develop the latter and adapt the former. Organizational structures and IT role models in many Russian companies did not have time to adapt to the systematic implementation of AI-based solutions, which blurs the distribution of responsibility and the role of Data specialists.

AI is often perceived as a "fashionable toy," while the most important thing is missed - AI should give the company a systemic transformational effect. It is necessary to create a technological and organizational base for the systematic development of digital products from prototyping to obtaining an effect. Here it is worth thinking about creating a "digital pipeline" in a compartment with the business transformation of the company itself. As a result, AI should become an understandable and familiar technology for business - a daily working tool integrated into current business systems such as ERP m CRM systems and analytics, said Alexey Bogomolov, director of the "Transformation Strategy" practice "Reksoft Consulting."

Named 5 trends in the Data Science market

Generative artificial intelligence systems will have a significant impact on the global industry of data science and machine learning (DSML). This is stated in the Gartner report, published on August 1, 2023.

Against the background of the active introduction of machine learning tools in different industries, a market transformation is taking place - the focus is shifting from conventional predictive models to a more democratized and dynamic data-driven approach. This is facilitated by the development of generative AI platforms. Along with the potential risks, there are many new opportunities and options for using AI in the field of Data Science, "says Peter Krensky, director analyst at Gartner.

Generative AI systems will have a significant impact on the global DSML industry

A Gartner survey of more than 2,500 executives from various organizations found that 45% of companies increased investment in AI following the introduction of the ChatGPT chatbot. At the same time, 70% of respondents reported that they are studying the possibility of using generative AI tools, while 19% are already experimenting with such systems. Gartner identifies five key trends that will determine the further development of the DSML industry.

Trend 1. Cloud Data Ecosystems

Data solutions are transformed from standalone software or mixed deployments to full cloud platforms. By 2024, Gartner believes, 50% of new applications in the cloud will be based on a holistic data ecosystem, not manually integrated point arrays.

Trend 2. Artificial intelligence on the periphery

There is a growing need for Edge AI. Such tools allow data to be processed at the time it is created, which helps organizations get valuable information in real time and comply with strict privacy requirements. Gartner predicts that by 2025, more than 55% of all deep neural network data analysis will occur on the periphery. For comparison: in 2021, this figure was less than 10%.

Trend 3. Responsible AI

A responsible-use approach maximizes the benefit of AI technology adoption and circumvents possible trust and societal risk issues. The concept of responsible AI covers many business and ethical aspects. Gartner recommends that organizations be careful when implementing neural network models and apply a risk-based business strategy to ensure the value of AI. This will help protect against financial losses, lawsuits and reputational damage.

45% of companies increased investment in AI after the advent of ChatGPT

Trend 4. Data-centric artificial intelligence

The data-centric approach will enable the creation of better AI applications and services. The use of generative AI to form synthetic data is one of the fast-growing areas contributing to the efficient training of machine learning models. Gartner predicts that by 2024, 60% of data for modeling reality, new scenarios for the use of AI and risk reduction will be synthetic. In 2021, this figure was only 1%.

Trend 5. Accelerating investment in AI

Financial injections into AI technologies will continue to increase, which will be facilitated by the increased use of appropriate tools. By 2026, Gartner experts believe, more than $10 billion will be invested in startups that use large-scale AI models trained on huge amounts of data.^[3]

2020: Data Science: Five Key Trends

1. Accelerating AI Adoption in Business

Over the past few years, AI has gradually become one of the main technologies for both small and large enterprises, and there is every reason to believe that this will continue over the next few years. Today we are in the early stages of using AI, but it is likely that by the end of 2020 we will see new and more progressive methods of its use in scientific fields and business. Driving this rapid growth is the fact that AI allows companies of all sizes to significantly improve the efficiency and efficiency of their business processes and operations. With it, you can also achieve tremendous success in managing client and user data^[4].

Many enterprises will face difficulties in implementing AI, which is due to limited financial resources or lack of qualified personnel, but those who invest in it will receive tangible returns in the form of advanced applications developed using AI, MoD and other technologies that will significantly change the methods of work that are adopted today.

Another trend that will take a visible shape in the coming months is automated MO, which helps transform data science with improved data management. This will result in novice data scientists needing to take specialized courses to learn deep learning techniques.

2. Rapid growth IoT

Investment in IoT technology will reach $1 trillion by the end of 2020, according to the IDC, a clear indication of expected growth in smart and connected devices. Many people already use applications and devices to use them to control their household appliances - electric furnaces, refrigerators, air conditioners and televisions. These are all examples of basic IoT technology, and users often may not know what is hidden behind it. Smart devices like Google Assistant, Amazon Alexa and Microsoft Cortana allow people to easily automate everyday tasks at home. It is only a matter of time before companies use them in combination with business applications and begin to invest more actively in this technology. The most noticeable progress from the use of IoT is expected in production - there it will help optimize the work of factory workshops.

3. Evolution of Big Data Analytics

Effective big data analysis undoubtedly helps businesses gain significant competitive advantage and achieve core goals. Today, they use various tools and technologies, such as Python, to analyze their data accumulations. More companies have focused on identifying the reasons behind certain events that are currently happening, in which case predictive analytics come to the rescue - it allows you to identify trends and predict what may happen in the future. For example, it will be useful in order to determine user habits based on the history of views or purchases. Sales and marketing professionals can analyze these models to create more focused strategies to attract new customers and retain existing ones. Amazon applies predictive models to fill inventory based on demand in a particular sales region.

4. Edge Computing on the rise

Peripheral computing is gaining popularity, and sensors are responsible for this. The offensive of this technology will continue in large part due to the popularization of IoT, which captures the main computing systems. Edge Computing provides companies with the ability to store streaming data near sources and analyze it in real time. Peripheral computing is also an alternative to big data analytics, which requires high-performance storage devices and much greater network bandwidth. The number of devices and sensors collecting data is growing exponentially, so more companies are adopting Edge Computing due to its capabilities to solve bandwidth, latency, and communication problems. In addition, the combination of peripheral and cloud technologies forms a synchronized infrastructure that can minimize the risks associated with data analysis and management.

5. Growing Demand for Data Security Professionals

Without a doubt, the introduction of AI and MO will lead to the emergence of many new specialties in the IT and high-tech industries. One of the most popular will be a data security specialist. There are already a sufficient number of experts in the field of AI, MO and data specialists in the labor market, but in addition to them there is a need for data security specialists who are able to analyze and process data in this way in order to transfer it to customers in a safe form. To perform these functions, they must be well versed in the latest technologies such as Python and other popular languages that apply to data science and analytics. A clear understanding of Python concepts will help solve data security issues.

Training Data Science

2024: Nanosemantic Announces Partnership with Skillfactory School of IT Professions

The company "Nanosemantics" will act as a technological partner of the online program "Data Science in Medicine" of the school of IT professions Skillfactory. The collaboration aims to train qualified Data Science professionals for the medical industry. This was announced by Nanosemantics on March 11, 2024. Read more here.

2020: NUST MISIS, SkillFactory and Mail.ru Group launch Russian-language online master's degree in Data Science

On May 28, 2020, the company VK (formerly Mail.ru Group) announced that NUST "" and MISIS the educational platform in the field of Data Science - - SkillFactory entered into an agreement on the creation of a joint online master's program "Science of" and data cooperation in the development of educational technologies in the highest. education This is a partnership between a private educational company state and a university under the OPM (Online Program Management) model. The industrial partner of the program is Group Mail.ru. The program is also supported, and. Nvidia Rostelecom NTI University "20.35"

Graduates of the program will be able to work in the fields Big Data of Engineering, Machine Learning Development Artificial Intelligence and Development. The goal of the program is to involve sciences more than 1,000 young specialists in the field of data by 2025 within the framework of the federal project "Personnel for," the digital economy task of which is to prepare at least 120,000 university graduates on IT directories.

Classes will be taught by professors of NUST MISIS and practitioners from Mail.ru Group, Yandex, Tinkoff and VTB banks, Lamoda, BIOCAD, AlfaStrakhovanie, etc. An intensive online master's program will allow students to master the knowledge and skills demanded by employers, get the foundation for further development and career building, and take an internship in partner companies of the program.

The interdisciplinary master's program Data Science was created by NUST MISIS together with SkillFactory and companies - Mail.ru Group, Rostelecom and NVidia. Its graduates will have knowledge and competencies in the fields of big data, artificial intelligence and machine learning. These skills are relevant in the labor market and are in demand by employers, " noted' Alevtina Chernikova, rector of NUST "MISIS" '

Another feature of the program is working with mentors. In addition to teachers, a team of mentors - specialists in the field of Data Science will work with students. They will help students with the difficulties that arise during training, give meaningful feedback on the work performed, share experience and knowledge by profession. Mentor support will be available to students in a live chat.

The technology partner of the program was SkillFactory, which provides support for the educational process. An individual training plan will be formed for each student, which will allow managing his educational experience and motivation, which, in turn, increases the effectiveness of training. Students will learn from interactive simulators and solve practical problems based on real data. Among the disciplines within the program: Python programming language, Machine Learning, Deep Learning, Big Data, Computer Vision.

"We believe in the OPM (Online Program Manager) model - the interaction of universities and educational companies in the creation and implementation of educational programs. This model has been working in the USA and Europe for more than 10 years, and we are sure that in the coming years it will show itself well in Russian universities, " noted' by Alexander Eroshkin, CEO of SkillFactory '

"Training of Data Science specialists is one of the main areas of Mail.ru Group's educational activities. We implement different formats, including the development of the competencies of professionals who already work in this area. In this direction, we are working closely with NUST MISIS - in 2019 we opened the MADE Big Data Academy, where, as of May 2020, 200 students from all over the country study - and are ready to share their experience in supporting the university's online master's program. The online format has many advantages, but the main thing is accessibility. Residents of any region will be able to get a master's degree from a Moscow university, " noted Dmitry Smyslov, Vice President for Human Resources and Educational Projects, Mail.ru Group

Graduates of the bachelor's degree in any area of ​ ​ training based on the results of the online exam will be able to enter the magistracy.

Data Director - Chief Data Officer, CDO

Chief Data Officer (CDO)

Data scientist

Main article - here

Why Data Scientist is sexier than a BI analyst

Due to the growing popularity of data science (DS), two very obvious questions arise. First, what is the qualitative difference between this recently formed scientific direction from the existing one for several decades and the business intelligence (BI) direction actively used in the industry? The second - perhaps more important from a practical point of view - how do the functions of specialists of two related specialties data scientist and BI analyst differ? In the material prepared specifically for TAdviser, journalist Leonid Chernyak answers these questions.

Data management

• Internet of Things (IoT)
• Internet of Things, IoT, M2M (Global Market)
• Internet of Things, IoT, M2M (Russian market)
• Internet of Things: you can't stay in time
• What is the Internet of Things (IoT)
• IIoT - Industrial Internet of Things

• Big Data
• Big Data in Russia
• Big Data (Big Data) Global Market
• Big Data Specialist
• Data Mining
• Data Quality

• Master Data Management (Management of the main master data) Catalogue of systems and projects
• Regulatory Reference Information Management Systems in Russia.
  Top players and top trends
• Chief Data Officer (CDO)
• Data management
• Data Science
• Data scientist
• Chief Digital Officer, CDO
• CIO (Chief Information Officer)
• Chief information security officer (CISO)
• CFO - Chief Financial Officer

Notes