This document provides an overview of personal finance topics for engineers. It begins by explaining why personal finance is important but poorly covered, and why it is relevant for engineers specifically. It then outlines some fast finance basics like behavioral finance, liquidity, cash flow, compounding returns, and the benefits of index fund investing. The document also discusses more advanced topics such as calculating returns in Excel, retirement planning challenges, collectible coins, and derivatives. The overall message is that personal finance is not as rational as people think, and the keys are to save early, avoid debt, and keep investing simple through low-cost index funds.
Understand what Governance Is
We start with a definition of governance, its constituent parts, and their purpose
Identify Core Taxonomy Governance Processes
There are certain functions that any governance effort must perform . We show how these apply to taxonomy governance, and why
Identify Standard Processes and Tools
Business and supporting IT organizations already perform tasks that are in many ways similar to those needed for successful taxonomy governance. To minimize new investment in tools and training, it makes sense to use these where possible
Tricks of the Trade
We’ll show some of the detailed considerations that are important when setting up a taxonomy governance effort, and how we’ve handled them
Context
We’ll discuss how taxonomy governance fits in the broader operational context of an organization: specifically, how it connects with an IT organization and with business stakeholders
An introduction to Data Quality Services. DQS enables to discover, build, and manage knowledge about your data. Use that knowledge to perform data cleansing, matching and profiling. We will explore the numerous features and capabilities of Data Quality Services and its integration with SSIS with the DQS Cleansing Transform. Data Quality Services in SQL Server 2012
10 Atlassian Tool Hacks to Improve Team CultureAtlassian
Your team is already running along with JIRA, Confluence, or HipChat enabling you to deliver awesome projects. But can they help you with team culture? How can you use the tools your team already has in place to cultivate an innovative and open culture?
This talk will cover different ways to use JIRA and Confluence to hack your team culture. We will give you tips on how to use Atlassian to make large organizations feel like small teams, build and maintain an innovative culture, and inject some fun and humor into your team - making your workplace feel a bit more enjoyable!
Sherif Mansour, Principal Product Manager, Atlassian
Clark Glamour
ABSTRACT: "Data dredging"--searching non experimental data for causal and other relationships and taking that same data to be evidence for those relationships--was historically common in the natural sciences--the works of Kepler, Cannizzaro and Mendeleev are examples. Nowadays, "data dredging"--using data to bring hypotheses into consideration and regarding that same data as evidence bearing on their truth or falsity--is widely denounced by both philosophical and statistical methodologists. Notwithstanding, "data dredging" is routinely practiced in the human sciences using "traditional" methods--various forms of regression for example. The main thesis of my talk is that, in the spirit and letter of Mayo's and Spanos’ notion of severe testing, modern computational algorithms that search data for causal relations severely test their resulting models in the process of "constructing" them. My claim is that in many investigations, principled computerized search is invaluable for reliable, generalizable, informative, scientific inquiry. The possible failures of traditional search methods for causal relations, multiple regression for example, are easily demonstrated by simulation in cases where even the earliest consistent graphical model search algorithms succeed. ... These and other examples raise a number of issues about using multiple hypothesis tests in strategies for severe testing, notably, the interpretation of standard errors and confidence levels as error probabilities when the structures assumed in parameter estimation are uncertain. Commonly used regression methods, I will argue, are bad data dredging methods that do not severely, or appropriately, test their results. I argue that various traditional and proposed methodological norms, including pre-specification of experimental outcomes and error probabilities for regression estimates of causal effects, are unnecessary or illusory in application. Statistics wants a number, or at least an interval, to express a normative virtue, the value of data as evidence for a hypothesis, how well the data pushes us toward the true or away from the false. Good when you can get it, but there are many circumstances where you have evidence but there is no number or interval to express it other than phony numbers with no logical connection with truth guidance. Kepler, Darwin, Cannizarro, Mendeleev had no such numbers, but they severely tested their claims by combining data dredging with severe testing.
Understand what Governance Is
We start with a definition of governance, its constituent parts, and their purpose
Identify Core Taxonomy Governance Processes
There are certain functions that any governance effort must perform . We show how these apply to taxonomy governance, and why
Identify Standard Processes and Tools
Business and supporting IT organizations already perform tasks that are in many ways similar to those needed for successful taxonomy governance. To minimize new investment in tools and training, it makes sense to use these where possible
Tricks of the Trade
We’ll show some of the detailed considerations that are important when setting up a taxonomy governance effort, and how we’ve handled them
Context
We’ll discuss how taxonomy governance fits in the broader operational context of an organization: specifically, how it connects with an IT organization and with business stakeholders
An introduction to Data Quality Services. DQS enables to discover, build, and manage knowledge about your data. Use that knowledge to perform data cleansing, matching and profiling. We will explore the numerous features and capabilities of Data Quality Services and its integration with SSIS with the DQS Cleansing Transform. Data Quality Services in SQL Server 2012
10 Atlassian Tool Hacks to Improve Team CultureAtlassian
Your team is already running along with JIRA, Confluence, or HipChat enabling you to deliver awesome projects. But can they help you with team culture? How can you use the tools your team already has in place to cultivate an innovative and open culture?
This talk will cover different ways to use JIRA and Confluence to hack your team culture. We will give you tips on how to use Atlassian to make large organizations feel like small teams, build and maintain an innovative culture, and inject some fun and humor into your team - making your workplace feel a bit more enjoyable!
Sherif Mansour, Principal Product Manager, Atlassian
Clark Glamour
ABSTRACT: "Data dredging"--searching non experimental data for causal and other relationships and taking that same data to be evidence for those relationships--was historically common in the natural sciences--the works of Kepler, Cannizzaro and Mendeleev are examples. Nowadays, "data dredging"--using data to bring hypotheses into consideration and regarding that same data as evidence bearing on their truth or falsity--is widely denounced by both philosophical and statistical methodologists. Notwithstanding, "data dredging" is routinely practiced in the human sciences using "traditional" methods--various forms of regression for example. The main thesis of my talk is that, in the spirit and letter of Mayo's and Spanos’ notion of severe testing, modern computational algorithms that search data for causal relations severely test their resulting models in the process of "constructing" them. My claim is that in many investigations, principled computerized search is invaluable for reliable, generalizable, informative, scientific inquiry. The possible failures of traditional search methods for causal relations, multiple regression for example, are easily demonstrated by simulation in cases where even the earliest consistent graphical model search algorithms succeed. ... These and other examples raise a number of issues about using multiple hypothesis tests in strategies for severe testing, notably, the interpretation of standard errors and confidence levels as error probabilities when the structures assumed in parameter estimation are uncertain. Commonly used regression methods, I will argue, are bad data dredging methods that do not severely, or appropriately, test their results. I argue that various traditional and proposed methodological norms, including pre-specification of experimental outcomes and error probabilities for regression estimates of causal effects, are unnecessary or illusory in application. Statistics wants a number, or at least an interval, to express a normative virtue, the value of data as evidence for a hypothesis, how well the data pushes us toward the true or away from the false. Good when you can get it, but there are many circumstances where you have evidence but there is no number or interval to express it other than phony numbers with no logical connection with truth guidance. Kepler, Darwin, Cannizarro, Mendeleev had no such numbers, but they severely tested their claims by combining data dredging with severe testing.
Architecting Agile Data Applications for ScaleDatabricks
Data analytics and reporting platforms historically have been rigid, monolithic, hard to change and have limited ability to scale up or scale down. I can’t tell you how many times I have heard a business user ask for something as simple as an additional column in a report and IT says it will take 6 months to add that column because it doesn’t exist in the datawarehouse. As a former DBA, I can tell you the countless hours I have spent “tuning” SQL queries to hit pre-established SLAs. This talk will talk about how to architect modern data and analytics platforms in the cloud to support agility and scalability. We will include topics like end to end data pipeline flow, data mesh and data catalogs, live data and streaming, performing advanced analytics, applying agile software development practices like CI/CD and testability to data applications and finally taking advantage of the cloud for infinite scalability both up and down.
ChaoSlingr: Introducing Security based Chaos TestingAaron Rinehart
ChaoSlingr is a Security Chaos Engineering Tool focused primarily on the experimentation on AWS Infrastructure to bring system security weaknesses to the forefront.
The industry has traditionally put emphasis on the importance of preventative security control measures and defense-in-depth where-as our mission is to drive new knowledge and perspective into the attack surface by delivering proactively through detective experimentation. With so much focus on the preventative mechanisms we never attempt beyond one-time or annual pen testing requirements to actually validate whether or not those controls actually are performing as designed.
Our mission is to address security weaknesses proactively, going beyond the reactive processes that currently dominate traditional security models.
Get Mainframe Data to Snowflake’s Cloud Data WarehousePrecisely
Organizations are rapidly adopting the cloud data platform, Snowflake. Snowflake helps IT deliver insights to the business more quickly and at a lower cost than traditional data warehouses. In making that move, many companies find that they are missing highly-valued data from systems that are traditionally on-premises, such as the mainframe. Learn how the Syncsort Connect product family is helping IT save time and money getting mainframe data into Snowflake. View this webinar on-demand to:
• Understand common challenges with getting mainframe data into Snowflake and how to overcome them
• Where mainframe data can add value as a source for Snowflake
• A demo on how mainframe data can be integrated into Snowflake in 3-minutes or less using Syncsort Connect
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
Keeping the Pulse of Your Data: Why You Need Data Observability Precisely
With the explosive growth of DataOps to drive faster and better-informed business decisions, proactively understanding the health of your data is more important than ever. Data observability is one of the foundational capabilities of DataOps and an emerging discipline used to expose anomalies in data by continuously monitoring and testing data using artificial intelligence and machine learning to trigger alerts when issues are discovered.
Join Paul Rasmussen and Shalaish Koul from Precisely, to learn how data observability can be used as part of a DataOps strategy to prevent data issues from wreaking havoc on your analytics and ensure that your organization can confidently rely on the data used for advanced analytics and business intelligence.
Topics you will hear addressed in this webinar:
Data observability – what is it and how it is different from other monitoring solutions
Why now is the time to incorporate data observability into your DataOps strategy
How data observability helps prevent data issues from impacting downstream analytics
Examples of how data observability can be used to prevent real-world issues
Data-Ed Online: Approaching Data QualityDATAVERSITY
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy. This, in turn, allows for speedy identification of business problems, the delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues. Organizations must realize what it means to utilize Data Quality engineering in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor Data Quality. Showing how Data Quality should be engineered provides a useful framework in which to develop an effective approach. This, in turn, allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Learning Objectives:
Help you understand foundational Data Quality concepts based on the DAMA Guide to Data Management Book of Knowledge (DAMA DMBoK), as well as guiding principles, best practices, and steps for improving Data Quality at your organization
Demonstrate how chronic business challenges for organizations are often rooted in poor Data Quality
Share case studies illustrating the hallmarks and benefits of Data Quality success
DII - Mapping Organizational Readiness Assessments to an Implementation Frame...UCLA CTSI
November 1, 2017
Isomi Miake-Lye, PhD
VA Greater Los Angeles Healthcare System
“Unpacking Organizational Readiness for Change: Mapping Organizational Readiness Assessments to an Implementation Framework”
A presentation of the Southern California Regional Dissemination, Implementation and Improvement Science Webinar Series
Provided by the UCLA CTSI
Intuit Data Ecosystem supports unique consumer and small business assets at scale, and handle petabytes of customer data. We have 8M active small business customers and 16M paid workers that uses Intuit Quick Books and Quick Books Payroll Products. Huge customer base and large volumes of data always challenges the data teams in terms of freshness of data, correctness of data etc. This presentation is intended to cover such problems we faced at Intuit along with the data observability model we follow to cure, detect and prevent data Issues. We would like to provide deep insights into the implementations and the impact of some of the great work done by Intuit in this direction.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Best practices, lessons learned, and examples for taxonomy governance and iteration. Developed by Enterprise Knowledge and originally presented for the Knowledge Management Institute.
As the adoption of AI technologies increases and matures, the focus will shift from exploration to time to market, productivity and integration with existing workflows. Governing Enterprise data, scaling AI model development, selecting a complete, collaborative hybrid platform and tools for rapid solution deployments are key focus areas for growing data scientist teams tasked to respond to business challenges. This talk will cover the challenges and innovations for AI at scale for the Enterprise focusing on the modernization of data analytics, the AI ladder and AI life cycle and infrastructure architecture considerations. We will conclude by viewing the benefits and innovation of running your modern AI and Data Analytics applications such as SAS Viya and SAP HANA on IBM Power Systems and IBM Storage in hybrid cloud environments.
An illustrated guide to a week in the life of a woman in technology, what she goes through, and how it differs from being a guy in tech. Uses Tech Doodles.
Architecting Agile Data Applications for ScaleDatabricks
Data analytics and reporting platforms historically have been rigid, monolithic, hard to change and have limited ability to scale up or scale down. I can’t tell you how many times I have heard a business user ask for something as simple as an additional column in a report and IT says it will take 6 months to add that column because it doesn’t exist in the datawarehouse. As a former DBA, I can tell you the countless hours I have spent “tuning” SQL queries to hit pre-established SLAs. This talk will talk about how to architect modern data and analytics platforms in the cloud to support agility and scalability. We will include topics like end to end data pipeline flow, data mesh and data catalogs, live data and streaming, performing advanced analytics, applying agile software development practices like CI/CD and testability to data applications and finally taking advantage of the cloud for infinite scalability both up and down.
ChaoSlingr: Introducing Security based Chaos TestingAaron Rinehart
ChaoSlingr is a Security Chaos Engineering Tool focused primarily on the experimentation on AWS Infrastructure to bring system security weaknesses to the forefront.
The industry has traditionally put emphasis on the importance of preventative security control measures and defense-in-depth where-as our mission is to drive new knowledge and perspective into the attack surface by delivering proactively through detective experimentation. With so much focus on the preventative mechanisms we never attempt beyond one-time or annual pen testing requirements to actually validate whether or not those controls actually are performing as designed.
Our mission is to address security weaknesses proactively, going beyond the reactive processes that currently dominate traditional security models.
Get Mainframe Data to Snowflake’s Cloud Data WarehousePrecisely
Organizations are rapidly adopting the cloud data platform, Snowflake. Snowflake helps IT deliver insights to the business more quickly and at a lower cost than traditional data warehouses. In making that move, many companies find that they are missing highly-valued data from systems that are traditionally on-premises, such as the mainframe. Learn how the Syncsort Connect product family is helping IT save time and money getting mainframe data into Snowflake. View this webinar on-demand to:
• Understand common challenges with getting mainframe data into Snowflake and how to overcome them
• Where mainframe data can add value as a source for Snowflake
• A demo on how mainframe data can be integrated into Snowflake in 3-minutes or less using Syncsort Connect
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
Keeping the Pulse of Your Data: Why You Need Data Observability Precisely
With the explosive growth of DataOps to drive faster and better-informed business decisions, proactively understanding the health of your data is more important than ever. Data observability is one of the foundational capabilities of DataOps and an emerging discipline used to expose anomalies in data by continuously monitoring and testing data using artificial intelligence and machine learning to trigger alerts when issues are discovered.
Join Paul Rasmussen and Shalaish Koul from Precisely, to learn how data observability can be used as part of a DataOps strategy to prevent data issues from wreaking havoc on your analytics and ensure that your organization can confidently rely on the data used for advanced analytics and business intelligence.
Topics you will hear addressed in this webinar:
Data observability – what is it and how it is different from other monitoring solutions
Why now is the time to incorporate data observability into your DataOps strategy
How data observability helps prevent data issues from impacting downstream analytics
Examples of how data observability can be used to prevent real-world issues
Data-Ed Online: Approaching Data QualityDATAVERSITY
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy. This, in turn, allows for speedy identification of business problems, the delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues. Organizations must realize what it means to utilize Data Quality engineering in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor Data Quality. Showing how Data Quality should be engineered provides a useful framework in which to develop an effective approach. This, in turn, allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Learning Objectives:
Help you understand foundational Data Quality concepts based on the DAMA Guide to Data Management Book of Knowledge (DAMA DMBoK), as well as guiding principles, best practices, and steps for improving Data Quality at your organization
Demonstrate how chronic business challenges for organizations are often rooted in poor Data Quality
Share case studies illustrating the hallmarks and benefits of Data Quality success
DII - Mapping Organizational Readiness Assessments to an Implementation Frame...UCLA CTSI
November 1, 2017
Isomi Miake-Lye, PhD
VA Greater Los Angeles Healthcare System
“Unpacking Organizational Readiness for Change: Mapping Organizational Readiness Assessments to an Implementation Framework”
A presentation of the Southern California Regional Dissemination, Implementation and Improvement Science Webinar Series
Provided by the UCLA CTSI
Intuit Data Ecosystem supports unique consumer and small business assets at scale, and handle petabytes of customer data. We have 8M active small business customers and 16M paid workers that uses Intuit Quick Books and Quick Books Payroll Products. Huge customer base and large volumes of data always challenges the data teams in terms of freshness of data, correctness of data etc. This presentation is intended to cover such problems we faced at Intuit along with the data observability model we follow to cure, detect and prevent data Issues. We would like to provide deep insights into the implementations and the impact of some of the great work done by Intuit in this direction.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Best practices, lessons learned, and examples for taxonomy governance and iteration. Developed by Enterprise Knowledge and originally presented for the Knowledge Management Institute.
As the adoption of AI technologies increases and matures, the focus will shift from exploration to time to market, productivity and integration with existing workflows. Governing Enterprise data, scaling AI model development, selecting a complete, collaborative hybrid platform and tools for rapid solution deployments are key focus areas for growing data scientist teams tasked to respond to business challenges. This talk will cover the challenges and innovations for AI at scale for the Enterprise focusing on the modernization of data analytics, the AI ladder and AI life cycle and infrastructure architecture considerations. We will conclude by viewing the benefits and innovation of running your modern AI and Data Analytics applications such as SAS Viya and SAP HANA on IBM Power Systems and IBM Storage in hybrid cloud environments.
An illustrated guide to a week in the life of a woman in technology, what she goes through, and how it differs from being a guy in tech. Uses Tech Doodles.
Flawless Project Delivery is discipline that merges risk management, leading edge statistical analysis, advanced people management and leadership skills. These slides from a 30 minute lecture I deliver on the subject. Flawless improves outcomes in the billions. Not for the faint hearted in execution as it required courage and out the box leadership.
Epc project interdepency and Work Flow- promoignitetribes
Engineering, Procurement and Construction are highly correlated and set precedence against each other. They are very interdependent and these dependencies become increasingly critical as the phases are overlapped. In this module we share the interdependence of Engineering - Procurement and the influence in Construction. Here we touch a bit on work front monitoring and work face planning.
This presentation was created about a month ago in light of his resignation. However, recently, Steve Jobs has passed away and these quotes become evermore real of his brilliance and courage to change the world. Remember his legacy and the impact he left on all of us.
Rest in peace Steve Jobs. 1955-2011
Personal Finance for Engineers (AirBnB 2013)Adam Nash
This is the version of Personal Finance for Engineers given on November 19 at AirBnB headquarters in San Francisco. It is largely the same content as the version for Twitter.
Personal Finance for Engineers (Github 2014)Adam Nash
This is the version of Personal Finance for Engineers given at the Github HQ in San Francisco on Feb 20, 2014. This version is made extra awesome by the appearance of octocat.
Personal Finance for Everyone (Stripe 2014)Adam Nash
This is the version of my talk, Personal Finance for Engineers, that I gave on February 20, 2014 at Stripe HQ in San Francisco. It has minor modifications to broaden the language for the whole company.
Personal Finance for Engineering (Pinterest, 2014)Adam Nash
This is the version of Personal Finance for Engineers, covering behavioral finance, liquidity, savings, compounding, and long term investing. It was given at Pinterest HQ in San Francisco on Jan 23, 2014.
#193 - Pengapsykologi - Del 2 av 3 | Diskussion utifrån Morgan Housels bokJan Bolmeson
Idag fortsätter vi diskussionen från förra veckan om pengar, beteende och psykologi. Avsnittet bygger helt och hållet på Morgan Housels bok "The Psychology of Money" som släpptes på svenska i februari. Avsnittet är fristående så du behöver inte ha sett det första (#192) för att hänga med.
Hela artikeln finns på:
https://rikatillsammans.se/pengapsykologi-del2
Patreon-communityn: https://www.patreon.com/rikatillsammans
Nyhetsbrevet: https://rikatillsammans.se/nyhetsbrev
Digital workshop: https://rikatillsammans.se/plus/?a=workshop
Stanford CS 007-03 (2022): Personal Finance for Engineers / CompensationAdam Nash
These are the slides from the 3rd session of the Stanford University class, CS 007 "Personal Finance for Engineers" given on October 11, 2022. This seminar covers compensation, equity & comparing offers.
Stanford CS 007-02 (2022): Personal Finance for Engineers / Behavioral FinanceAdam Nash
These are the slides from the 2nd session of the Stanford University class, CS 007 "Personal Finance for Engineers," given on October 4, 2022. This seminar covers the topic of Behavioral Finance.
Stanford CS 007-01 (2022): Personal Finance for Engineers / IntroductionAdam Nash
These are the slides from the 1st session of the Stanford University class, CS 007 "Personal Finance for Engineers" given on September 27, 2022. This seminar covers a survey of the students enrolled in the course, with an overview of the topics to be covered over the course of the series.
Stanford CS 007-10 (2021): Personal Finance for Engineers / Additional Topics...Adam Nash
These are the slides from the 10th session of the Stanford University class, CS 007 "Personal Finance for Engineers" offered on December 7, 2021. This seminar covers student requested additional topics for the course, including bitcoin / cryptocurrency, derivatives, futures, options, private equity & venture capital.
Stanford CS 007-09 (2021): Personal Finance for Engineers / Real EstateAdam Nash
These are the slides from the 9th session of the Stanford University class, CS 007 "Personal Finance for Engineers" offered on November 30, 2021. This seminar covers real estate and related financial decisions: buying, renting, rent vs. buy, real estate investment, rental properties & tax advantages.
Stanford CS 007-08 (2021): Personal Finance for Engineers / Financial Plannin...Adam Nash
These are the slides from the 8th session of the Stanford University class, CS 007 "Personal Finance for Engineers" offered on November 16, 2021. This seminar covers financial planning, financial goals, couples & life insurance.
Stanford CS 007-07 (2021): Personal Finance for Engineers / InvestingAdam Nash
These are the slides from the 7th session of the Stanford University class, CS 007 "Personal Finance for Engineers" given on November 9, 2021. This seminar covers compounding, types of investments, diversification, how to invest, and the four keys to good investing (all boring).
Stanford CS 007-06 (2021): Personal Finance for Engineers / DebtAdam Nash
These are the slides from the 6th session of the Stanford University class, CS 007 "Personal Finance for Engineers" on October 26, 2021. This seminar focuses on compounding, mortgages, auto loans, student loans, credit cards and credit scores.
Stanford CS 007-05 (2021): Personal Finance for Engineers / Assets & Net WorthAdam Nash
These are the slides from the 5th session of the Stanford University class, CS 007 "Personal Finance for Engineers" taught on October 19, 2021. This seminar focuses on liquidity, emergency funds, assets & liabilities, and net worth.
Stanford CS 007-04 (2021): Personal Finance for Engineers / Savings & BudgetsAdam Nash
These are the slides from the 4th session of the Stanford University class, CS 007 "Personal Finance for Engineers" given on October 12, 2021. This seminar covers savings rates, income & expenses & budgeting.
Stanford CS 007-2 (2021): Personal Finance for Engineers / Behavioral FinanceAdam Nash
These are the slides from the 2nd session of the Stanford University class, CS 007 "Personal Finance for Engineers," given on September 28, 2021. This seminar covers the topic of Behavioral Finance.
Stanford CS 007-01 (2021): Personal Finance for Engineers / IntroductionAdam Nash
These are the slides from the 1st session of the Stanford University class, CS 007 "Personal Finance for Engineers" given on September 21, 2021. This seminar covers a survey of the students enrolled in the course, with an overview of the topics to be covered over the course of the series.
Stanford CS 007-10 (2020): Personal Finance for Engineers / Additional Topics...Adam Nash
These are the slides from the 10th session of the Stanford University class, CS 007 "Personal Finance for Engineers" offered in November 2020. This seminar covers student requested additional topics for the course, including bitcoin / cryptocurrency, derivatives, futures, options, private equity & venture capital.
Stanford CS 007-09 (2020): Personal Finance for Engineers / Real EstateAdam Nash
These are the slides from the 9th session of the Stanford University class, CS 007 "Personal Finance for Engineers" offered in November 2020. This seminar covers real estate and related financial decisions: buying, renting, rent vs. buy, real estate investment, rental properties & tax advantages.
Stanford CS 007-08 (2020): Personal Finance for Engineers / Financial Plannin...Adam Nash
These are the slides from the 8th session of the Stanford University class, CS 007 "Personal Finance for Engineers" offered on November 3, 2020. This seminar covers financial planning, financial goals, couples & life insurance.
Stanford CS 007-07 (2020): Personal Finance for Engineers / InvestingAdam Nash
These are the slides from the 7th session of the Stanford University class, CS 007 "Personal Finance for Engineers" given on October 27, 2020. This seminar covers compounding, types of investments, diversification, how to invest, and the four keys to good investing (all boring).
Stanford CS 007-06 (2020): Personal Finance for Engineers / DebtAdam Nash
These are the slides from the 6th session of the Stanford University class, CS 007 "Personal Finance for Engineers" This seminar focuses on compounding, mortgages, auto loans, student loans, credit cards and credit scores.
Stanford CS 007-05 (2020): Personal Finance for Engineers / Assets & Net WorthAdam Nash
These are the slides from the 5th session of the Stanford University class, CS 007 "Personal Finance for Engineers" This seminar focuses on liquidity, emergency funds, assets & liabilities, and net worth.
Stanford CS 007-2 (2020): Personal Finance for Engineers / Behavioral FinanceAdam Nash
These are the slides from the 2nd session of the Stanford University class, CS 007 "Personal Finance for Engineers," given on September 22, 2020. This seminar covers the topic of Behavioral Finance.
Stanford CS 007-01 (2020): Personal Finance for Engineers / IntroductionAdam Nash
These are the slides from the 1st session of the Stanford University class, CS 007 "Personal Finance for Engineers" given on September 15, 2020. This seminar covers a survey of the students enrolled in the course, with an overview of the topics to be covered over the course of the series.
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The European Unemployment Puzzle: implications from population agingGRAPE
We study the link between the evolving age structure of the working population and unemployment. We build a large new Keynesian OLG model with a realistic age structure, labor market frictions, sticky prices, and aggregate shocks. Once calibrated to the European economy, we quantify the extent to which demographic changes over the last three decades have contributed to the decline of the unemployment rate. Our findings yield important implications for the future evolution of unemployment given the anticipated further aging of the working population in Europe. We also quantify the implications for optimal monetary policy: lowering inflation volatility becomes less costly in terms of GDP and unemployment volatility, which hints that optimal monetary policy may be more hawkish in an aging society. Finally, our results also propose a partial reversal of the European-US unemployment puzzle due to the fact that the share of young workers is expected to remain robust in the US.
how to swap pi coins to foreign currency withdrawable.DOT TECH
As of my last update, Pi is still in the testing phase and is not tradable on any exchanges.
However, Pi Network has announced plans to launch its Testnet and Mainnet in the future, which may include listing Pi on exchanges.
The current method for selling pi coins involves exchanging them with a pi vendor who purchases pi coins for investment reasons.
If you want to sell your pi coins, reach out to a pi vendor and sell them to anyone looking to sell pi coins from any country around the globe.
Below is the contact information for my personal pi vendor.
Telegram: @Pi_vendor_247
Empowering the Unbanked: The Vital Role of NBFCs in Promoting Financial Inclu...Vighnesh Shashtri
In India, financial inclusion remains a critical challenge, with a significant portion of the population still unbanked. Non-Banking Financial Companies (NBFCs) have emerged as key players in bridging this gap by providing financial services to those often overlooked by traditional banking institutions. This article delves into how NBFCs are fostering financial inclusion and empowering the unbanked.
how to sell pi coins effectively (from 50 - 100k pi)DOT TECH
Anywhere in the world, including Africa, America, and Europe, you can sell Pi Network Coins online and receive cash through online payment options.
Pi has not yet been launched on any exchange because we are currently using the confined Mainnet. The planned launch date for Pi is June 28, 2026.
Reselling to investors who want to hold until the mainnet launch in 2026 is currently the sole way to sell.
Consequently, right now. All you need to do is select the right pi network provider.
Who is a pi merchant?
An individual who buys coins from miners on the pi network and resells them to investors hoping to hang onto them until the mainnet is launched is known as a pi merchant.
debuts.
I'll provide you the Telegram username
@Pi_vendor_247
What price will pi network be listed on exchangesDOT TECH
The rate at which pi will be listed is practically unknown. But due to speculations surrounding it the predicted rate is tends to be from 30$ — 50$.
So if you are interested in selling your pi network coins at a high rate tho. Or you can't wait till the mainnet launch in 2026. You can easily trade your pi coins with a merchant.
A merchant is someone who buys pi coins from miners and resell them to Investors looking forward to hold massive quantities till mainnet launch.
I will leave the telegram contact of my personal pi vendor to trade with.
@Pi_vendor_247
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Currently pi network is not tradable on binance or any other exchange because we are still in the enclosed mainnet.
Right now the only way to sell pi coins is by trading with a verified merchant.
What is a pi merchant?
A pi merchant is someone verified by pi network team and allowed to barter pi coins for goods and services.
Since pi network is not doing any pre-sale The only way exchanges like binance/huobi or crypto whales can get pi is by buying from miners. And a merchant stands in between the exchanges and the miners.
I will leave the telegram contact of my personal pi merchant. I and my friends has traded more than 6000pi coins successfully
Tele-gram
@Pi_vendor_247
when will pi network coin be available on crypto exchange.DOT TECH
There is no set date for when Pi coins will enter the market.
However, the developers are working hard to get them released as soon as possible.
Once they are available, users will be able to exchange other cryptocurrencies for Pi coins on designated exchanges.
But for now the only way to sell your pi coins is through verified pi vendor.
Here is the telegram contact of my personal pi vendor
@Pi_vendor_247
how to sell pi coins in South Korea profitably.DOT TECH
Yes. You can sell your pi network coins in South Korea or any other country, by finding a verified pi merchant
What is a verified pi merchant?
Since pi network is not launched yet on any exchange, the only way you can sell pi coins is by selling to a verified pi merchant, and this is because pi network is not launched yet on any exchange and no pre-sale or ico offerings Is done on pi.
Since there is no pre-sale, the only way exchanges can get pi is by buying from miners. So a pi merchant facilitates these transactions by acting as a bridge for both transactions.
How can i find a pi vendor/merchant?
Well for those who haven't traded with a pi merchant or who don't already have one. I will leave the telegram id of my personal pi merchant who i trade pi with.
Tele gram: @Pi_vendor_247
#pi #sell #nigeria #pinetwork #picoins #sellpi #Nigerian #tradepi #pinetworkcoins #sellmypi
what is the best method to sell pi coins in 2024DOT TECH
The best way to sell your pi coins safely is trading with an exchange..but since pi is not launched in any exchange, and second option is through a VERIFIED pi merchant.
Who is a pi merchant?
A pi merchant is someone who buys pi coins from miners and pioneers and resell them to Investors looking forward to hold massive amounts before mainnet launch in 2026.
I will leave the telegram contact of my personal pi merchant to trade pi coins with.
@Pi_vendor_247
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how to sell pi coins on Bitmart crypto exchangeDOT TECH
Yes. Pi network coins can be exchanged but not on bitmart exchange. Because pi network is still in the enclosed mainnet. The only way pioneers are able to trade pi coins is by reselling the pi coins to pi verified merchants.
A verified merchant is someone who buys pi network coins and resell it to exchanges looking forward to hold till mainnet launch.
I will leave the telegram contact of my personal pi merchant to trade with.
@Pi_vendor_247
2. Caveats & Preface
• I am not a financial planner
• This presentation is not financial advice
• You would be extremely foolish to make
investment decisions based on the
content of this presentation or
discussion
• The opinions in this deck are intended to
provoke discussion & further education
3. Why Personal Finance?
• Poorly covered in traditional
education, even top tier universities
• Not technically different, but
signal:noise ratio is terrible
• Massive impact on your life
(Money is one of the top 3 reasons for
marital problems)
4. Why “For Engineers”
• Understand / Prefer Math
• Tend to make higher incomes early in life,
thus face questions sooner.
• Tend to have complicated instruments,
like stock options, as part of their
compensation.
• Believe they are rational, which is actually
a problem when it comes to money
5. Fast Five Finance Basics
• Behavioral Finance Basics
• Liquidity is Undervalued
• Cash Flow Matters
• The Magic of Compounding
• Good Investing is Boring
6. “Advanced Settings”
• Calculating Returns in Excel
• Why Retirement Planning is Hard
• Why Do You Collect Coins?
• Understanding Derivatives
• Recommended Books
7. How Many of You Think You
Are Rational with Money?
(raise your hands)
8. You Are Not Rational
• Anchoring
• Mental Accounting
• Confirmation & Hindsight Bias
• Gambler’s Fallacy
• Herd Behavior
• Overconfidence
• Overreaction & Availability Bias
• Loss Aversion (aka Prospect Theory)
9. Anchoring
• People estimate answers to new /
novel problems with a bias towards
reference points
• Example: 1974 Study
• Most common examples:
• Price you bought a stock at
• High point for a stock
10. Mental Accounting
• Money is fungible, but people put it in
separate “mental accounts”
• Lost movie tickets example
• “Found Money” problem
• Vacation fund & credit card debt
11. Confirmation &
Hindsight Bias
• We selectively seek information that
support pre-existing theories, and
ignore / dispute information that
disproves them.
• We overestimate our ability to predict
the future based on the “obviousness”
of the past. (example: real estate)
12. Gambler’s Fallacy
• We see patterns in independent,
random chains of events
• We believe that based on series of
previous events, an outcome is more
likely than odds actually suggest
• Coin flip example
• It’s because with human behavior,
there are no “independent” events
13. Herd Behavior
• We have a tendency to mimic the
actions of the larger group
• Crowd psychology is a major
contributor to bubbles (believed)
• Easier to be “wrong with everyone”
than “right and alone”
• No one gets fired for buying IBM?
14. Overconfidence
• In one study, 74% of investment
managers believe they deliver above
average returns.
• Positively correlated with High IQ...
• Learn humility early
15. Overreaction &
Availability Bias
• Overreact to recent events
• Overweight recent trends
• Studies demonstrate that checking
stock prices daily leads to more
trading and worse results on average
• Worse in high tech, because we are
immersed in “game changers”
16. Loss Aversion
(aka Prospect Theory)
• You have $1,000 and you must pick one of the following choices:
• Choice A: You have a 50% chance of gaining $1,000, and a
50% chance of gaining $0.
Choice B: You have a 100% chance of gaining $500.
• You have $2,000 and you must pick one of the following choices:
• Choice A: You have a 50% chance of losing $1,000, and 50%
of losing $0.
• Choice B: You have a 100% chance of losing $500.
• We hate losses more than we love winning
• Average loss aversion is 3:1 (!)
• Affects views on wide range of situations, including taxes,
holding on to losing stocks, “sunk cost” mistakes
17. It’s OK to Not Be
Rational
• The key is that humans are
predictably irrational
• Know your own flaws, and you can set
up systems to account for them
• Self-awareness is key
(yes, my Mom is a psychologist...)
18. Liquidity
• Almost universally undervalued
• Strictly defined - it’s the
quantification of how much
money you can get, and how fast.
• Liquidity is the power to take
advantage of great investment
opportunities
• Liquidity is also, in the end, the
only thing that matters when you
need to pay for something.
19. Liquidity & Returns
• In almost all cases, liquidity is
inversely correlated with returns
• Examples:
• cash = very liquid
• private equity = very illiquid
• Common mistake:
Safety != Liquidity
20. Practical Outcome:
Emergency Funds
• Standard recommendation is that you
have 3-6 months of living expenses in
cash / cash-equivalents.
• That number increases if you are in
highly volatile industry / career.
• Worth considering length of time for
potential job search.
21. Cash Flow
• The ultimate secret to personal finance
is quite simple.
• Spend less than you make (on an
ongoing basis)
• Very easy to measure, but few people
do. Annual budget is a great idea.
• Don’t forget to model in annual
expenses & “personal spending”
22. Savings Targets
• What’s the right number? 3%? 6%?10%? 20%?
• There is no question - the more you save, the more
secure you are. Income comes & goes, but expenses /
lifestyle are sticky!
• A lot of models assume working 40 years, and
producing savings to generate 80% of working income.
• These models don’t actually match anyone’s real world
experience.
• There are a lot of models out there, and rules of thumb,
but it’s important to run the numbers yourself.
23. The Magic of
Compounding
• Not convinced that Albert Einstein
said it was the greatest force in the
universe.
• It’s the key to almost all long term
financial planning.
• Exponentials are bad in algorithmic
cost, good in savings returns.
24. Simple Model
• Rule of 72
• In Excel, for each year, just use
=POWER(1+rate, year)
• 4% over 20 years is 2.19x
• 8% over 20 years is 4.66x
• Careful: it works on debt just as well
as savings... in reverse!
25. The Benefits of
An Early Start
• Compounding really takes off over
long time periods
Years Return at 8% In most retirement
10 2.16x planning models,
money saved
20 4.66x between ages 25 - 35
30 10.06x produces more
money than all
40 21.72x savings between
50 46.9x 35 - 65!
26. The Dangers of Debt
• Bankruptcy is literally when you can’t pay
your debts. You can’t go bankrupt if you
don’t have debt.
• You will never find an investment that pays
8% guaranteed, let alone 20%+
• You will find *tons* of credit offers out there
that will charge you that.
• “Bad” debt is toxic, your best return is to pay
it off. But emergency fund takes precedence.
27. Good Investing is Boring
• No one wants to be average, but with
investing, average is actually well
above average.
• You will beat most mutual funds, and
a large majority of your peers with
simple, low-cost index funds.
• Asset allocation explains ~90% of the
variance between fund performance
28. Basic Asset Allocation
• Different types of assets (cash, bonds,
stocks, etc) have different volatility &
return characteristics
• Combinations can lower volatility
significantly, with moderate impact to
returns
• Complication: historical performance
does not predict future performance
29. Simple Operating Model
• 2 hours of work per year.
• Pick an asset allocation that is appropriate for
your emotional character & time frame & goals.
• For each asset class, pick cheap index fund to
represent.
• Rebalance every 1-2 years.
• http://blog.adamnash.com/2010/12/31/
personal-finance-how-to-rebalance-your-
portfolio/
30. Calculating Returns in
Excel
• You can model as a cash flow in Excel
• Two columns: Dates & Amounts
• Additions are negative, Withdrawals
are positive. (yes, that’s right)
• XIRR function is magic, but solving
non-linear equations requires a hint
32. Why Retirement
Planning is Hard
• Saving is hard enough
• Reliably modeling future returns is
extremely difficult (simple, monte
carlo, etc)
• Converting lump sum into annual
income is borderline impossible
• No do overs
33. Why Do You Collect
Coins?
• Obvious answer: I am a nerd
• Less obvious answer:
• Collectible gold/silver coins are a unique asset class
• Precious metals provide a backstop in value, but over
long term, coins trade like collectibles, indexed to the
incomes of higher income brackets
• Rewards long-term contrarian thinking (buy when
unpopular)
• Game mechanics are reliable / predictable, if you
understand collection games (collect them all, rarity /
desirability, subscriptions)
• Most likely correct answer: I am a nerd
34. Understanding
Derivatives
• Derivative is a financial instrument that is
based on another financial instrument.
• Date back to medieval Japan & rice futures.
Critical to managing risk.
• Most common types are calls & puts
• Call = right to buy a stock at a certain price
over a given time period.
• Put = right to sell a stock at a certain price
over a given time period.
52. Recommended Books
• WSJ Guide to Understanding Money & Investing
• The Millionaire Next Door
• A Random Walk Down Wall Street
• The Essays of Warren Buffett
• Common Stocks & Uncommon Profits
• The Intelligent Investor
• Devil Take the Hindmost
• When Genius Failed
• Against the Gods: The Remarkable Story of Risk
• http://blog.adamnash.com/2007/02/14/personal-finance-education-
series-2-recommended-books/