The project is based on the following-
1) Internal rate of return (IRR) is the rate of return that will equate the present value of a multi-year cash flow with the cost of investing in a project
The IRR is the discount rate that renders the NPV of the project equal to zero
2) Profitability index also called as Benefit- Cost ratio or desirability factor is relationship between present value of cash inflow and the present value of cash outflow.
How did your implementation go last year? In this session, we will cover issues that we or our clients encountered during the implementation of GASB 68 and 71. We will also cover anticipated challenges, new information from actuaries, as well as sample journal entries in this first year after implementation. Presenter Amy Myer, CPA, Audit Partner
Roger Sutton CEO, CERA - speaking at Seismics and the City 2014SmartNet
Roger Sutton CEO, CERA - speaking at Seismics and the City 2014
Building Momentum
Local and central government, business and media perspectives on the Canterbury Recovery and Rebuild.
What is your take on where we are with the rebuild?
What are positive signs of progress and what's in the pipeline?
What are the main obstacles and how can they be resolved?
The document discusses SQL query optimization and why it is so difficult. It begins by noting that there are often many possible execution plans for a given query, sometimes numbering in the millions. The query optimizer must select the most efficient plan by estimating the cost of each possible plan. This involves estimating predicate selectivities using statistics like histograms, and calculating estimates of the CPU and I/O costs for each operator. While query optimization is a fundamental task, after decades of research it remains challenging due to the complexity of modern hardware, software and queries.
The document defines the Define phase of a Lean Six Sigma project. It includes templates for key Define activities like developing a project charter, scope statement, voice of the customer, SIPOC diagram, CTQ tree, schedule, business case, communication plan, current status report, issue log, and sign-off sheet. The Define phase establishes the foundation for a project by understanding the problem, customers, processes, metrics and goals.
Lean six sigma executive overview (case study) templatesSteven Bonacorsi
This case study describes a project to improve the average speed to answer calls at a retail business. The project team analyzed call data, identified root causes such as call type and time of day, and implemented cross-training and staffing changes. These improvements reduced customer downtime costs by $150,000 annually and increased the process sigma level. Key tools used in the project included data collection, analysis of call times, and control charts to monitor ongoing performance.
Value stream mapping is a practical and highly effective way to learn to see and resolve disconnects, redundancies, and gaps in how work gets done.
This VSM project template helps you and your project team to put together a "storyboard" for effective presentation to your key stakeholders. It includes four key phases:
1) Define and pick product/service family
2) Create a current state map
3) Develop a future state map
4) Develop an implementation plan
This document consists of a VSM project template in Powerpoint format and a set of Excel templates comprising VSM charter, Results table, Implementation Plan and common VSM icons.
1. The document discusses how to lay the foundation for a smooth robotic process automation (RPA) implementation through various steps such as performing an organizational assessment, defining a target operating model, mapping stakeholders, assessing the automation portfolio, and building, delivering, and supporting RPA processes.
2. It emphasizes the importance of understanding what RPA can and cannot do, selecting the right tools, integrating with the enterprise, and allowing time for adoption. RPA requires a change in mindset and redeploying staff from routine tasks to more value-added activities.
3. The recipe for a smooth RPA implementation includes embedding and scaling the automation by creating a reference architecture, capturing the business case and benefits plan, and
The project is based on the following-
1) Internal rate of return (IRR) is the rate of return that will equate the present value of a multi-year cash flow with the cost of investing in a project
The IRR is the discount rate that renders the NPV of the project equal to zero
2) Profitability index also called as Benefit- Cost ratio or desirability factor is relationship between present value of cash inflow and the present value of cash outflow.
How did your implementation go last year? In this session, we will cover issues that we or our clients encountered during the implementation of GASB 68 and 71. We will also cover anticipated challenges, new information from actuaries, as well as sample journal entries in this first year after implementation. Presenter Amy Myer, CPA, Audit Partner
Roger Sutton CEO, CERA - speaking at Seismics and the City 2014SmartNet
Roger Sutton CEO, CERA - speaking at Seismics and the City 2014
Building Momentum
Local and central government, business and media perspectives on the Canterbury Recovery and Rebuild.
What is your take on where we are with the rebuild?
What are positive signs of progress and what's in the pipeline?
What are the main obstacles and how can they be resolved?
The document discusses SQL query optimization and why it is so difficult. It begins by noting that there are often many possible execution plans for a given query, sometimes numbering in the millions. The query optimizer must select the most efficient plan by estimating the cost of each possible plan. This involves estimating predicate selectivities using statistics like histograms, and calculating estimates of the CPU and I/O costs for each operator. While query optimization is a fundamental task, after decades of research it remains challenging due to the complexity of modern hardware, software and queries.
The document defines the Define phase of a Lean Six Sigma project. It includes templates for key Define activities like developing a project charter, scope statement, voice of the customer, SIPOC diagram, CTQ tree, schedule, business case, communication plan, current status report, issue log, and sign-off sheet. The Define phase establishes the foundation for a project by understanding the problem, customers, processes, metrics and goals.
Lean six sigma executive overview (case study) templatesSteven Bonacorsi
This case study describes a project to improve the average speed to answer calls at a retail business. The project team analyzed call data, identified root causes such as call type and time of day, and implemented cross-training and staffing changes. These improvements reduced customer downtime costs by $150,000 annually and increased the process sigma level. Key tools used in the project included data collection, analysis of call times, and control charts to monitor ongoing performance.
Value stream mapping is a practical and highly effective way to learn to see and resolve disconnects, redundancies, and gaps in how work gets done.
This VSM project template helps you and your project team to put together a "storyboard" for effective presentation to your key stakeholders. It includes four key phases:
1) Define and pick product/service family
2) Create a current state map
3) Develop a future state map
4) Develop an implementation plan
This document consists of a VSM project template in Powerpoint format and a set of Excel templates comprising VSM charter, Results table, Implementation Plan and common VSM icons.
1. The document discusses how to lay the foundation for a smooth robotic process automation (RPA) implementation through various steps such as performing an organizational assessment, defining a target operating model, mapping stakeholders, assessing the automation portfolio, and building, delivering, and supporting RPA processes.
2. It emphasizes the importance of understanding what RPA can and cannot do, selecting the right tools, integrating with the enterprise, and allowing time for adoption. RPA requires a change in mindset and redeploying staff from routine tasks to more value-added activities.
3. The recipe for a smooth RPA implementation includes embedding and scaling the automation by creating a reference architecture, capturing the business case and benefits plan, and
This report recommends buying Insight Enterprises (NSIT) stock with a price target of $36.52. It summarizes Insight's business as a leading global provider of IT solutions and products for commercial and government clients. Financially, Insight has strong free cash flow, revenue growth, and acquires companies to expand its offerings. A discounted cash flow valuation implies the stock is undervalued with a fair value estimate of $47.41 per share.
The document discusses the Improve phase of the Lean Six Sigma methodology. It provides an overview of the key tools and activities used in the Improve phase, including identifying and prioritizing root causes, developing and selecting solutions, implementing pilots, and developing implementation plans. It also discusses tollgate reviews, which are checkpoints to review progress. The Improve phase aims to develop, test, and select solutions to address the root causes identified in the Analyze phase in order to meet the project goals.
The document discusses the Improve phase of the Lean Six Sigma methodology. It provides an overview of the key tools and activities used in the Improve phase, including identifying and prioritizing root causes, developing and selecting solutions, implementing pilots, and developing implementation plans. It also discusses tollgate reviews, which are checkpoints to review progress. The Improve phase aims to develop, test, and select solutions to address the root causes identified in the Analyze phase in order to meet the project goals.
SCOR uma realidade em Logística. O modelo de pratica de negócio mais atualizado produzido de forma colaborativa por grandes empresas do mercado internacioal e organizado pelo SCC Supply Chain Council.
The document outlines copyright restrictions for materials from Six Sigma Simplified. Videos and guides cannot be copied, but slides can be shown and customized as long as the copyright is not removed. A company can play videos for employees but not outside parties and cannot charge others to view videos. The document then discusses using a balanced scorecard approach to focus on financial growth, customer satisfaction, learning and growth, and quality/productivity. It provides examples of objectives, measures, and targets for each area. Finally, it outlines a Six Sigma approach to identify problems, sponsor teams to conduct root cause analysis, develop and implement solutions, and measure results.
Recommender systems help users deal with overwhelming choices by providing personalized recommendations. They are commonly used by websites like Amazon, Netflix, and YouTube. Research on recommender systems has grown significantly over the past 20 years. Common recommendation models include collaborative filtering, which predicts ratings based on similar users or items, and matrix factorization, which represents users and items as vectors in a latent space. Transfer learning techniques allow knowledge from related domains to improve recommendations for new users or items.
Recommender systems help users deal with large amounts of options by providing personalized recommendations. They are commonly used by websites like Amazon, Netflix, and YouTube. Twenty years of research on recommender systems has led to many different recommendation models, including collaborative filtering, content-based filtering, knowledge-based, and hybrid approaches. Collaborative filtering uses user ratings and preferences to find similar users or items and provide recommendations. It has been widely used by many companies and responsible for a large portion of their sales and views.
The document discusses supply chain management concepts including the SCOR (Supply Chain Operations Reference) model. SCOR is a process reference model developed by the Supply Chain Council to help companies define and measure supply chain processes. It provides a framework to measure performance at different levels, from overall supply chain metrics to more granular process metrics. The document also discusses how benchmarking with SCOR allows companies to compare their supply chain performance to peers.
SQL Query Optimization: Why Is It So Hard to Get Right?Brent Ozar
The query optimizer's role is to transform SQL queries into efficient execution plans by:
1) Enumerating logically equivalent query plans through equivalence rules.
2) Enumerating physical execution plans for each logical plan.
3) Estimating the costs of each physical plan by estimating predicate selectivities and operator costs.
4) Selecting the physical plan with the lowest estimated cost to run the query.
Embracing service-level-objectives of your microservices in your Cl/CDNebulaworks
Embracing service-level-objectives of your microservices in your Cl/CD
This presentation discusses defining service level objectives (SLOs) for microservices and using them to automate quality gates in continuous integration/continuous delivery (CI/CD) pipelines. It presents the open-source Keptn framework, which can provide quality gates for microservices running on Kubernetes clusters based on defined SLOs. The presentation covers how SLOs and service level indicators (SLIs) can help prevent bad code from reaching production and configure alerts, and demonstrates Keptn's automated SLO evaluation and a use case for performance as a self-service tool.
Movie revenue prediction from IMDB dataset. The slides include how I clean up data, perform EDA analysis, and build up models. All of the codes are included in my Github (https://github.com/ChungHsuanKao/1stCapstoneProject_github)
This document provides an overview of Six Sigma, including its history and development, key principles and goals, methodology processes, roles and team structure, metrics, statistical tools, and potential project types. Six Sigma aims to reduce defects through a data-driven DMAIC (Define, Measure, Analyze, Improve, Control) approach. It was pioneered by Motorola in the 1980s and later adopted by other large companies to drive process improvement. The document outlines the Six Sigma methodology and how it can be applied to any process to meet customer requirements and drive business results.
The document discusses optimization approaches for credit limit management. It begins with a background on traditional credit limit change programs and efforts to improve decisions. The ideal credit limit optimization (CLO) solution is described as identifying an optimal solution without lengthy testing, optimizing specific goals like profit, and accounting for uncertainty. Various CLO solution components are then outlined, including data requirements, action-effect models, optimization problem formulations, and deployment challenges. Both linear and non-linear programming optimization approaches are presented, along with discussions of robust optimization and Markov decision processes.
The document analyzes the baseline performance of Seresto's media channels and platforms based on 2021 data. It finds that display outperforms other channels for key metrics like impressions and clicks, while digital video underperforms despite a large budget share. To optimize spend efficiency, predictive modeling is recommended to analyze performance considering multiple dimensions like channel, platform, and objective simultaneously. Sample predictive models are provided for different media objectives that predict optimized spend allocations could significantly improve key performance metrics over the baseline.
Monetizing Risks - A Prioritization & Optimization SolutionBlack & Veatch
This presentation explains a budget prioritization process and model that assists utilities with managing the important balance of asset/system performance, cost, and risk. Originally presentation at Texas Water 2015. Learn more at www.bv.com
This document discusses feature engineering and machine learning approaches for predicting customer behavior. It begins with an overview of feature engineering, including how it is used for image recognition, text mining, and generating new variables from existing data. The document then discusses challenges with artificial intelligence and machine learning models, particularly around explainability. It concludes that for smaller datasets, feature engineering can improve predictive performance more than complex machine learning models, while large datasets are better suited to machine learning approaches. Testing on a small travel acquisition dataset confirmed that traditional models with feature engineering outperformed neural networks.
This document discusses different software estimation techniques including LOC-based, FP-based, and process-based estimation. It provides examples of estimating effort, cost, and schedule for a CAD software project using each technique. LOC-based estimation involves decomposing the software into functions and estimating LOC for each. FP-based estimation involves estimating information domain values and complexity factors to calculate function points. Process-based estimation involves estimating effort for each software process activity and function. The document also discusses software metrics, size-oriented metrics using LOC as a normalization value, and function-oriented metrics using function points. It provides steps for establishing a goal-driven software metrics program.
Default Prediction & Analysis on Lending Club Loan DataDeep Borkar
This document analyzes lending club loan data to predict loan defaults and calculate default probabilities using models like gradient boosting, neural networks, and logistic regression. The goal is to make informed decisions about future loans to assess profitability. Various machine learning models are trained and tested on the data, with gradient boosting achieving the best results. The loans are then segmented by default risk to analyze the net present value of the portfolio under various hypothetical default rates.
The metrics that matter using scalability metrics for project planning of a d...Mary Chan
Have you expanded your organization across multiple locations, or are you a client that utilizes external partners that provide outsourcing services? Both have their "cost savings" challenge where cost savings analysis is often a topic well scrutinized. However, in the grand scheme of your organization, is it a metric that really matters? See actual analytics on multiple game projects and why cost savings isn't as important a metric when making informed decisions about project planning for scalable and distributed development. It's all about the Metrics that Matter.
Five 'Ps' to operate a building for sustainability: create a program, measure and monitor performance, develop policies, improve practices, and upgrade properties. Green building operations and maintenance improves bottom line and is a critical part of reducing your companies carbon footprint.
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
This report recommends buying Insight Enterprises (NSIT) stock with a price target of $36.52. It summarizes Insight's business as a leading global provider of IT solutions and products for commercial and government clients. Financially, Insight has strong free cash flow, revenue growth, and acquires companies to expand its offerings. A discounted cash flow valuation implies the stock is undervalued with a fair value estimate of $47.41 per share.
The document discusses the Improve phase of the Lean Six Sigma methodology. It provides an overview of the key tools and activities used in the Improve phase, including identifying and prioritizing root causes, developing and selecting solutions, implementing pilots, and developing implementation plans. It also discusses tollgate reviews, which are checkpoints to review progress. The Improve phase aims to develop, test, and select solutions to address the root causes identified in the Analyze phase in order to meet the project goals.
The document discusses the Improve phase of the Lean Six Sigma methodology. It provides an overview of the key tools and activities used in the Improve phase, including identifying and prioritizing root causes, developing and selecting solutions, implementing pilots, and developing implementation plans. It also discusses tollgate reviews, which are checkpoints to review progress. The Improve phase aims to develop, test, and select solutions to address the root causes identified in the Analyze phase in order to meet the project goals.
SCOR uma realidade em Logística. O modelo de pratica de negócio mais atualizado produzido de forma colaborativa por grandes empresas do mercado internacioal e organizado pelo SCC Supply Chain Council.
The document outlines copyright restrictions for materials from Six Sigma Simplified. Videos and guides cannot be copied, but slides can be shown and customized as long as the copyright is not removed. A company can play videos for employees but not outside parties and cannot charge others to view videos. The document then discusses using a balanced scorecard approach to focus on financial growth, customer satisfaction, learning and growth, and quality/productivity. It provides examples of objectives, measures, and targets for each area. Finally, it outlines a Six Sigma approach to identify problems, sponsor teams to conduct root cause analysis, develop and implement solutions, and measure results.
Recommender systems help users deal with overwhelming choices by providing personalized recommendations. They are commonly used by websites like Amazon, Netflix, and YouTube. Research on recommender systems has grown significantly over the past 20 years. Common recommendation models include collaborative filtering, which predicts ratings based on similar users or items, and matrix factorization, which represents users and items as vectors in a latent space. Transfer learning techniques allow knowledge from related domains to improve recommendations for new users or items.
Recommender systems help users deal with large amounts of options by providing personalized recommendations. They are commonly used by websites like Amazon, Netflix, and YouTube. Twenty years of research on recommender systems has led to many different recommendation models, including collaborative filtering, content-based filtering, knowledge-based, and hybrid approaches. Collaborative filtering uses user ratings and preferences to find similar users or items and provide recommendations. It has been widely used by many companies and responsible for a large portion of their sales and views.
The document discusses supply chain management concepts including the SCOR (Supply Chain Operations Reference) model. SCOR is a process reference model developed by the Supply Chain Council to help companies define and measure supply chain processes. It provides a framework to measure performance at different levels, from overall supply chain metrics to more granular process metrics. The document also discusses how benchmarking with SCOR allows companies to compare their supply chain performance to peers.
SQL Query Optimization: Why Is It So Hard to Get Right?Brent Ozar
The query optimizer's role is to transform SQL queries into efficient execution plans by:
1) Enumerating logically equivalent query plans through equivalence rules.
2) Enumerating physical execution plans for each logical plan.
3) Estimating the costs of each physical plan by estimating predicate selectivities and operator costs.
4) Selecting the physical plan with the lowest estimated cost to run the query.
Embracing service-level-objectives of your microservices in your Cl/CDNebulaworks
Embracing service-level-objectives of your microservices in your Cl/CD
This presentation discusses defining service level objectives (SLOs) for microservices and using them to automate quality gates in continuous integration/continuous delivery (CI/CD) pipelines. It presents the open-source Keptn framework, which can provide quality gates for microservices running on Kubernetes clusters based on defined SLOs. The presentation covers how SLOs and service level indicators (SLIs) can help prevent bad code from reaching production and configure alerts, and demonstrates Keptn's automated SLO evaluation and a use case for performance as a self-service tool.
Movie revenue prediction from IMDB dataset. The slides include how I clean up data, perform EDA analysis, and build up models. All of the codes are included in my Github (https://github.com/ChungHsuanKao/1stCapstoneProject_github)
This document provides an overview of Six Sigma, including its history and development, key principles and goals, methodology processes, roles and team structure, metrics, statistical tools, and potential project types. Six Sigma aims to reduce defects through a data-driven DMAIC (Define, Measure, Analyze, Improve, Control) approach. It was pioneered by Motorola in the 1980s and later adopted by other large companies to drive process improvement. The document outlines the Six Sigma methodology and how it can be applied to any process to meet customer requirements and drive business results.
The document discusses optimization approaches for credit limit management. It begins with a background on traditional credit limit change programs and efforts to improve decisions. The ideal credit limit optimization (CLO) solution is described as identifying an optimal solution without lengthy testing, optimizing specific goals like profit, and accounting for uncertainty. Various CLO solution components are then outlined, including data requirements, action-effect models, optimization problem formulations, and deployment challenges. Both linear and non-linear programming optimization approaches are presented, along with discussions of robust optimization and Markov decision processes.
The document analyzes the baseline performance of Seresto's media channels and platforms based on 2021 data. It finds that display outperforms other channels for key metrics like impressions and clicks, while digital video underperforms despite a large budget share. To optimize spend efficiency, predictive modeling is recommended to analyze performance considering multiple dimensions like channel, platform, and objective simultaneously. Sample predictive models are provided for different media objectives that predict optimized spend allocations could significantly improve key performance metrics over the baseline.
Monetizing Risks - A Prioritization & Optimization SolutionBlack & Veatch
This presentation explains a budget prioritization process and model that assists utilities with managing the important balance of asset/system performance, cost, and risk. Originally presentation at Texas Water 2015. Learn more at www.bv.com
This document discusses feature engineering and machine learning approaches for predicting customer behavior. It begins with an overview of feature engineering, including how it is used for image recognition, text mining, and generating new variables from existing data. The document then discusses challenges with artificial intelligence and machine learning models, particularly around explainability. It concludes that for smaller datasets, feature engineering can improve predictive performance more than complex machine learning models, while large datasets are better suited to machine learning approaches. Testing on a small travel acquisition dataset confirmed that traditional models with feature engineering outperformed neural networks.
This document discusses different software estimation techniques including LOC-based, FP-based, and process-based estimation. It provides examples of estimating effort, cost, and schedule for a CAD software project using each technique. LOC-based estimation involves decomposing the software into functions and estimating LOC for each. FP-based estimation involves estimating information domain values and complexity factors to calculate function points. Process-based estimation involves estimating effort for each software process activity and function. The document also discusses software metrics, size-oriented metrics using LOC as a normalization value, and function-oriented metrics using function points. It provides steps for establishing a goal-driven software metrics program.
Default Prediction & Analysis on Lending Club Loan DataDeep Borkar
This document analyzes lending club loan data to predict loan defaults and calculate default probabilities using models like gradient boosting, neural networks, and logistic regression. The goal is to make informed decisions about future loans to assess profitability. Various machine learning models are trained and tested on the data, with gradient boosting achieving the best results. The loans are then segmented by default risk to analyze the net present value of the portfolio under various hypothetical default rates.
The metrics that matter using scalability metrics for project planning of a d...Mary Chan
Have you expanded your organization across multiple locations, or are you a client that utilizes external partners that provide outsourcing services? Both have their "cost savings" challenge where cost savings analysis is often a topic well scrutinized. However, in the grand scheme of your organization, is it a metric that really matters? See actual analytics on multiple game projects and why cost savings isn't as important a metric when making informed decisions about project planning for scalable and distributed development. It's all about the Metrics that Matter.
Five 'Ps' to operate a building for sustainability: create a program, measure and monitor performance, develop policies, improve practices, and upgrade properties. Green building operations and maintenance improves bottom line and is a critical part of reducing your companies carbon footprint.
Similar to Metis Project 2: Predicting Worldwide Gross - JungleBoogie (20)
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
2. Motivation
Create a linear regression model that can predict
Worldwide Gross of top-rated movies by
determining the features most influential to their
success.
3. Procedure
➔ Scraping
Data from various sources, clean and
merge into one dataframe
➔ EDA & OLS regression
On training set (70%), model evaluation
and regularization
➔ Choosing model
Apply new model to the rest of dataset
4. Procedure: Data Scraping
Dataframe: Top rated
100 movies*21 years
Features:
# Worldwide Gross
# Domestic Gross
# Foreign Gross # Budget
# Runtime # Composer
# Genre # Year
# IMDB Metascore
# Rating # Oscar Wins
# Winning Awards
5. Training Set Results: OLS
Feature Coef. P>|t|
R^2: 0.47
Adjusted R^2:
0.46
Budget ($M) 2.7038 0.000
Runtime (min) 0.6776 0.001
Year 3.6525 0.000
Rating 14.911 0.015
IMDB Metascore 1.2369 0.001
Top 15 Composers 40.921 0.000
Winning Awards 14.095 0.067
Oscar 55.205 0.000
6. Final Model: Degree 2 Polynomial Regression
Feature Coef.
R^2: 0.632Budget ($M) 1.426
Runtime (min) -5.506
Year -1887.55
Rating -67.562
IMDB Metascore 1.571
Top 15 Composers 22.64
Winning Awards 3.400
Oscar 25.744
9. Composers
Although this is not the most important
feature in my model:
➔ When making a movie
You should have a really high budget!!!
➔ Giving the job to one of the top
(10%) composers
You would have to pay roughly 5-22M
$
John Williams Danny Elfman
Alan Silvestri
Hans Zimmer
James Newton Howard
10. Conclusions
The budget is is the key indicator of
Worldwide Gross.
Runtime & Year of production are
negatively correlated with
Worldwide Gross.
Top composers indicator
contribution to the model is higher
than winning Oscar or other
Awards.