As companies increasingly integrate data across functions, the boundaries between marketing, sales and operations have been blurring. This allows them to find new opportunities that arise by aligning and integrating the activities of supply and demand to improve commercial effectiveness. Instead of conducting post-hoc analyses that allow them to correct future actions, companies generate and analyze data in near real-time and adjust their operations processes dynamically. Transitioning from static analytics outputs to more dynamic contextualized insights means analytics can be delivered with increased relevance closer to the point of decision.
This talk will cover the analytics journey from descriptive, predictive and prescriptive analytics to derive actionable and timely insights to improve customer experience to drive marketing, salesforce and operations excellence.
Government GraphSummit: Leveraging Graphs for AI and MLNeo4j
Phani Dathar, Ph.D., Data Science Solution Architect, Neo4j
Relationships are highly predictive of behavior. Graph technology abstracts connections in our data so businesses can apply relationships and network structures to make better predictions. Hear about the journey from graph analytics and machine learning to graph-enhanced AI. We’ll also cover how enterprises are using graph data science in areas such as fraud, targeted marketing, healthcare, and recommendations.
Introduction to Data Science and AnalyticsSrinath Perera
This webinar serves as an introduction to WSO2 Summer School. It will discuss how to build a pipeline for your organization and for each use case, and the technology and tooling choices that need to be made for the same.
This session will explore analytics under four themes:
Hindsight (what happened)
Oversight (what is happening)
Insight (why is it happening)
Foresight (what will happen)
Recording http://t.co/WcMFEAJHok
Government GraphSummit: Keynote - Graphs in GovernmentNeo4j
Jim Webber Ph.D., Chief Scientist, Neo4j
Learn about the importance of graph technology, its evolution over the last few years and the impact it has had on the database and data analytics industry. This session will provide an overview of graph technology and talk about the past, present, and future of graphs and data management. Multiple use cases and customer examples will be covered, including examples of where graph databases and graph data science can assist and accelerate machine learning and artificial intelligence projects.
This presentation explains what data engineering is and describes the data lifecycles phases briefly. I used this presentation during my work as an on-demand instructor at Nooreed.com
- Learn to understand what knowledge graphs are for
- Understand the structure of knowledge graphs (and how it relates to taxonomies and ontologies)
- Understand how knowledge graphs can be created using manual, semi-automatic, and fully automatic methods.
- Understand knowledge graphs as a basis for data integration in companies
- Understand knowledge graphs as tools for data governance and data quality management
- Implement and further develop knowledge graphs in companies
- Query and visualize knowledge graphs (including SPARQL and SHACL crash course)
- Use knowledge graphs and machine learning to enable information retrieval, text mining and document classification with the highest precision
- Develop digital assistants and question and answer systems based on semantic knowledge graphs
- Understand how knowledge graphs can be combined with text mining and machine learning techniques
- Apply knowledge graphs in practice: Case studies and demo applications
Modern Data Challenges require Modern Graph TechnologyNeo4j
This session focuses on key data trends and challenges impacting enterprises. And, how graph technology is evolving to future-proof data strategy and architectures.
Government GraphSummit: Leveraging Graphs for AI and MLNeo4j
Phani Dathar, Ph.D., Data Science Solution Architect, Neo4j
Relationships are highly predictive of behavior. Graph technology abstracts connections in our data so businesses can apply relationships and network structures to make better predictions. Hear about the journey from graph analytics and machine learning to graph-enhanced AI. We’ll also cover how enterprises are using graph data science in areas such as fraud, targeted marketing, healthcare, and recommendations.
Introduction to Data Science and AnalyticsSrinath Perera
This webinar serves as an introduction to WSO2 Summer School. It will discuss how to build a pipeline for your organization and for each use case, and the technology and tooling choices that need to be made for the same.
This session will explore analytics under four themes:
Hindsight (what happened)
Oversight (what is happening)
Insight (why is it happening)
Foresight (what will happen)
Recording http://t.co/WcMFEAJHok
Government GraphSummit: Keynote - Graphs in GovernmentNeo4j
Jim Webber Ph.D., Chief Scientist, Neo4j
Learn about the importance of graph technology, its evolution over the last few years and the impact it has had on the database and data analytics industry. This session will provide an overview of graph technology and talk about the past, present, and future of graphs and data management. Multiple use cases and customer examples will be covered, including examples of where graph databases and graph data science can assist and accelerate machine learning and artificial intelligence projects.
This presentation explains what data engineering is and describes the data lifecycles phases briefly. I used this presentation during my work as an on-demand instructor at Nooreed.com
- Learn to understand what knowledge graphs are for
- Understand the structure of knowledge graphs (and how it relates to taxonomies and ontologies)
- Understand how knowledge graphs can be created using manual, semi-automatic, and fully automatic methods.
- Understand knowledge graphs as a basis for data integration in companies
- Understand knowledge graphs as tools for data governance and data quality management
- Implement and further develop knowledge graphs in companies
- Query and visualize knowledge graphs (including SPARQL and SHACL crash course)
- Use knowledge graphs and machine learning to enable information retrieval, text mining and document classification with the highest precision
- Develop digital assistants and question and answer systems based on semantic knowledge graphs
- Understand how knowledge graphs can be combined with text mining and machine learning techniques
- Apply knowledge graphs in practice: Case studies and demo applications
Modern Data Challenges require Modern Graph TechnologyNeo4j
This session focuses on key data trends and challenges impacting enterprises. And, how graph technology is evolving to future-proof data strategy and architectures.
Fighting financial fraud at Danske Bank with artificial intelligenceRon Bodkin
Danske Bank, the leader in mobile payments in Denmark, is innovating with AI. Danske Bank’s existing fraud detection engine is being enhanced with deep learning algorithms that can analyze potentially tens of thousands of latent features. Danske Bank’s current system is largely based on handcrafted rules created by the business, based on intuition and some light analysis. The system is effective at blocking fraud, but it has a high rate of false positives, which is expensive and inconvenient, and it has proved impractical to update and maintain as fraudsters evolve their capabilities. Moreover, the bank understands that fraud is getting worse in the near- and long-term future due to the increased digitization of banking and the prevalence of mobile banking applications and recognizes the need to use cutting-edge techniques to engage fraudsters not where they are today but where they will be tomorrow.
Application fraud is an important emerging trend, in which machines fill in transaction forms. There is evidence that criminals are employing sophisticated machine-learning techniques to attack, so it’s critical to use sophisticated machine learning to catch fraud in banking and mobile payment transactions.
Ron Bodkin and Nadeem Gulzar explore how Danske Bank uses deep learning for better fraud detection. Danske Bank’s multistep program first productionizes “classic” machine learning techniques (boosted decision trees) while in parallel developing deep learning models with TensorFlow as a “challenger” to test. The system was first tested in shadow production and then in full production in a champion-challenger setup against live transactions. Ron and Nadeem explain how the bank is integrating the models with the efforts already running, giving the bank and its investigation team the ability to adapt to new patterns faster than before and taking on complex highly varying functions not present in the training examples.
***** Blockchain Training : https://www.edureka.co/blockchain-training *****
This Edureka video on "Blockchain Explained" is to guide you through the fundamentals of the new revolutionary technology called Blockchain and its defining concepts. Below are the topics covered in this tutorial:
1. History of blockchain
2. What is Blockchain
3. Traditional Transaction vs Blockchain
4. How Blockchain Works
5. Benefits of Blockchain
6. Blockchain Transaction Demo
Here is the link to the Blockchain blog series: https://goo.gl/DPoAHR
You can also refer this playlist on Blockchain: https://goo.gl/V5iayd
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
Digital Transformation is a top priority for many organizations, and a successful digital journey requires a strong data foundation. Creating this digital transformation requires a number of core data management capabilities such as MDM, With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
Building a Data Strategy Your C-Suite Will SupportReid Colson
Being a data leader in any industry is an advantage that creates measurable financial benefits. Many studies have shown this – I’ve seen them from Bain, McKinsey, MIT and more. Since most firms are measured on profit, getting good at making data driven decisions is a key to being competitive. You can't get there without a plan. That is where a data strategy comes in.
In speaking with ~300 firms who indicated that their organizations were effective in using data and analytics, McKinsey found that construction of a data strategy was the number one contributing factor to their success. Being good at using data to drive decisions creates a meaningful profit advantage and those who are leaders indicated that the number one driver of their success was their data strategy.
This presentation will cover what a data strategy is, how to construct one, and how to get buy in from your executive team. The author is a former Fortune 500 Chief Data Officer and has held senior data roles at Capital One and Markel.
Here are a few helpful links for your data journey:
Free Data Investment ROI Template:
https://www.udig.com/digging-in/roi-calculator-for-it-projects/
Real world data use cases:
https://www.udig.com/our-work/?category=data
Contact Me:
https://www.udig.com/contact/
AI Modernization at AT&T and the Application to Fraud with DatabricksDatabricks
AT&T has been involved in AI from the beginning, with many firsts; “first to coin the term AI”, “inventors of R”, “foundational work on Conv. Neural Nets”, etc. and we have applied AI to hundreds of solutions. Today we are modernizing these AI solutions in the cloud with the help of Databricks and a variety of in-house developments. This talk will highlight our AI modernization effort along with its application to Fraud which is one of our biggest benefitting applications.
Two hour lecture I gave at the Jyväskylä Summer School. The purpose of the talk is to give a quick non-technical overview of concepts and methodologies in data science. Topics include a wide overview of both pattern mining and machine learning.
See also Part 2 of the lecture: Industrial Data Science. You can find it in my profile (click the face)
DAS Slides: Building a Data Strategy - Practical Steps for Aligning with Busi...DATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task. The opportunity in getting it right can be significant, however, as data drives many of the key initiatives in today’s marketplace: digital transformation, marketing, customer centricity, and more. This webinar will help de-mystify Data Strategy and Data Architecture and will provide concrete, practical ways to get started.
Ross Chayka. Gartner Hype Cycle
Ross Chayka's personal websites:
UA - https://rchayka.one
EN - https://rosschayka.com
LIn - https://www.linkedin.com/in/rchayka
At Neo4j we believe that ‘Graphs Are Everywhere’. In this session, we’ll be looking specifically at graphs within the Financial Services industry. We’ll review the types of data that are typically available within a bank, illustrate the graphs can be formed from that data, and discuss the use cases that those graphs can enable and support.
The use cases presented will include Anti-Money Laundering and Fraud Detection and Prevention (including integration with AI and Machine Learning technologies), Regulatory Compliance (such as BCBS 239 and GDPR), Customer 360 View, Master Data Management, and Identity and Access Management.
Many players in the Financial Services industry already rely on Neo4j's graph database: such as Lending Club, the world's largest microservices credit marketplace, for Network and IT, the big German insurance company die Bayerische for graph-based search, Cerved for Master Data Management, Wobi for price comparison and real-time recommendation, or UBS for Identity and Access Management.
This course covers in detail the technical principles & concepts behind blockchain. In addition, it seeks to provide you with the insights and deep understanding of the various components of blockchain technology, and enables you to determine for yourself how to best leverage and exploit blockchain for your project, organisation or start-up.
Link - https://www.experfy.com/training/courses/blockchain-technology-fundamentals
BI Consultancy - Data, Analytics and StrategyShivam Dhawan
The presentation describes my views around the data we encounter in digital businesses like:
- Looking at common Data collection methodologies,
-What are the common issues within the decision support system and optimiztion lifecycle,
- Where are most of failing?
and most importantly, "How to connect the dots and move from Data to Strategy?"
I work with all facets of Web Analytics and Business Strategy and see the structures and governance models of various domains to establish and analyze the key performance indicators that allow you to have a 360º overview of online and offline multi-channel environment.
Apart from my experience with the leading analytic tools in the market like Google Analytics, Omniture and BI tools for Big Data, I am developing new solutions to solve complex digital / business problems.
As a resourceful consultant, I can connect with your team in any modality or in any form that meets your needs and solves any data/strategy problem.
Data Science - Part I - Sustaining Predictive Analytics CapabilitiesDerek Kane
This is the first lecture in a series of data analytics topics and geared to individuals and business professionals who have no understand of building modern analytics approaches. This lecture provides an overview of the models and techniques we will address throughout the lecture series, we will discuss Business Intelligence topics, predictive analytics, and big data technologies. Finally, we will walk through a simple yet effective example which showcases the potential of predictive analytics in a business context.
Fighting financial fraud at Danske Bank with artificial intelligenceRon Bodkin
Danske Bank, the leader in mobile payments in Denmark, is innovating with AI. Danske Bank’s existing fraud detection engine is being enhanced with deep learning algorithms that can analyze potentially tens of thousands of latent features. Danske Bank’s current system is largely based on handcrafted rules created by the business, based on intuition and some light analysis. The system is effective at blocking fraud, but it has a high rate of false positives, which is expensive and inconvenient, and it has proved impractical to update and maintain as fraudsters evolve their capabilities. Moreover, the bank understands that fraud is getting worse in the near- and long-term future due to the increased digitization of banking and the prevalence of mobile banking applications and recognizes the need to use cutting-edge techniques to engage fraudsters not where they are today but where they will be tomorrow.
Application fraud is an important emerging trend, in which machines fill in transaction forms. There is evidence that criminals are employing sophisticated machine-learning techniques to attack, so it’s critical to use sophisticated machine learning to catch fraud in banking and mobile payment transactions.
Ron Bodkin and Nadeem Gulzar explore how Danske Bank uses deep learning for better fraud detection. Danske Bank’s multistep program first productionizes “classic” machine learning techniques (boosted decision trees) while in parallel developing deep learning models with TensorFlow as a “challenger” to test. The system was first tested in shadow production and then in full production in a champion-challenger setup against live transactions. Ron and Nadeem explain how the bank is integrating the models with the efforts already running, giving the bank and its investigation team the ability to adapt to new patterns faster than before and taking on complex highly varying functions not present in the training examples.
***** Blockchain Training : https://www.edureka.co/blockchain-training *****
This Edureka video on "Blockchain Explained" is to guide you through the fundamentals of the new revolutionary technology called Blockchain and its defining concepts. Below are the topics covered in this tutorial:
1. History of blockchain
2. What is Blockchain
3. Traditional Transaction vs Blockchain
4. How Blockchain Works
5. Benefits of Blockchain
6. Blockchain Transaction Demo
Here is the link to the Blockchain blog series: https://goo.gl/DPoAHR
You can also refer this playlist on Blockchain: https://goo.gl/V5iayd
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
Digital Transformation is a top priority for many organizations, and a successful digital journey requires a strong data foundation. Creating this digital transformation requires a number of core data management capabilities such as MDM, With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
Building a Data Strategy Your C-Suite Will SupportReid Colson
Being a data leader in any industry is an advantage that creates measurable financial benefits. Many studies have shown this – I’ve seen them from Bain, McKinsey, MIT and more. Since most firms are measured on profit, getting good at making data driven decisions is a key to being competitive. You can't get there without a plan. That is where a data strategy comes in.
In speaking with ~300 firms who indicated that their organizations were effective in using data and analytics, McKinsey found that construction of a data strategy was the number one contributing factor to their success. Being good at using data to drive decisions creates a meaningful profit advantage and those who are leaders indicated that the number one driver of their success was their data strategy.
This presentation will cover what a data strategy is, how to construct one, and how to get buy in from your executive team. The author is a former Fortune 500 Chief Data Officer and has held senior data roles at Capital One and Markel.
Here are a few helpful links for your data journey:
Free Data Investment ROI Template:
https://www.udig.com/digging-in/roi-calculator-for-it-projects/
Real world data use cases:
https://www.udig.com/our-work/?category=data
Contact Me:
https://www.udig.com/contact/
AI Modernization at AT&T and the Application to Fraud with DatabricksDatabricks
AT&T has been involved in AI from the beginning, with many firsts; “first to coin the term AI”, “inventors of R”, “foundational work on Conv. Neural Nets”, etc. and we have applied AI to hundreds of solutions. Today we are modernizing these AI solutions in the cloud with the help of Databricks and a variety of in-house developments. This talk will highlight our AI modernization effort along with its application to Fraud which is one of our biggest benefitting applications.
Two hour lecture I gave at the Jyväskylä Summer School. The purpose of the talk is to give a quick non-technical overview of concepts and methodologies in data science. Topics include a wide overview of both pattern mining and machine learning.
See also Part 2 of the lecture: Industrial Data Science. You can find it in my profile (click the face)
DAS Slides: Building a Data Strategy - Practical Steps for Aligning with Busi...DATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task. The opportunity in getting it right can be significant, however, as data drives many of the key initiatives in today’s marketplace: digital transformation, marketing, customer centricity, and more. This webinar will help de-mystify Data Strategy and Data Architecture and will provide concrete, practical ways to get started.
Ross Chayka. Gartner Hype Cycle
Ross Chayka's personal websites:
UA - https://rchayka.one
EN - https://rosschayka.com
LIn - https://www.linkedin.com/in/rchayka
At Neo4j we believe that ‘Graphs Are Everywhere’. In this session, we’ll be looking specifically at graphs within the Financial Services industry. We’ll review the types of data that are typically available within a bank, illustrate the graphs can be formed from that data, and discuss the use cases that those graphs can enable and support.
The use cases presented will include Anti-Money Laundering and Fraud Detection and Prevention (including integration with AI and Machine Learning technologies), Regulatory Compliance (such as BCBS 239 and GDPR), Customer 360 View, Master Data Management, and Identity and Access Management.
Many players in the Financial Services industry already rely on Neo4j's graph database: such as Lending Club, the world's largest microservices credit marketplace, for Network and IT, the big German insurance company die Bayerische for graph-based search, Cerved for Master Data Management, Wobi for price comparison and real-time recommendation, or UBS for Identity and Access Management.
This course covers in detail the technical principles & concepts behind blockchain. In addition, it seeks to provide you with the insights and deep understanding of the various components of blockchain technology, and enables you to determine for yourself how to best leverage and exploit blockchain for your project, organisation or start-up.
Link - https://www.experfy.com/training/courses/blockchain-technology-fundamentals
BI Consultancy - Data, Analytics and StrategyShivam Dhawan
The presentation describes my views around the data we encounter in digital businesses like:
- Looking at common Data collection methodologies,
-What are the common issues within the decision support system and optimiztion lifecycle,
- Where are most of failing?
and most importantly, "How to connect the dots and move from Data to Strategy?"
I work with all facets of Web Analytics and Business Strategy and see the structures and governance models of various domains to establish and analyze the key performance indicators that allow you to have a 360º overview of online and offline multi-channel environment.
Apart from my experience with the leading analytic tools in the market like Google Analytics, Omniture and BI tools for Big Data, I am developing new solutions to solve complex digital / business problems.
As a resourceful consultant, I can connect with your team in any modality or in any form that meets your needs and solves any data/strategy problem.
Data Science - Part I - Sustaining Predictive Analytics CapabilitiesDerek Kane
This is the first lecture in a series of data analytics topics and geared to individuals and business professionals who have no understand of building modern analytics approaches. This lecture provides an overview of the models and techniques we will address throughout the lecture series, we will discuss Business Intelligence topics, predictive analytics, and big data technologies. Finally, we will walk through a simple yet effective example which showcases the potential of predictive analytics in a business context.
Data storytelling connects two worlds, As a result, storytelling is fast becoming the most effective way to reach people. Storytelling is the new way to get consumers and the answers they need about you and your business. It's also becoming a vital element of a strong campaign, and stories are a proven medium for teaching, explaining, and influencing. Data storytelling is a communication technique that uses data as the guiding source. For more just check it out this presentation, and you will get best ideas from it. For more email us on info@nds.group
Enabling Success With Big Data - Driven Talent AcquisitionDavid Bernstein
Adopting an evidence-based recruitment marketing strategy is not just reserved for large employers. In fact, a targeted sourcing strategy can in some ways have a greater impact on small and mid-size businesses who need to allocate already-limited resources to the areas that will provide the most value. Ultimately, hiring the right candidate means profitability for your business. How can talent acquisition professionals gain the insights their organizations need to make better-informed decisions about their recruitment marketing efforts?
There is a gap between data and analytics growth and firm growth – Investing in data and analytics through the lens of the customer equity framework can help close that gap.
Difference B/w Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data
The most popular and rapidly evolving technologies in the world are Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data. All firms, large and small, are increasingly looking for IT experts who can filter through the data and help with the efficient implementation of sound business decisions. In light of the current competitive environment, Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data are essential technologies that drive company growth and development. In this topic, “Difference Between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, And Big Data,” we will examine the key definitions and skills needed to obtain them. We will also examine the main differences between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data. So let’s start by briefly introducing each concept.
Data Analysis vs Data Analytics
Data Analysis is the process of analyzing, organizing, and manipulating a collection of data to extract relevant information. An “Analytics platform” is a piece of software that enables data and statistics to be generated and examined systematically, whereas a “business analyst” is a person who applies an analytical method to a collection of information for a specific goal. As this is becoming increasingly popular the corporate sector has started to broadly accept it. Data Analysis makes it easy to understand the data. It provides an important historical context for understanding what has occurred recent past. To master Power BI check out Power BI Online Course
Data Analytics includes both decision-making processes and performance enhancement through relevant forecasts. Businesses may utilize data analytics to enhance business decisions, evaluate market trends, and analyze customer satisfaction, all of which can lead to the creation of new, enhanced products and services. Using Data Analytics, it is possible to make more accurate forecasts for the future by examining previous data. To master Data Analytics Skills visit Data Analytics Course in Pune
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Data Analytics
Data Analysis
Data Analytics is analytics that is used to make conclusions based on data.
Data Analysis is a subset of data analytics that is used to analyze data and derive specific insights from it.
Using historical data and customer expectations, businesses may develop a solid business strategy.
Making the most of historical data helps organizations identify new possibilities promote business growth and make more effective decisions.
The term “data analytics” refers to the collecting and assessment of data that involves one or more users.
Artificial Intelligence: Evolution and its Impact on MarketingZenith
In one real-life minute, Google receives over 4 million searches, 2.5 million pieces of content are shared on Facebook, and Pandora users listen to 61 thousand hours of music. The amount of data that is produced in a day is massive that the world has began to turn to artificial intelligence to make use of this data. Read here to learn about the way that artificial intelligence is revolutionizing the use of big data and how this will impact the world of marketing and business.
What is business intelligence and where it is applicable is described in this presentation. The subject is offered as elective to BE IT students of Pune University.
Bigger and Better: Employing a Holistic Strategy for Big Data toward a Strong...IT Network marcus evans
Bigger and Better: Employing a Holistic Strategy for Big Data toward a Strong Value-Adding Proposition
by Patrick Hadley, Australian Bureau of Statistics at the Australian CIO Summit 2014
Intro to Artificial Intelligence w/ Target's Director of PMProduct School
Given that Machine Learning (ML) is on every product enthusiast’s mind, this talk gave a broad view of the investment landscape for future innovation. Director of Product Management at Target, Aarthi Srinivasan, talked about macro AI themes & trends, how you can build your AI team and how to create a ML backed product vision.
Additionally, this talk armed the attendees with enough information to create your Point of View (POV) on how to incorporate AI into your business.
Designing Impactful Services and User Experience - Lim Wee KheeNUS-ISS
In this engaging talk, we explore crafting impactful user-centric services, revealing the design principles that drive exceptional experiences. From empathetic customer journeys to innovative interfaces, learn how design can create meaningful connections, inspiring you to revolutionise your approach and drive lasting change in user satisfaction and brand success.
Upskilling the Evolving Workforce with Digital Fluency for Tomorrow's Challen...NUS-ISS
In today's digital age, the key to true transformation lies in our people. This talk will highlight the importance of digital fluency, emphasizing that everyone in an organization is now a digital professional. By synergizing the fundamental digital skills ranging from an agile mindset to making data-informed decisions and design thinking, we will discuss how a digitally skilled workforce can propel organizations to drive digital transformation with new heights of value creation. Though widespread workforce upskilling presents its challenges, this talk offers innovative organizational learning approaches that may pave the way to success. Join us to find out how to shape the future of your organization where success is defined not just by technology but by a workforce fully equipped with digital competencies, ready to take on whatever the future holds.
How the World's Leading Independent Automotive Distributor is Reinventing Its...NUS-ISS
In this captivating session, we'll unveil the profound impact of AI, poised to revolutionise the business landscape. Prepare to shift your perspective, as we transition from the lens of a data scientist to the visionary mindset of a product manager. We're about to demystify the captivating world of Generative AI, dispelling myths and illuminating its remarkable potential. We will also delve into the pioneering applications that Inchcape is leading, pushing the boundaries of what's achievable. Join us for an exhilarating journey into the future of AI, where professionalism meets unparalleled excitement, and innovation takes center stage!
The Importance of Cybersecurity for Digital TransformationNUS-ISS
In the rapidly evolving landscape of digital transformation, the importance of cybersecurity cannot be overstated. As organizations embrace digital technologies to enhance their operations, innovate, and connect with customers in new and dynamic ways, they simultaneously become more vulnerable to cyber threats.
This talk will discuss the importance of having a well thought through approach in dealing with cybersecurity in the form of a strategy that lays out the various programmes and initiatives that will underpin a secure and resilient digital transformation journey. Not surprisingly, having a pool of well-trained cybersecurity personnel is one of the key ingredient in a cyber strategy as exemplified in Singapore's own national cybersecurity strategy.
Architecting CX Measurement Frameworks and Ensuring CX Metrics are fit for Pu...NUS-ISS
Join us for a deep dive into the art of architecting Customer Experience (CX) measurement frameworks and ensuring that CX metrics are precisely tailored for their intended purpose. In this engaging session, you'll walk away with actionable insights and a tangible plan for refining your measurement strategies. Discover how to craft CX measurement frameworks that align seamlessly with your business objectives, ensuring that your metrics deliver meaningful and robust insights. Whether you're seeking to enhance customer satisfaction, optimise processes, or drive innovation, this session will provide you with potential approaches and practical steps to bolster the effectiveness and relevance of your CX metrics. It's your blueprint for creating a customer-centric roadmap to success.
Understanding GenAI/LLM and What is Google Offering - Felix GohNUS-ISS
With the recent buzz on Generative AI & Large Language Models, the question is to what extent can these technologies be applied at work or when you're studying and how easy is it to manage/develop your own models? Hear from our guest speaker from Google as he shares some insights into how industries are evolving with these trends and what are some of Google's offerings from Duet AI in Google Workspace to the GenAI App Builder on Google Cloud.
Digital Product-Centric Enterprise and Enterprise Architecture - Tan Eng TszeNUS-ISS
Enterprises striving to unlock value through digital products face a pivotal shift towards product-centric management, a transformation that carries its share of challenges. To navigate this journey successfully, close collaboration between Enterprise Architects and Digital Product Managers is essential. Together, they can craft the ideal strategy to deliver digital products on a grand scale. Join us in this session as we shed light on the critical interactions and activities that foster synergy between Enterprise Architects and Digital Product Managers. Discover how this collaboration paves the way for effective product-centric management, enabling enterprises to harness the full potential of their digital offerings.
Emerging & Future Technology - How to Prepare for the Next 10 Years of Radica...NUS-ISS
We find ourselves in an era of exponential growth and transformation. The relentless pace of technological advancement is reshaping our world at a rate never seen before, making it increasingly challenging to stay abreast of these rapid developments. Join us for an insightful talk where we embark on a journey to explore the most significant technology trends set to unfold over the next decade. These trends promise to be nothing short of seismic, with the power to reshape every facet of our lives, from the way we work and learn to how we forge relationships and structure our society. Prepare to be enlightened as we delve into a future where the very fabric of our existence is on the brink of transformation. This talk is your compass to navigate the uncharted territory of tomorrow's world, and it's an opportunity you won't want to miss.
Beyond the Hype: What Generative AI Means for the Future of Work - Damien Cum...NUS-ISS
The hottest topic in the tech world right now is generative AI. In this session, we go beyond the hype to delve into honest answers about how generative AI is impacting the future of work. This is an important topic for all digital leaders to have a thorough understanding of when driving digital transformation.
Supply Chain Security for Containerised Workloads - Lee Chuk MunnNUS-ISS
Containers have emerged as an indispensable component of modern cloud-native applications, serving diverse roles from development environments to application distribution and deployment on platforms like Azure's App Service and Kubernetes. In this presentation, we will delve into a suite of powerful tools designed to ensure the adoption of best practices in container management. You'll gain insights into how to scan container images rigorously, identifying and mitigating vulnerabilities effectively. We'll also explore the art of generating comprehensive software bill of materials (SBOM) for your containers and the significance of signing container images for enhanced security. The ultimate goal of this presentation is to empower you with the knowledge and skills necessary to seamlessly integrate these tools and practices into your CI (Continuous Integration) pipelines. By the end of this session, you'll be well-equipped to fortify your container workflows, delivering secure and robust cloud-native applications that thrive in today's dynamic digital landscape.
The future is always uncertain. To be truly future-ready, companies need the ability to quickly learn and adapt and to foster a culture of continuous curiosity and experimentation. But how can we facilitate rapid learning throughout the organisation? What will the future of learning look like for you? How can we ensure our organisations become engines of growth through learning?
The future is always uncertain. To be truly future-ready, companies need the ability to quickly learn and adapt and to foster a culture of continuous curiosity and experimentation. But how can we facilitate rapid learning throughout the organisation? What will the future of learning look like for you? How can we ensure our organisations become engines of growth through learning?
Site Reliability Engineer (SRE), We Keep The Lights On 24/7NUS-ISS
There are many phases in the software development cycle, from requirements to development and testing, but at the tail of the process, is an often overlooked aspect: deployment and delivery. With the paradigm shift of delivering on-site software to offering software-as-a-service, Site Reliability Engineering is beginning to take a greater role in product delivery.
This session aims to give a glimpse of the work that goes into site reliability engineering (SRE) and effort that goes into keeping a service going 24/7.
Product Management in The Trenches for a Cloud ServiceNUS-ISS
More often than not, people’s perception of Product Management is usually centred around the definition, management and prioritisation of software features and functionality. While that is largely true, it is also one of many things that a Product Manager needs to focus on, given limited time and resources.
This session aims to provide an unfiltered view of how Product Management looks like in the context of Enterprise Cloud Applications development, the challenges confronting Product Managers, and the tradeoff decisions to be made in order to overcome these challenges.
All this, while shipping a working product with each release that will surprise and delight the end user.
With the use of Predictive Analytics, companies are able to predict future trends based on existing available data. The actionable business predictions can help companies achieve cost savings, higher revenue, better resource allocation and efficiency. Predictive analytics has been used in various sectors such as banking & finance, sales & marketing, logistics, retail, healthcare, F&B, etc. for various purposes.
Get set to learn more about the different stages of predictive analytics modelling such as data collection & preparation, model development & evaluation metrics, and model deployment considerations will be discussed.
In this digital transformation era, we have seen the rise of digital platforms and increased usages of devices particularly in the area of wearables and the Internet of Things (IoT). Given the fast pace change to the IoT landscape and devices, data has become one of the important source of truth for analytics and continuous streaming of data from sensors have also emerged as one of the fuel that revolutionise the emergence of IoT. These includes health telematics, vehicle telematics, predictive maintenance of equipment, manufacturing quality management, consumer behaviour, and more. With this, we will give you an introduction on how to leverage the power of data science and machine learning to understand and explore feature engineering of IoT and sensor data.
Diagnosing Complex Problems Using System ArchetypesNUS-ISS
In today’s VUCA world, we are faced with problems coming in fast and furious. In order to resolve such problems quickly, we need to first understand the problems. One of the techniques to understand complex problem is through the use of system archetypes. System archetypes are patterns of behaviour of a system. Let’s us explore some of the system archetypes in this session as well as tips on how to resolve them.
Satisfying the ‘-ilities’ of an Enterprise Cloud ServiceNUS-ISS
‘Feature is DONE !’ A regular statement that is typically shared by an overjoyed engineer who is declaring the completion of a feature that he/she has implemented and has been approved by a test engineer. BUT is it ‘Done, Done ?!!’ Engineering an enterprise cloud services would require teams to satisfy the ‘ilities’ requirements. If you are wondering what they are, how engineering teams tackle them and satisfy these requirements, this session will give you an insight of the work and investment needed for a feature to be ‘Done’ in the enterprise cloud world.
Preparing and Acing your Kubernetes CertificationNUS-ISS
Kubernetes is one of the 'go to' platform for deploying containerised workloads. A certification in this area is a recognition by an industry body of your skill. The assessment itself is a grueling two hours task oriented assessment where you will need to solve multiple tasks from the command line.
In this talk, NUS-ISS Chief of StackUp Programme will talk about the 3 Ps, - Preparation, Practices and Process in your certification journey.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
Overview of Data and Analytics Essentials and Foundations
1. Data and Analytics Essentials
Christine CHEONG & Brandon NG
#ISSLearningFest
2. The Essential of Data and Analytics
• Data and Analytics
• Analytics Maturity Model
• Descriptive Analytics
• Predictive Analytics
• Prescriptive Analytics
#ISSLearningFest
Icons made by Vectors Market, http://www.flaticon.com/authors/vectors-market
is licensed by Creative Commons BY 3.0, http://creativecommons.org/licenses/by/3.0/
4. “Without data
we’re just another person with an
opinion.”
– W. Edwards Deming
http://www.meliorgroup.com/without-data-just-another-person-with-opinion/
6. from
HiPPO = Highest Paid Personnel’s Opinion
to
Data Driven Decision Making Organization
7. Worlds Collide as Augmented Analytics Draws Analytics, BI and Data Science Together
Published: 10 March 2020 ID: G00463513, Analyst(s): Carlie Idoine
Descriptive/ Diagnostics/
Predictive/ Prescriptive
Analytics Models
Reports / Dashboards
Consumes
Produces
Descriptive/ Diagnostics/
Predictive/ Prescriptive
Analytics Models
Reports / Dashboards
Business Analyst /
Business Intelligence Analyst Data Analyst / Data Scientist
Analytics Role
8. Expanding,
Understanding &
Investigating
• Data scientist
• Data engineers
• Business analyst
Exploration &
Discovery
• Data scientist
• Data engineer
Foundational Core
(Core operational
processes)
• Business user
• Business analyst
Establishing Value
• Data engineer
• Business analyst
Data
Known Unknown
Business
Questions
Unknown
Known
Solve Your Data Challenges With the Data Management Infrastructure
Model, Refreshed: 3 April 2019 | Published: 19 October 2017 ID:
G00336474 Analyst(s): Adam Ronthal, Nick Heudecker
Data Analytics
Roles and Skills
Data Management
Infrastructure Model
9. Role of Analytics in Decision Management
Analytically
Assisted Decision
Making
Decision
Management
How Companies Succeed at Decision Management, Published: 19 October 2018 ID: G00341368, Analyst(s): W. Roy Schulte, Erick Brethenoux
Descriptive Analytics
- Explain what happened
Diagnostic Analytics
- Explore how it happened
Predictive Analytics
- Explore what is likely to happen
next or in the future
Prescriptive Analytics
- Specify what to do, or
automatically trigger a response
does not specify what to do
10. Worlds Collide as Augmented Analytics Draws Analytics, BI and Data Science Together
Published: 10 March 2020 ID: G00463513, Analyst(s): Carlie Idoine
11. Intersection & Interrelationship of
Data, Analytics and Decision
Decision
•Change, movement
Wisdom
•Understanding, integrated, actionable
Knowledge
•contextual, synthesized
Information
•Useful, organized, structured
Data
•Signals/know-nothing
Descriptive
Analytics
FUTURE
What Action?
- direction
What is the
best?
- Principles
PAST
Why?
- patterns
What?
- relationships
Diagnostics
Analytics
Predictive
Analytics
Prescriptive
Analytics Data Analysis
Artificial
Intelligence,
Machine
Learning,
Deep Learning,
etc
Data Integration
Big data, cloud
computing, etc
Data Collection
IoT, sensor
network, mobile
devices, etc
13. Hype Cycle for Analytics and Business Intelligence, 2022, 1
4 July 2022 - ID G00770971, By Analyst(s): Peter Krensky
14. Business Intelligence
and Analytics for
Decision-Making
Business
Intelligence
and Analytics
right
information
right person
right time
right
quantity
right
quality
right place
27. Marketing Decisions
Managerial decisions –whether to advertise, change prices, launch
a new product or service, assess impact of marketing and
communication effectiveness etc
• What factors or product formulation are important in driving
product/brand choice?
• What prices to charge for different range of products?
• Which advertising messages/campaigns are effective in
deepening engagement with stakeholders?
• Which customer/stakeholder segments should we target to drive
conversion and/improve profitability?
28. Marketing Trends
#ISSLearningFest
• Marketing-mix decisions are increasingly made quantitatively instead
of qualitatively. Pricing decisions are routinely made using dynamic
quantitative models, so are assortment, channel, and location decisions.
• Customer Engagement - Machine learning algorithms extract consumer
preferences from massive online data, and help create engaging text
and images to attract attention; intelligent agents assist customer
engagement to improve experience.
• Search engine is where many customer journeys begin. While keyword has been the dominant form
of online search, machine learning methods are making searches based on other content types within
reach eg with voice recognition, natural language processing, and text-to-speech capabilities
• Recommending the right products to the interested consumers can significantly improve marketing
performance. Deep neural networks and embedding methods have been leveraged to further
enhance performance.
Machine learning and AI in marketing – Connecting computing power to human insights by Liye Ma a,⁎, Baohong Sun b
29. Marketing Trends
• Product Development - Rapid experimentation and simulation for product and process innovation
• Go-to-market/commercialization - Real time analytics, dynamic pricing optimization, connected product innovation
• From Big Data to Small and Wide Data - statistical/machine learning, AI techniques
https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/why-tech-enabled-go-to-market-innovation-is-critical-for-industrial-companies
30. • Machine Learning arose as a subfield of Artificial Intelligence while Statistical Learning arose as a subfield of
Statistics. While statistical and econometric models with increasing levels of sophistication are being developed,
researchers have also turned to machine learning methods as a valuable alternative
• Machine Learning has a greater emphasis on large scale applications and prediction accuracywhile Statistical
Learning emphasizes models and their interpretability, and precision and uncertainty. But the distinction has become
more and more blurred, and there is a great deal of “cross-fertilization”.
• Balance between a theory-driven with a data-driven perspective by injecting human insights and domain
knowledge into the use of machine learning methods
Statistical and Machine Learning
Structured data is comprised of clearly defined data types with patterns that make
them easily searchable; while unstructured data is comprised of data that is usually not
as easily searchable, including formats like audio, video, and social media postings.
31. Statistical and Machine Learning Problems
Structured (eg quantitative demographic and behavioural data)
• Identify the effects of demographic and marketing data on insurance policy product purchase
• Predict housing prices based on sociodemographic and geospatial data
• Establish the relationship between marketing promotion (eg price, location, advertising etc on store level product sales)
Source : Introduction to Statistical Learning by Trevor Hastie and Robert Tibshirani, Second Edition 2021
32. Statistical and Machine Learning Problems
Unstructured (eg image, text, speech/voice data etc)
• Customize an email spam detection system based on frequently occurring words (features).
• Predict user interaction and engagement on social media based on image / facial recognition
• Predict positive vs negative sentiments based on attributes of internet movie ratings
• data from 4601 emails sent to an individual (named George, at HP labs,
before 2000). Each is labeled as s
p
amor email.
• goal: build a customized spam filter.
• input features: relative frequencies of 57 of the most commonly occurring
words and punctuation marks in these email messages.
Source : Introduction to Statistical Learning by Trevor Hastie and Robert Tibshirani, Second Edition 2021
33. Trends in Machine Learning
#ISSLearningFest
ML methods are well positioned to extract rich insights from rich data. While studies have frequently
analyzed text and image data, there are opportunities to focus on audio, video, and consumer tracking data, as
well as network data and data of hybrid formats.
Opportunities to broaden and extend usage of machine learning methods. While machine learning methods
have been used frequently for prediction and feature extraction, they can be harnessed for causal and
prescriptive analysis
Machine learning and AI in marketing – Connecting computing power to human insights by Liye Ma a,⁎, Baohong Sun b
34. Trends in Machine Learning
#ISSLearningFest
Opportunities to broaden and extend ML usage in the entire customer purchase journey, to develop decision-
support capabilities covering all aspects of marketing functions, from more strategic areas like brand positioning and
competitive analysis to operational areas like customer satisfaction/service delivery
Machine learning and AI in marketing – Connecting computing power to human insights by Liye Ma a,⁎, Baohong Sun b
35. Machine Learning Tasks
Supervised Learning (eg prediction and forecasting techniques)
• Outcome measurement Y (also called dependent variable, response, target) vs a set of predictors (features)
measured on a set of samples
• Regression vs Classification Problem
UnsupervisedLearning (eg segmentation and association)
• No outcome variable, just a set of predictors (features) measured on a set of samples.
• Find groups of samples that behave similarly, find features that behave similarly, find linear combinations of
features with the most variation. Useful as a pre-processing step for supervised learning.
36. Machine Learning Tasks
Semi-supervised Learning and Transfer Learning
• Semi-supervised – Output is known for only a subset of the data. The instances in the training dataset for which the output is
not observed are nonetheless used to improve learning eg through label propagation
• Transfer learning – Researchers leverage an existing model, trained using a different dataset or for a different
purpose. For example, image analysis where an existing model trained using a large set of images is updated
using the specific images of the research project
Active Learning
• Only limited training instances are available at first. The goal is to maximize the predictive accuracy while minimizing the
data requirement. Determining the most important instances is a key focus of active learning
• Reinforcement learning : The learning agent continuously interacts with the surrounding environment by taking actions and
observing feedback. The learning algorithm needs to determine the actions to take to both learn the environment’s
characteristics and craft optimal policy given the states.
37. Supervised Learning - Feature Selection Methods
Subset selection
We identify a subset of p predictors that we believe to be related to the response. We then fit a model using least
squares on the reduced set of variables.
Dimension Reduction
We project that p predictors into a M-dimensional subspace where M <p.
This is achieved by computing M different linear combinations or projections
of the variables. Then these M projections are used as predictors to fit a
linear regression model by least squares
Shrinkage (or Regularization) for large sparse data
We fit a model involving all p predictors, but the estimated coefficients
are shrunken towards zero relative to the least squares estimates. This
shrinkage (also known as regularization) has the effect of reducing
variance and can also perform variable selection
Source : Introduction to Statistical Learning by Trevor Hastie and Robert Tibshirani, Second Edition 2021
38. Feature Selection (Flexibility vs Interpretability)
Source : Introduction to Statistical Learning by Trevor Hastie and Robert Tibshirani, Second Edition 2021
Example: Income prediction based on socio-demographic survey data (eg age, education, seniority etc)
39. Feature Selection (Dimension reduction)
Source : Introduction to Statistical Learning by Trevor Hastie and Robert Tibshirani, Second Edition 2021
Example: Income prediction on socio-demographic and geo-location data
40. Feature Selection (Regularization)
Source : Introduction to Statistical Learning by Trevor Hastie and Robert Tibshirani, Second Edition 2021
Example: Credit risk assessment on demographic and behavioural data
41. Big data vary in shape. These call for different approaches
Big Data Learning Problems
Source : Introduction to Statistical Learning by Trevor Hastie and Robert Tibshirani, Second Edition 2021
42. Big Data Learning Problems
Example: IMDB (internet movie database) ratings using machine/deep learning
RNN - https://web.cs.dal.ca/~shali/project2.html
Many data sources are sequential in nature, and call for special treatment when building predictive models. For example,
documents such as book and movie reviews, newspaper articles and tweets. We can use the sequence of words occurring in a
document to make predictions about the label for the entire document (eg positive or negative sentiment). Machine/deep
learning approaches eg recurrent neural networks can be used for classification, sentiment analysis, and language translation.
43. Big Data Learning Problems
Example: Image recognition in social media context using machine/deep learning
CNN - https://www.semanticscholar.org/paper/Toward-Large-Scale-Face-Recognition-Using-Social-Stone-Zickler/2f2d69bdfaca54eb3a6ede3e5eb2c76713bb8064
Neural networks rebounded around 2010 with big successes in image classification. Around that time, massive databases of
labeled images were being accumulated, with ever-increasing numbers of classes. A special family of convolutional neural
networks (CNNs) has evolved for classifying images on a wide range of problems. CNNs mimic to some degree how humans
classify images, by recognizing specific features or patterns anywhere in the image that distinguish each particular object class.
44. Example: Online shopping analysis using models on large sparse data (B2C)
From Big to Small and Wide Data
Source : Introduction to Statistical Learning by Trevor Hastie and Robert Tibshirani, Second Edition 2021
A marketing analyst interested in understanding people’s online shopping
patterns could treat as features all of the search terms entered by users of
a search engine. This is sometimes known as the “bag-of-words” model.
The same researcher might have access to the search histories of only a
few hundred or a few thousand search engine users who have consented to
share their information with the researcher. For a given user, each of the p
search terms is scored present (0) or absent (1), creating a large binary
feature vector. Then n ≈ 1,000 and p is much larger.
45. Example: Webpage browsing analytics using models on large sparse webpage session information (B2C)
Quantcast is a digital marketing company. Data are five-minute internet sessions. Binary target is type of family (≤ 2 adults vs
adults plus children). 7 million features of session info (web page indicators and descriptors). Divided into training set (54M),
validation (5M) and test (5M).
All but 1.1M features could be screened because ≤ 3 nonzero values. Fit 100 models in 2 hours in R
Richest model had 42K nonzero coefficients, and explained 10% deviance (like R-squared).
From Big to Small and Wide Data
Source : Introduction to Statistical Learning by Trevor Hastie and Robert Tibshirani, Second Edition 2021
46. Observational vs Experimental Studies
In observational studies, researchers are only observers. They measure
what people do, or say they would do in a situation not of their making
(eg surveys and focus groups)
In contrast, when conducting experiments, researchers control the
important variables that influence consumer behavior to more precisely
observe the effect.
https://www.youtube.com/watch?v=qwfd8cf3_UY&feature=youtu.be
47. Observational studies/data
Data : Then and now
Data in the 80s-90s Data now
• Retail scanner
data
• Survey data
• Transactional/
• behavioral data
+ clickstream data
+ Social networking
+ Product review
+ Search data
+ Mobile
+ Text
Primary & secondary market research/trends (eg structured
vs unstructured data including social media)
Knowledge/Consumer immersion (eg observation
studies/ethnography, extracting value from connected products,
real-time analytics eg smart sensors etc)
Quantitative data (eg direct questioning, buy-response surveys,
transactional data)
50. Why experiments?
Experiments allow analysts to answer business questions related to
cause and effect.
It is important for the analyst to know whether she has an
“umbrella problem” or a “rain dance problem.” If all she wants
to know is whether or not she should carry an umbrella, then she
has a pure prediction problem and causal questions are of
secondary importance; she only needs to know whether the
probability of rain is high or low.
On the other hand, if there has been a long drought and she
wants to end it, prediction is of little value: causal questions are
of primary importance. If she wants to induce rainfall, she needs
to know what variables cause rain and then try to manipulate
those variables
51. Business Experiments
• A/B/n testing using hypothesis testing (eg compare landing pages
to see which one generates more sales)
• Multivariate analysis using predictive analytics (eg screening
designs and factorial designs, conjoint analysis)
https://www.youtube.com/watch?v=zFMgpxG-chM
52. Business Experiments
Right Customers, Right Channels, Right Comms Messages
(Example : Alcon case study)
https://www.edenspiekermann.com/case-studies/alcon-wearlenses/
54. Optimal mix of Data Science and
Machine Learning Techniques
Predictive
Predictions
•Probability of a specific
outcome
Forecasting
•Predicting a series of
outcomes over time
(univariate vs multivariate)
Simulation
•Predicting multiple
outcomes and
highlighting uncertainties
Prescriptive
Rules
•Predefined framework for
choosing between
alternatives
Optimisation
•Outcome-driven, constraint-
based evaluation of an
interdependent set of options
Decision
Making
Greater
Business
Impact
When and How to Use Advanced Analytics Techniques to Solve Business Problems, Published 17 September
2021 - ID G00750951, By Analyst(s): Carlie Idoine, Erick Brethenoux
55. Three Emergent AI Technologies
Pre-trained
AI Model
Optimization
Solver
Generative
AI
#ISSLearningFest
Quick Answer: What Three Emergent AI Technologies Will Have an Impact in 2022?,
Published 11 March 2022 - ID G00752286, Owen Chen
57. Application of Optimisation
Travelling Salesman Problem
Traffic and Shipment Routing Route (travel time, cost, distance) optimisation
Introduction to Genetic Algorithm & their application in data science
https://www.analyticsvidhya.com/blog/2017/07/introduction-to-genetic-algorithm/
Linear programming
(Genetic Algorithm)
Monte Carlo
Simulation
58. Inventory Optimisation and Simulation
Inventory Simulation using Monte Carlo Simulation
https://cloud.anylogic.com/model/b0156f6d-6c04-431b-b48d-1b875b2720e7?mode=SETTINGS
Monte Carlo
Simulation
61. Retail Analytics
In-Store Operational Excellence via Real Time Streaming Analytics
IoT powered Intelligent Retail, https://www.youtube.com/watch?v=n-ouKu9tNPM
62. Operational Analytics
Foot Traffic Analytics for Demand Planning and Management
using Queuing Theory / Model
https://www.channelnewsasia.com/commentary/singapore-slow-reopening-seniors-elderly-strategy-covid-19-2230601
https://www.channelnewsasia.com/singapore/covid-singapore-vaccine-vaccination-centre-behind-the-scenes-1882811
Queuing
Model
63. Operational Analytics
Foot Traffic Analytics for Demand Planning and Management using
Queuing Theory / Model
#ISSLearningFest
https://www.todayonline.com/singapore/long-queues-supermarkets-after-
announcement-circuit-breakers-contain-covid-19
65. Give Us Your Feedback
#ISSLearningFest
Day 3 Programme
66. Survey:
Data and Analytics Essentials
#ISSLearningFest
https://docs.google.com/forms/d/e/1FAIpQLScayAdYauu-SwTwzOgKQhpBpK8tsCrv-3cJhYycdlAWH9WThQ/viewform?usp=sf_link