This document discusses common NLP problems, including sentiment analysis, chatbots, translation services, and document summarization. It then presents four case studies applying NLP techniques: 1) Using chatbot conversation data and topology to understand customer groups, 2) Using text classification for product types, 3) Using topic modeling on poetry to classify poems by genre, 4) Using linguistic analysis and changepoint detection on public statements to understand changes in a leader's behavior during war. Finally, it lists helpful Python packages for NLP, topology, and modeling.
Technical writing, an introduction to academic writingColleen Farrelly
Will likely be turning this into a YouTube talk at some point, but gives resources, breakdowns of parts of a scientific paper, and tips to avoid plagiarism.
This presentation explains what is a survey/review paper.
Moreover it deals with the aspects that have to be kept in mind while writing a review paper.
Technical writing, an introduction to academic writingColleen Farrelly
Will likely be turning this into a YouTube talk at some point, but gives resources, breakdowns of parts of a scientific paper, and tips to avoid plagiarism.
This presentation explains what is a survey/review paper.
Moreover it deals with the aspects that have to be kept in mind while writing a review paper.
A guide to preparing Research Reports/Dissertations in Qualitative Psychology. The Structure, format and features of a report are underlined. Simple language
Good practice in researching: A qualitative and cross-disciplinary researchRichard Lalleman
I was asked by the London Metropolitan University to present my experiences regarding a knowledge management research, with special focus on research methodologies
Data in the HS Classroom: When, Why, and How?ICPSR
Presentation given as part of the High School Teachers of Sociology Workshop at the American Sociological Association Annual Meeting, 2012 (Denver, CO).
Scientific incubation: The “Interim” as case study in scientific writing by P...SATN
Prof Lategan’s (Dean: Research and Development, Central University of Technology) presentation at the SATN Annual Conference 2009.
Theme: “Technological innovation at Universities in South Africa: towards industrial and socio-economic development”
16 - 17 July 2009
Cape Peninsula University of Technology
Bellville Campus.
Are topic-specific search term, journal name and author name recommendations ...GESIS
In this paper we describe a case study where researchers in the social sciences (n=19) assess topical relevance for controlled search terms, journal names and author names which have been compiled automatically by bibliometric-enhanced information retrieval (IR) services. We call these bibliometric-enhanced IR services Search Term Recommender (STR), Journal Name Recommender (JNR) and Author Name Recommender (ANR) in this paper. The researchers in our study (practitioners, PhD students and postdocs) were asked to assess the top n pre-processed recommendations from each recommender for specific research topics which have been named by them in an interview before the experiment. Our results show clearly that the presented search term, journal name and author name recommendations are highly relevant to the researchers’ topic and can easily be integrated for search in Digital Libraries. The average precision for top ranked recommendations is 0.75 for author names, 0.74 for search terms and 0.73 for journal names. The relevance distribution differs largely across topics and researcher types. Practitioners seem to favor author name recommendations while postdocs have rated author name recommendations the lowest. In the experiment the small postdoc group (n=3) favor journal name recommendations.
ProjectPro offers Solved End-to-End, Ready to Deploy, Enterprise-Grade Big Data, and Data Science Projects for Reuse and Upskilling. Each project solves a real business problem end-to-end and comes with solution code, explanation videos, cloud lab, and tech support.
16 Decision Support and Business Intelligence Systems (9th E.docxRAJU852744
16 Decision Support and Business Intelligence Systems (9th Edition) Instructor’s Manual
Chapter 7:
Text Analytics, Text Mining, and Sentiment Analysis
Learning Objectives for Chapter 7
1. Describe text mining and understand the need for text mining
2. Differentiate among text analytics, text mining, and data mining
3. Understand the different application areas for text mining
4. Know the process of carrying out a text mining project
5. Appreciate the different methods to introduce structure to text-based data
6. Describe sentiment analysis
7. Develop familiarity with popular applications of sentiment analysis
8. Learn the common methods for sentiment analysis
9. Become familiar with speech analytics as it relates to sentiment analysis
10. Learn three facets of Web analytics—content, structure, and usage mining
11. Know social analytics including social media and social network analyses
CHAPTER OVERVIEW
This chapter provides a comprehensive overview of text analytics/mining and Web analytics/mining along with their popular application areas such as search engines, sentiment analysis, and social network/media analytics. As we have been witnessing in recent years, the unstructured data generated over the Internet of Things (IoT) (Web, sensor networks, radio-frequency identification [RFID]–enabled supply chain systems, surveillance networks, etc.) are increasing at an exponential pace, and there is no indication of its slowing down. This changing nature of data is forcing organizations to make text and Web analytics a critical part of their business intelligence/analytics infrastructure.
CHAPTER OUTLINE
7.1 Opening Vignette: Amadori Group Converts Consumer Sentiments into
Near-Real-Time Sales
7.2 Text Analytics and Text Mining Overview
7.3 Natural Language Processing (NLP)
7.4 Text Mining Applications
7.5 Text Mining Process
7.6 Sentiment Analysis
7.7 Web Mining Overview
7.8 Search Engines
7.9 Web Usage Mining
7.10 Social Analytics
ANSWERS TO END OF SECTION REVIEW QUESTIONS( ( ( ( ( (
Section 7.1 Review Questions
1. According to the vignette and based on your opinion, what are the challenges that the food industry is facing today?
Student perceptions may vary, but some common themes related to the challenges faced by the food industry could include the changing nature and role of food in people’s lifestyles, the shift towards pre-prepared or easily prepared food, and the growing importance of marketing to keep customers interested in brands.
2. How can analytics help businesses in the food industry to survive and thrive in this competitive marketplace?
Analytics can serve dual purposes by both tracking customer interest in the brand as well as providing valuable feedback on customer preferences. An analytics system can be used to evaluate the traffic to various brand marketing campaigns (website or social) that play a pivotal role in ensuring that products are being shown to new pot.
16 Decision Support and Business Intelligence Systems (9th E.docxherminaprocter
16 Decision Support and Business Intelligence Systems (9th Edition) Instructor’s Manual
Chapter 7:
Text Analytics, Text Mining, and Sentiment Analysis
Learning Objectives for Chapter 7
1. Describe text mining and understand the need for text mining
2. Differentiate among text analytics, text mining, and data mining
3. Understand the different application areas for text mining
4. Know the process of carrying out a text mining project
5. Appreciate the different methods to introduce structure to text-based data
6. Describe sentiment analysis
7. Develop familiarity with popular applications of sentiment analysis
8. Learn the common methods for sentiment analysis
9. Become familiar with speech analytics as it relates to sentiment analysis
10. Learn three facets of Web analytics—content, structure, and usage mining
11. Know social analytics including social media and social network analyses
CHAPTER OVERVIEW
This chapter provides a comprehensive overview of text analytics/mining and Web analytics/mining along with their popular application areas such as search engines, sentiment analysis, and social network/media analytics. As we have been witnessing in recent years, the unstructured data generated over the Internet of Things (IoT) (Web, sensor networks, radio-frequency identification [RFID]–enabled supply chain systems, surveillance networks, etc.) are increasing at an exponential pace, and there is no indication of its slowing down. This changing nature of data is forcing organizations to make text and Web analytics a critical part of their business intelligence/analytics infrastructure.
CHAPTER OUTLINE
7.1 Opening Vignette: Amadori Group Converts Consumer Sentiments into
Near-Real-Time Sales
7.2 Text Analytics and Text Mining Overview
7.3 Natural Language Processing (NLP)
7.4 Text Mining Applications
7.5 Text Mining Process
7.6 Sentiment Analysis
7.7 Web Mining Overview
7.8 Search Engines
7.9 Web Usage Mining
7.10 Social Analytics
ANSWERS TO END OF SECTION REVIEW QUESTIONS( ( ( ( ( (
Section 7.1 Review Questions
1. According to the vignette and based on your opinion, what are the challenges that the food industry is facing today?
Student perceptions may vary, but some common themes related to the challenges faced by the food industry could include the changing nature and role of food in people’s lifestyles, the shift towards pre-prepared or easily prepared food, and the growing importance of marketing to keep customers interested in brands.
2. How can analytics help businesses in the food industry to survive and thrive in this competitive marketplace?
Analytics can serve dual purposes by both tracking customer interest in the brand as well as providing valuable feedback on customer preferences. An analytics system can be used to evaluate the traffic to various brand marketing campaigns (website or social) that play a pivotal role in ensuring that products are being shown to new pot.
A guide to preparing Research Reports/Dissertations in Qualitative Psychology. The Structure, format and features of a report are underlined. Simple language
Good practice in researching: A qualitative and cross-disciplinary researchRichard Lalleman
I was asked by the London Metropolitan University to present my experiences regarding a knowledge management research, with special focus on research methodologies
Data in the HS Classroom: When, Why, and How?ICPSR
Presentation given as part of the High School Teachers of Sociology Workshop at the American Sociological Association Annual Meeting, 2012 (Denver, CO).
Scientific incubation: The “Interim” as case study in scientific writing by P...SATN
Prof Lategan’s (Dean: Research and Development, Central University of Technology) presentation at the SATN Annual Conference 2009.
Theme: “Technological innovation at Universities in South Africa: towards industrial and socio-economic development”
16 - 17 July 2009
Cape Peninsula University of Technology
Bellville Campus.
Are topic-specific search term, journal name and author name recommendations ...GESIS
In this paper we describe a case study where researchers in the social sciences (n=19) assess topical relevance for controlled search terms, journal names and author names which have been compiled automatically by bibliometric-enhanced information retrieval (IR) services. We call these bibliometric-enhanced IR services Search Term Recommender (STR), Journal Name Recommender (JNR) and Author Name Recommender (ANR) in this paper. The researchers in our study (practitioners, PhD students and postdocs) were asked to assess the top n pre-processed recommendations from each recommender for specific research topics which have been named by them in an interview before the experiment. Our results show clearly that the presented search term, journal name and author name recommendations are highly relevant to the researchers’ topic and can easily be integrated for search in Digital Libraries. The average precision for top ranked recommendations is 0.75 for author names, 0.74 for search terms and 0.73 for journal names. The relevance distribution differs largely across topics and researcher types. Practitioners seem to favor author name recommendations while postdocs have rated author name recommendations the lowest. In the experiment the small postdoc group (n=3) favor journal name recommendations.
ProjectPro offers Solved End-to-End, Ready to Deploy, Enterprise-Grade Big Data, and Data Science Projects for Reuse and Upskilling. Each project solves a real business problem end-to-end and comes with solution code, explanation videos, cloud lab, and tech support.
16 Decision Support and Business Intelligence Systems (9th E.docxRAJU852744
16 Decision Support and Business Intelligence Systems (9th Edition) Instructor’s Manual
Chapter 7:
Text Analytics, Text Mining, and Sentiment Analysis
Learning Objectives for Chapter 7
1. Describe text mining and understand the need for text mining
2. Differentiate among text analytics, text mining, and data mining
3. Understand the different application areas for text mining
4. Know the process of carrying out a text mining project
5. Appreciate the different methods to introduce structure to text-based data
6. Describe sentiment analysis
7. Develop familiarity with popular applications of sentiment analysis
8. Learn the common methods for sentiment analysis
9. Become familiar with speech analytics as it relates to sentiment analysis
10. Learn three facets of Web analytics—content, structure, and usage mining
11. Know social analytics including social media and social network analyses
CHAPTER OVERVIEW
This chapter provides a comprehensive overview of text analytics/mining and Web analytics/mining along with their popular application areas such as search engines, sentiment analysis, and social network/media analytics. As we have been witnessing in recent years, the unstructured data generated over the Internet of Things (IoT) (Web, sensor networks, radio-frequency identification [RFID]–enabled supply chain systems, surveillance networks, etc.) are increasing at an exponential pace, and there is no indication of its slowing down. This changing nature of data is forcing organizations to make text and Web analytics a critical part of their business intelligence/analytics infrastructure.
CHAPTER OUTLINE
7.1 Opening Vignette: Amadori Group Converts Consumer Sentiments into
Near-Real-Time Sales
7.2 Text Analytics and Text Mining Overview
7.3 Natural Language Processing (NLP)
7.4 Text Mining Applications
7.5 Text Mining Process
7.6 Sentiment Analysis
7.7 Web Mining Overview
7.8 Search Engines
7.9 Web Usage Mining
7.10 Social Analytics
ANSWERS TO END OF SECTION REVIEW QUESTIONS( ( ( ( ( (
Section 7.1 Review Questions
1. According to the vignette and based on your opinion, what are the challenges that the food industry is facing today?
Student perceptions may vary, but some common themes related to the challenges faced by the food industry could include the changing nature and role of food in people’s lifestyles, the shift towards pre-prepared or easily prepared food, and the growing importance of marketing to keep customers interested in brands.
2. How can analytics help businesses in the food industry to survive and thrive in this competitive marketplace?
Analytics can serve dual purposes by both tracking customer interest in the brand as well as providing valuable feedback on customer preferences. An analytics system can be used to evaluate the traffic to various brand marketing campaigns (website or social) that play a pivotal role in ensuring that products are being shown to new pot.
16 Decision Support and Business Intelligence Systems (9th E.docxherminaprocter
16 Decision Support and Business Intelligence Systems (9th Edition) Instructor’s Manual
Chapter 7:
Text Analytics, Text Mining, and Sentiment Analysis
Learning Objectives for Chapter 7
1. Describe text mining and understand the need for text mining
2. Differentiate among text analytics, text mining, and data mining
3. Understand the different application areas for text mining
4. Know the process of carrying out a text mining project
5. Appreciate the different methods to introduce structure to text-based data
6. Describe sentiment analysis
7. Develop familiarity with popular applications of sentiment analysis
8. Learn the common methods for sentiment analysis
9. Become familiar with speech analytics as it relates to sentiment analysis
10. Learn three facets of Web analytics—content, structure, and usage mining
11. Know social analytics including social media and social network analyses
CHAPTER OVERVIEW
This chapter provides a comprehensive overview of text analytics/mining and Web analytics/mining along with their popular application areas such as search engines, sentiment analysis, and social network/media analytics. As we have been witnessing in recent years, the unstructured data generated over the Internet of Things (IoT) (Web, sensor networks, radio-frequency identification [RFID]–enabled supply chain systems, surveillance networks, etc.) are increasing at an exponential pace, and there is no indication of its slowing down. This changing nature of data is forcing organizations to make text and Web analytics a critical part of their business intelligence/analytics infrastructure.
CHAPTER OUTLINE
7.1 Opening Vignette: Amadori Group Converts Consumer Sentiments into
Near-Real-Time Sales
7.2 Text Analytics and Text Mining Overview
7.3 Natural Language Processing (NLP)
7.4 Text Mining Applications
7.5 Text Mining Process
7.6 Sentiment Analysis
7.7 Web Mining Overview
7.8 Search Engines
7.9 Web Usage Mining
7.10 Social Analytics
ANSWERS TO END OF SECTION REVIEW QUESTIONS( ( ( ( ( (
Section 7.1 Review Questions
1. According to the vignette and based on your opinion, what are the challenges that the food industry is facing today?
Student perceptions may vary, but some common themes related to the challenges faced by the food industry could include the changing nature and role of food in people’s lifestyles, the shift towards pre-prepared or easily prepared food, and the growing importance of marketing to keep customers interested in brands.
2. How can analytics help businesses in the food industry to survive and thrive in this competitive marketplace?
Analytics can serve dual purposes by both tracking customer interest in the brand as well as providing valuable feedback on customer preferences. An analytics system can be used to evaluate the traffic to various brand marketing campaigns (website or social) that play a pivotal role in ensuring that products are being shown to new pot.
ProjectPro offers a hands-on approach to mastering machine learning and data science through 150+ solved end-to-end deployable machine learning and data science projects. They also provide 2000+ FREE data science code examples that can help one master the foundations of basic data science and machine learning concepts.
Regardless of the content architecture (DITA, DocBook, Structured, Un-Structured, etc…) you should have a strategy around your content creation and production. But is your strategy working for you? Is it delivering what your customers are looking for? Can they find what they need? Do they actually enjoy using your content?
Looks at developing a product content strategy that comes from the outside (your customers side): A strategy that will not only keep your content consumers satisfied, but that will keep you modern and current even as technologies and consumers change over time.
SearchInFocus: Exploratory Study on Query Logs and Actionable Intelligence Marina Santini
Query logs are an important source of information to surmize users intents'. Although Karlgren (2010) points out that “There are several reasons to be cautious in drawing too far-reaching conclusions: we cannot say for sure what the users were after; [...]“, some linguistic problems could be sorted out by applying more advanced text/content analytics, such as register/sublanguage identification and terminology classification (see Friberg Heppin, 2011) . In this presentation, I will argue that query logs can be considered a digital textual genre alike emails, blogs, chats, tweets and so forth. All these genres contain unstructured information that, still today, is difficult to leverage upon satisfactorily. The hypothesis that I would like to put forward in this workshop is that query logs might be easier to exploit to extract useful information and actionable intelligence than other digital genres.
Thema webinar from BookNet Canada, June 2014BookNet Canada
Thema is a new international subject classification standard for books. It should be used in addition to BISAC (the North American subject classification standard). Download these slides for helpful information on what Thema is, why you should use it, and how to start implementing it.
An overview of some core concept in natural language processing, some example (experimental for now!) use cases, and a brief survey of some tools I have explored.
Topic modeling of marketing scientific papers: An experimental surveyICDEcCnferenece
Malek Chebil, Rim Jallouli, Mohamed Anis Bach Tobji and Chiheb Eddine Ben Ncir. Topic modeling of marketing scientific papers: An experimental survey. (ICDEc 2021)
BEA 2015 Demystifying Subject Codes and KeywordsBowker
This was originally presented at BEA 2015. This presentation covers tips for generating keywords and for using them throughout your book metadata, things to consider when creating your internal subject schema, and overviews of several industry schemes.
Presentation at joint PIA workshop at UMAP 2014 CNGL_Ireland
CNGL's Dr. Rami Ghorab presented research in multilingual search personalisation during the joint PIA workshop at UMAP 2014. 'Does Personalisation Benefit Everyone in the Same Way? Multilingual Search Personalisation for English vs. Non-English Users'. The research paper, which is accessible here http://bit.ly/1qjRyY5 is authored by; M. Rami Ghorab, Séamus Lawless, Alexander O'Connor and Vincent Wade.
Generative AI for Social Good at Open Data Science East 2024Colleen Farrelly
A brief overview of generative AI technologies and their use for social good initiatives, including cultural training, medical image generation, drug design, and public health.
PyData Global 2023 talk overviewing case studies in network science, including stock market crash prediction, food price pattern mining, and stopping the spread of epidemics.
Overview of mathematical and machine learning models related to climate risk modeling, climate change simulations, and change point detection. Includes a hands-on session with geometry-based systems analysis of food prices related to climate change and geopolitical factors.
WiDS Workshop on natural language processing and generative AI. Details common methods that tie into coding examples. Ends with ethics discussion regarding these technologies and potential for misuse.
Link to talk YouTube: https://www.youtube.com/watch?v=byGzKm0H1-8&list=PLHAk3jHXWpxI7fHw8m5PhrpSRpR3NIjQo&index=3
ODSC-East 2023 presentation covering topics related to my book, The Shape of Data, including how geometry plays a role in text/image embeddings, network science problems, survey data analytics, image analytics, and epidemic wrangling.
This talk overviews my background as a female data scientist, introduces many types of generative AI, discusses potential use cases, highlights the need for representation in generative AI, and showcases a few tools that currently exist.
Emerging Technologies for Public Health in Remote Locations.pptxColleen Farrelly
The tools possible to leverage for public health interventions has changed significantly in the past decades. Tools from geometry, natural language processing, and generative AI allow for a quick design and implementation of interventions, even in very rural parts of the world. Case studies involve HIV, Ebola, and COVID interventions.
WoComToQC workshop lecture on Forman-Ricci curvature for applications in industry (social networks, disaster logistics, spatial data, and spatiotemporal goods pricing data).
PyData Global talk covering tools from geometry/topology and their uses in public health, public policy, and social good initiatives. Examples include food price prediction, COVID policies, public health interventions, and fair AI.
Data Science Dojo Talk on comparing time series using persistent homology. Short overview of time series data. A bit of topology. Code available. Example includes stock exchange data.
Statistical and topological algorithm piece of an Applied Machine Learning Days Morocco talk. Covers ARIMA models, SSA models, GEE models, and persistent homology. Applications include pricing data, stock data, development data, and healthcare data. Datasets and full presentation can be found on GitHub: https://github.com/gabayae/Time-Series-Applications_AMLD2022
An introduction to quantum machine learning.pptxColleen Farrelly
Very basic introduction to quantum computing given at Indaba Malawi 2022. Overviews some basic hardware in classical and quantum computing, as well as a few quantum machine learning algorithms in use today. Resources for self-study provided.
Indaba Malawi workshop on basic approaches to time series data, including ARIMA models and SSA models. Example in R includes an agricultural example from historical Malawi data with Rssa package and base ARIMA models.
NLP: Challenges and Opportunities in Underserved AreasColleen Farrelly
This talk highlights the challenges and opportunities that exist in linguistically underserved areas. It highlights NLP initiatives in Sub-Saharan Africa, as well as financial opportunities in technology if areas neglected linguistically can produce tools in their local languages. Ethics, ownership, and other concerns are highlighted to guide development initiatives.
Geometry, Data, and One Path Into Data Science.pptxColleen Farrelly
Women in Data Science (Alexandria, Egypt) keynote address. Topics cover my journey into data science/machine learning, an overview of data science as a profession, and some case studies on topology/geometry in analytics. Example case studies include insurance, natural language processing, social network analysis, and psychometrics.
WiDS Alexandria, Egypt workshop in topological data analysis (Python and R code available on request), covering persistent homology, the Mapper algorithm, and discrete Ricci curvature. Examples include text data and social network data.
SAS Global 2021 Introduction to Natural Language Processing Colleen Farrelly
Overview of text data, processing of text data, integration of text data with structured databases, and uses of text data in analytics across a variety of fields. Here's the talk link: https://www.youtube.com/watch?v=wS0X1bSsuUU
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
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.
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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.
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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.
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.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
4. CASE STUDY 1: CONSUMER GROUP
CLUSTERING
• Want to understand how
different groups interact
with a chatbot
• Sales implications
• Groups-specific needs
for future feature builds
• Chatbot conversation data
sample
• NLP to derive salient text
features
• Persistent homology to
5. CASE 2: SUPERVISED LEARNING
• Want to classify products by
type (such as fruit or canned
soup) using title text
• Data includes a small sample of
scraped titles from a sample of
retailers with manual annotation
of product type
• Text cleaning and embedding
algorithms to prepare the text
data for machine learning
• Supervised learning algorithm
to create the classier
6. CASE 3: TOPIC
MODELING
• Want to find main
topics discussed in a
corpus of documents
(poems)
• Poetry data sample
across genres of
poetry by a single
author
• Topic modeling to
classify poems
7. CASE 4:
TIME-
BASED
ANALYSIS
OF
MINDSET
• Want to quickly understand
changes in leader’s behavior at
onset of war
• Public statement sample by
president over course of several
weeks as input data
• NLP to derive linguistic features
• Longitudinal models and topology-
based changepoint algorithm on
linguistic feature time series
8. HELPFUL
PYTHON
PACKAGES
• NLP:
• NLTK (parts of speech tagging,
munging data…)
• Gensim (topic models)
• Vader (sentiment analysis)
• TDA
• Persim/ripser (persistent
homology)
• Kmapper (Mapper algorithm)
• Structural equation modeling/latent
class modeling
• Semopy (similar to lavaan in R)