Data Science: The Art of Foul Play by Serhiy ShelpukSoftServe
Serhiy Shelpuk, Lead Data Scientist, Competence Manager at SoftServe, Inc., delivered an insightful presentation on Data Science and SoftServe`s Data Science Group Knowledge Model at the 2013 IT Weekend Ukraine conference that took place on September 14, 2013, in Kyiv, Ukraine. Here`s his presentation.
Where does Data Democracy begin? [Segment-Synapse, 2019]aj_cache
Data Democracy is a global vision. This talk explores what this means within the context of large businesses with examples from the restructuring of Sun Basket's Data Science & Engineering team.
Data Science Salon: Culture, Data Engineering and Hamburger Stands: Thoughts ...Formulatedby
Presented by Becky Tucker, Data Scientist at Netflix
Next DSS NYC Event 👉 https://datascience.salon/newyork/
Next DSS LA Event 👉 https://datascience.salon/la/
Becky Tucker is speaking about how Netflix culture uniquely interacts with data science, the importance of data engineering to our data science teams, how their teams are structured to do data science "at scale," and what "data science at scale" looks like for her.
A short Introduction to the Influence of Big Data in today's world and how it's helping the organization and industry to be familiar with their clients and partners.
Big data for beginners. Tried to prove that "Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it..." is totally wrong.
Data Science: The Art of Foul Play by Serhiy ShelpukSoftServe
Serhiy Shelpuk, Lead Data Scientist, Competence Manager at SoftServe, Inc., delivered an insightful presentation on Data Science and SoftServe`s Data Science Group Knowledge Model at the 2013 IT Weekend Ukraine conference that took place on September 14, 2013, in Kyiv, Ukraine. Here`s his presentation.
Where does Data Democracy begin? [Segment-Synapse, 2019]aj_cache
Data Democracy is a global vision. This talk explores what this means within the context of large businesses with examples from the restructuring of Sun Basket's Data Science & Engineering team.
Data Science Salon: Culture, Data Engineering and Hamburger Stands: Thoughts ...Formulatedby
Presented by Becky Tucker, Data Scientist at Netflix
Next DSS NYC Event 👉 https://datascience.salon/newyork/
Next DSS LA Event 👉 https://datascience.salon/la/
Becky Tucker is speaking about how Netflix culture uniquely interacts with data science, the importance of data engineering to our data science teams, how their teams are structured to do data science "at scale," and what "data science at scale" looks like for her.
A short Introduction to the Influence of Big Data in today's world and how it's helping the organization and industry to be familiar with their clients and partners.
Big data for beginners. Tried to prove that "Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it..." is totally wrong.
In this presentation, let's have a look at What is Data Science and it's applications. We discussed most common use cases of Data Science.
I presented this at LSPE-IN meetup happened on 10th March 2018 at Walmart Global Technology Services.
Big data analytics is the often complex process of examining big data to uncover information -- such as hidden patterns, correlations, market trends and customer preferences -- that can help organizations make informed business decisions.
Data and Analytics Career Paths, Presented at IEEE LYC'19.
About Speaker:
Ahmed Amr is a Data/Analytics Engineer at Rubikal, where he leads, develops, and creates daily data/analytics operations, which includes data ingestion , data streaming, data warehousing, and analytical dashboards. Ahmed is graduated from Computer Engineering Department, Alexandria University; and he is currently pursuing his MSc degree in Computer Science, AAST. Professionally, Ahmed worked with Egyptian/US startups such as (Badr, Incorta, WhoKnows) to develop their data/analytics projects. Academically, Ahmed worked as a Teaching Assistant in CS department, AAST. Ahmed helps software companies to develop robust data engineering infrastructure, and powerful analytical insights.
References:
1) https://www.datacamp.com/community/tutorials/data-science-industry-infographic
2) Analytics: The real-world use of big data, IBM, Executive Report
Data Science Innovations : Democratisation of Data and Data Science suresh sood
Data Science Innovations : Democratisation of Data and Data Science covers the opportunity of citizen data science lying at the convergence of natural language generation and discoveries in data made by the professions, not data scientists.
My slides on how to use cloud as a data platform at BigDataWeek 2013 Romania
http://www.eurocloud.ro/en/events/all-there-is-to-know-about-big-data/#.UXZFaUDvlVI
Delivered January 30, 2018 at Mobile Tea Boston. This is an updated version of the presentation from June 2017.
Artificial Intelligence. Machine Learning. And Mobile? Yup. Every major technology company and a host of not so major ones are doing things with AI. With cheap, plentiful computing power and a growing number of open source and commercial offerings, applications that used to be the stuff of science fiction are available today from your desktop, phone, and watch. Let’s talk about what’s happening and how it affects how we think about mobile development.
May graph technology improve the deployment of humanitarian projects? The goal of using what we call “Graphs for good at Action Against Hunger” is to be more efficient and transparent, and this can have a crucial impact on people’s lives.
Is there common behaviour factors between different projects? Can elements of different resources or projects be related? For example, security incidents in a city could influence the way other projects run in there.
The explained use case data comes from a project called Kit For Autonomous Cash Transfer in Humanitarian Emergencies (KACHE) whose goal is to deploy electronic cash transfers in emergency situations when no suitable infrastructure is available.
It also offers the opportunity to track transactions in order to better recognize crisis-affected population behaviours, understanding goods distribution network to improve recommendations, identifying the role of culture in transactional patterns, as well as most required items for every place.
International Journal of Database and Analytics(IJDA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Database and Analytics . The journal focuses on all technical and practical aspects of Database and Analytics. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced Database and Analytics and establishing new collaborations in these areas.
Applied Data Science Course Part 1: Concepts & your first ML modelDataiku
In this first course of our Applied Data Science online course series, you'll learn about the mindset shift of going from small to big data, basic definitions and concepts, and an overview of the data science workflow.
Defining Data Science
• What Does a Data Science Professional Do?
• Data Science in Business
• Use Cases for Data Science
• Installation of R and R studio
Look no further than our comprehensive Data Science Training program in Chandigarh. Designed to equip individuals with the skills and knowledge required to thrive in today's data-centric world, our course offers a unique blend of theoretical foundations and hands-on practical experience.
Warren Buffet would often think of companies as castles with a competitive moat protecting the business. Products or companies that figure out how to build and leverage differentiated data assets will be best positioned to win their respective markets. This talk describes the properties of a good data moat, why it matters, and how to go about building them within your organization.
Advanced Analytics and Data Science ExpertiseSoftServe
An overview of SoftServe's Data Science service line.
- Data Science Group
- Data Science Offerings for Business
- Machine Learning Overview
- AI & Deep Learning Case Studies
- Big Data & Analytics Case Studies
Visit our website to learn more: http://www.softserveinc.com/en-us/
In this presentation, let's have a look at What is Data Science and it's applications. We discussed most common use cases of Data Science.
I presented this at LSPE-IN meetup happened on 10th March 2018 at Walmart Global Technology Services.
Big data analytics is the often complex process of examining big data to uncover information -- such as hidden patterns, correlations, market trends and customer preferences -- that can help organizations make informed business decisions.
Data and Analytics Career Paths, Presented at IEEE LYC'19.
About Speaker:
Ahmed Amr is a Data/Analytics Engineer at Rubikal, where he leads, develops, and creates daily data/analytics operations, which includes data ingestion , data streaming, data warehousing, and analytical dashboards. Ahmed is graduated from Computer Engineering Department, Alexandria University; and he is currently pursuing his MSc degree in Computer Science, AAST. Professionally, Ahmed worked with Egyptian/US startups such as (Badr, Incorta, WhoKnows) to develop their data/analytics projects. Academically, Ahmed worked as a Teaching Assistant in CS department, AAST. Ahmed helps software companies to develop robust data engineering infrastructure, and powerful analytical insights.
References:
1) https://www.datacamp.com/community/tutorials/data-science-industry-infographic
2) Analytics: The real-world use of big data, IBM, Executive Report
Data Science Innovations : Democratisation of Data and Data Science suresh sood
Data Science Innovations : Democratisation of Data and Data Science covers the opportunity of citizen data science lying at the convergence of natural language generation and discoveries in data made by the professions, not data scientists.
My slides on how to use cloud as a data platform at BigDataWeek 2013 Romania
http://www.eurocloud.ro/en/events/all-there-is-to-know-about-big-data/#.UXZFaUDvlVI
Delivered January 30, 2018 at Mobile Tea Boston. This is an updated version of the presentation from June 2017.
Artificial Intelligence. Machine Learning. And Mobile? Yup. Every major technology company and a host of not so major ones are doing things with AI. With cheap, plentiful computing power and a growing number of open source and commercial offerings, applications that used to be the stuff of science fiction are available today from your desktop, phone, and watch. Let’s talk about what’s happening and how it affects how we think about mobile development.
May graph technology improve the deployment of humanitarian projects? The goal of using what we call “Graphs for good at Action Against Hunger” is to be more efficient and transparent, and this can have a crucial impact on people’s lives.
Is there common behaviour factors between different projects? Can elements of different resources or projects be related? For example, security incidents in a city could influence the way other projects run in there.
The explained use case data comes from a project called Kit For Autonomous Cash Transfer in Humanitarian Emergencies (KACHE) whose goal is to deploy electronic cash transfers in emergency situations when no suitable infrastructure is available.
It also offers the opportunity to track transactions in order to better recognize crisis-affected population behaviours, understanding goods distribution network to improve recommendations, identifying the role of culture in transactional patterns, as well as most required items for every place.
International Journal of Database and Analytics(IJDA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Database and Analytics . The journal focuses on all technical and practical aspects of Database and Analytics. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced Database and Analytics and establishing new collaborations in these areas.
Applied Data Science Course Part 1: Concepts & your first ML modelDataiku
In this first course of our Applied Data Science online course series, you'll learn about the mindset shift of going from small to big data, basic definitions and concepts, and an overview of the data science workflow.
Defining Data Science
• What Does a Data Science Professional Do?
• Data Science in Business
• Use Cases for Data Science
• Installation of R and R studio
Look no further than our comprehensive Data Science Training program in Chandigarh. Designed to equip individuals with the skills and knowledge required to thrive in today's data-centric world, our course offers a unique blend of theoretical foundations and hands-on practical experience.
Warren Buffet would often think of companies as castles with a competitive moat protecting the business. Products or companies that figure out how to build and leverage differentiated data assets will be best positioned to win their respective markets. This talk describes the properties of a good data moat, why it matters, and how to go about building them within your organization.
Advanced Analytics and Data Science ExpertiseSoftServe
An overview of SoftServe's Data Science service line.
- Data Science Group
- Data Science Offerings for Business
- Machine Learning Overview
- AI & Deep Learning Case Studies
- Big Data & Analytics Case Studies
Visit our website to learn more: http://www.softserveinc.com/en-us/
This video includes:
Purpose of Data Science, Role of Data Scientist, Skills required for Data Scientist, Job roles for Data Scientist, Applications of Data Science, Career in Data Science.
There's a new breed of digital marketers who are employing data science practices to achieve better results more efficiently. Whether it's SEO, email marketing, marketing automation, response conversion, funnel optimization, or web analytics, these hybrid data scientists / data driven digital markets are using advanced mathematics, machine learning, predictive analytics, statistical modeling, and data mining in conjunction with marketing savvy to drive better results and grow companies faster. We'll discuss some case studies, best practices, and simple pseudo-data-science actions that even the non-data scientist can put to use immediately.
Leveraging data to become more customer-centric is a key factor for online retail sales. Using a host of Machine learning techniques like recommender systems, image analytics, customer churn and demand prediction- can impact sales, customer loyalty & improve revenues
Highlights and summary of long-running programmatic research on data science; practices, roles, tools, skills, organization models, workflow, outlook, etc. Profiles and persona definition for data scientist model. Landscape of org models for data science and drivers for capability planning. Secondary research materials.
A complete brief introduction and importance on Data Science, Data Analytics, Business Analytics, Tools used for Analytics, Artificial Intelligence and Machine Learning.
How to use your data science team: Becoming a data-driven organizationYael Garten
Talk given at Strata Hadoop World conference March 2016.
http://conferences.oreilly.com/strata/hadoop-big-data-ca/public/schedule/detail/48305
In this talk we review the culture, process and tools needed for a data driven organization. We review an example of how companies like LinkedIn use data to make business decisions, and then walk through the culture, process, and tools needed to foster this. We review the spectrum of data science used within an organization and explore organizational needs, such as the democratization of data via self-serve data platforms for experimentation, monitoring, and data exploration, as well as the challenges that come with such systems. Participants leave this session with the ability to identify opportunities for data scientists to contribute within their organization and with an understanding of what investments are needed to drive transformation into a data-driven organization.
Smarter businesses apply AI to learn and continuously evolve the way they work. To extract full value from AI, companies need data strategy that gives them access to all their data – no matter where it lives – in an environment that easily scales and applies the latest discovery technology including advanced analytics, visualization and AI. Learn how IBM Watson and Data provides all the tools companies need to embed AI, machine learning and deep learning in their business, while enabling professionals to gain the most from their data to drive smarter business and lead industry-changing transformations.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
2. Data Science:
• Data science, also known as data-driven science, is an
interdisciplinary field of scientific methods, processes,
and systems to extract knowledge or insights from
data in various forms. -Wikipedia
4. Data Scientist skills:
• A data scientist should be a good programmer!
• A data scientist should have solid quantitative skills!
• A data scientist should excel in communication and
visualization have a solid business understanding!
• A data scientist should be creative!
6. Data Science Applications:
• Search Recommendations
• Digital Advertisements
• Image Recognisation.
• Gaming
• Fraud and risk detection.
• Self Driving Cars
7. What do Data Scientists do ?
Identify
Business
Problem
Identify
Data
Sources
Select
the
Data
Clean
the
Data
Transform
the
Data
Analyze
the
Data
Intepret,
Evaluate,
and Deploy
the Model
Preprocessing Analytics
Post-
processing
8. In the Article:
• When LinkedIn had a pool of users, data science
helped in the exponential growth of the company by
recommending the best connections as
recommendations.
• Netflix has used data science to improve the movie
recommendation system.
9. In the Article:
• Amazon use data science to recommend the
appropriate product to customer based on his
shopping history.
10. Need of Data Scientist:
• With improvement in technology, data has been
accumulating exponentially.
• Need to find insights from the unstructured data to
improve the business requirements is growing rapidly.