Master class Hristo Hadjitchonev - Aubg

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Video: https://www.youtube.com/watch?v=onmF8SRLvVA For more information: http://datasciencesociety.net/ https://ask.datasciencesociety.net/

Science

a4everyone.com 3
BIG DATA
HowBIG is the big data?

a4everyone.com 7
DATA SCIENCE
When Harvard Business Review
called it "The Sexiest Job of the 21st
Century” the term became a
buzzword …

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DATA SCIENCE
Data science, also known as data-driven
science, is an interdisciplinary field of
scientific methods, processes, algorithms
and systems to extract knowledge or
insights from data in various forms, either
structured or unstructured, similar to data
mining.

a4everyone.com 10
CASES
Uniliver
• New
Technology
• Lipton Yellow
Label
• 5.6% Revenue
growth in 2
years
https://themarketingagenda.com/2
014/09/20/unilever-bcg-matrix/
PetSmart
• Price
Optimization
• +22 million $
• 600% ROI
http://www.parkeravery.com/casestudy-
Price-Optimization-Solution-
Implementation.html
Coca-Cola
• GEO Targeting
of Marketing
Campaign
• 20% better
efficiency
• 60’000
households
https://www.a4everyone.com/doc/Coc
a-Cola-Case-Study.pdf

a4everyone.com 11
GLOBAL PICTURE
Source: McKinsey Global Institute - Artificial Intelligence paper (June 2017)

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HOW TO JOIN THE PARTY
Project
Pros
• Fast
• Done by Professionals
• $$$
Cons
• Tactic rather Strategic
• Partial knowhow
• High Risk of Failure
In-house
Pros
• 100% in-house
• Flexibility
• Opt. for Digitalization
Cons
• Entire Team???
• $$$$$$ (investment)
• Maturity
Service
Pros
• $ (ROI)
• Latest and Greatest
• High Quality & Minimal
Risk
• Effort
Cons
• Partially Dependent
• Lack of know-how

a4everyone.com 13
DATA SCIENCE TEAM
NO ANSWER is a valid answer!!!
Industry Maturity (comparison with SWD)
Complexity
Business Operations

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90
69
49
38
31
21
12 8.3 4.6 3.4 2.6
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1790 1840 1880 1900 1910 1930 1950 1960 1970 1980 1990
% of the population occupied with farming
Other Farming

“The world´s most valuable resource“ titles „The Economist“ in May, referring to data as the resource. With computing resources in the cloud, cheap sensors in physical products, and advanced machine learning algorithms to make use of the collected data, the relevance of data will only increase. We believe that these developments open up new opportunities for companies to develop and profit form data products. They can include feedback loops in their existing products in order to improve them or create new products based on the usage data of an existing one. The usage data itself can even become the USP of the physical product. Machine learning can help to automate customer jobs that before required tedious input of data by the customer. They can realize more complex business models as users of a service and buyers of data might be very different. In the talk, we will illustrate our definition of data products based on project examples. We share our insights on how to implement a data product successfully. We have adapted the Lean start-up principle in order to get data products to the market quickly while maintaining the core factors of a valuable data product. Dr. Christoph Tempich works as Chief Data Economist at inovex GmbH, an innovative service provider for digital transformation projects. He watches over the economic aspects of digital transformation and takes a strategic view on data economics. Furthermore, he supports companies in defining data products and improving their digital business models.

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Since volcanic activity was first discovered on Io from Voyager images in 1979, changes on Io’s surface have been monitored from both spacecraft and ground-based telescopes. Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images show that a plume deposit from a powerful eruption at Pillan Patera has covered part of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive optics at visible wavelengths.

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Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024). Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).

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PRIYANKA PATEL

With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.

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Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.

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1. How Big is Data Science?

2. HRISTO HADJITCHONEV Co-founder A4E

3. a4everyone.com 3 BIG DATA HowBIG is the big data?

4. 4

5. 5

6. 6

7. a4everyone.com 7 DATA SCIENCE When Harvard Business Review called it "The Sexiest Job of the 21st Century” the term became a buzzword …

8. a4everyone.com 8 DATA SCIENCE Data science, also known as data-driven science, is an interdisciplinary field of scientific methods, processes, algorithms and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining.

9. a4everyone.com 9 DATA SCIENCE

10. a4everyone.com 10 CASES Uniliver • New Technology • Lipton Yellow Label • 5.6% Revenue growth in 2 years https://themarketingagenda.com/2 014/09/20/unilever-bcg-matrix/ PetSmart • Price Optimization • +22 million $ • 600% ROI http://www.parkeravery.com/casestudy- Price-Optimization-Solution- Implementation.html Coca-Cola • GEO Targeting of Marketing Campaign • 20% better efficiency • 60’000 households https://www.a4everyone.com/doc/Coc a-Cola-Case-Study.pdf

11. a4everyone.com 11 GLOBAL PICTURE Source: McKinsey Global Institute - Artificial Intelligence paper (June 2017)

12. a4everyone.com 12 HOW TO JOIN THE PARTY Project Pros • Fast • Done by Professionals • $$$ Cons • Tactic rather Strategic • Partial knowhow • High Risk of Failure In-house Pros • 100% in-house • Flexibility • Opt. for Digitalization Cons • Entire Team??? • $$$$$$ (investment) • Maturity Service Pros • $ (ROI) • Latest and Greatest • High Quality & Minimal Risk • Effort Cons • Partially Dependent • Lack of know-how

13. a4everyone.com 13 DATA SCIENCE TEAM NO ANSWER is a valid answer!!! Industry Maturity (comparison with SWD) Complexity Business Operations

14. a4everyone.com 14 90 69 49 38 31 21 12 8.3 4.6 3.4 2.6 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 1790 1840 1880 1900 1910 1930 1950 1960 1970 1980 1990 % of the population occupied with farming Other Farming

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