Bài giảng được thiết để cung cấp cho người học phương pháp và kỹ năng cần thiết của quá trình điều tra khảo sát. Thêm vào đó bài giảng cũng giúp người học một số phương pháp chọn mẫu cơ bản để có thể ứng dụng vào các cuộc khảo sát thông thường.
Bài giảng 4: Kích thước mẫu
Việc xác định cỡ mẫu luôn là bài toán khó cho các nhà nghiên cứu làm sao cho phù hợp với từng dự án cụ thể trong thực tế.Phần này người học sẽ nắm được công thức để xác định cỡ mẫu sao cho mẫu đại diện và có được đầy đủ đặc điểm của tổng thể
Xác định kích thước mẫu
Điều chỉnh-Trọng lượng
Các ví dụ
Tài liệu tham khảo:
Các tài liệu sau đây sẽ giúp người học hiểu, tiếp thu và bổ trợ được nhiều hơn nữa những nội dung của khóa học trên :
Sức mạnh của thiết kế điều tra, IAROSSI, WorldBank 2006
Sổ tay nghiên cứu điều tra, PETER H. ROSSI, D. ... Wright, 2003
Để biết thêm chi tiết về các hoạt động và nghiên cứu của DEPOCEN truy cập:
Website: http://depocen.org/vn/
LinkedIn: http://linkd.in/1GnHrHB
Facebook: DEPOCEN
Bài giảng được thiết để cung cấp cho người học phương pháp và kỹ năng cần thiết của quá trình điều tra khảo sát. Thêm vào đó bài giảng cũng giúp người học một số phương pháp chọn mẫu cơ bản để có thể ứng dụng vào các cuộc khảo sát thông thường.
Bài giảng 4: Kích thước mẫu
Việc xác định cỡ mẫu luôn là bài toán khó cho các nhà nghiên cứu làm sao cho phù hợp với từng dự án cụ thể trong thực tế.Phần này người học sẽ nắm được công thức để xác định cỡ mẫu sao cho mẫu đại diện và có được đầy đủ đặc điểm của tổng thể
Xác định kích thước mẫu
Điều chỉnh-Trọng lượng
Các ví dụ
Tài liệu tham khảo:
Các tài liệu sau đây sẽ giúp người học hiểu, tiếp thu và bổ trợ được nhiều hơn nữa những nội dung của khóa học trên :
Sức mạnh của thiết kế điều tra, IAROSSI, WorldBank 2006
Sổ tay nghiên cứu điều tra, PETER H. ROSSI, D. ... Wright, 2003
Để biết thêm chi tiết về các hoạt động và nghiên cứu của DEPOCEN truy cập:
Website: http://depocen.org/vn/
LinkedIn: http://linkd.in/1GnHrHB
Facebook: DEPOCEN
2024 State of Marketing Report – by HubspotMarius Sescu
https://www.hubspot.com/state-of-marketing
· Scaling relationships and proving ROI
· Social media is the place for search, sales, and service
· Authentic influencer partnerships fuel brand growth
· The strongest connections happen via call, click, chat, and camera.
· Time saved with AI leads to more creative work
· Seeking: A single source of truth
· TLDR; Get on social, try AI, and align your systems.
· More human marketing, powered by robots
ChatGPT is a revolutionary addition to the world since its introduction in 2022. A big shift in the sector of information gathering and processing happened because of this chatbot. What is the story of ChatGPT? How is the bot responding to prompts and generating contents? Swipe through these slides prepared by Expeed Software, a web development company regarding the development and technical intricacies of ChatGPT!
Product Design Trends in 2024 | Teenage EngineeringsPixeldarts
The realm of product design is a constantly changing environment where technology and style intersect. Every year introduces fresh challenges and exciting trends that mold the future of this captivating art form. In this piece, we delve into the significant trends set to influence the look and functionality of product design in the year 2024.
How Race, Age and Gender Shape Attitudes Towards Mental HealthThinkNow
Mental health has been in the news quite a bit lately. Dozens of U.S. states are currently suing Meta for contributing to the youth mental health crisis by inserting addictive features into their products, while the U.S. Surgeon General is touring the nation to bring awareness to the growing epidemic of loneliness and isolation. The country has endured periods of low national morale, such as in the 1970s when high inflation and the energy crisis worsened public sentiment following the Vietnam War. The current mood, however, feels different. Gallup recently reported that national mental health is at an all-time low, with few bright spots to lift spirits.
To better understand how Americans are feeling and their attitudes towards mental health in general, ThinkNow conducted a nationally representative quantitative survey of 1,500 respondents and found some interesting differences among ethnic, age and gender groups.
Technology
For example, 52% agree that technology and social media have a negative impact on mental health, but when broken out by race, 61% of Whites felt technology had a negative effect, and only 48% of Hispanics thought it did.
While technology has helped us keep in touch with friends and family in faraway places, it appears to have degraded our ability to connect in person. Staying connected online is a double-edged sword since the same news feed that brings us pictures of the grandkids and fluffy kittens also feeds us news about the wars in Israel and Ukraine, the dysfunction in Washington, the latest mass shooting and the climate crisis.
Hispanics may have a built-in defense against the isolation technology breeds, owing to their large, multigenerational households, strong social support systems, and tendency to use social media to stay connected with relatives abroad.
Age and Gender
When asked how individuals rate their mental health, men rate it higher than women by 11 percentage points, and Baby Boomers rank it highest at 83%, saying it’s good or excellent vs. 57% of Gen Z saying the same.
Gen Z spends the most amount of time on social media, so the notion that social media negatively affects mental health appears to be correlated. Unfortunately, Gen Z is also the generation that’s least comfortable discussing mental health concerns with healthcare professionals. Only 40% of them state they’re comfortable discussing their issues with a professional compared to 60% of Millennials and 65% of Boomers.
Race Affects Attitudes
As seen in previous research conducted by ThinkNow, Asian Americans lag other groups when it comes to awareness of mental health issues. Twenty-four percent of Asian Americans believe that having a mental health issue is a sign of weakness compared to the 16% average for all groups. Asians are also considerably less likely to be aware of mental health services in their communities (42% vs. 55%) and most likely to seek out information on social media (51% vs. 35%).
AI Trends in Creative Operations 2024 by Artwork Flow.pdfmarketingartwork
Creative operations teams expect increased AI use in 2024. Currently, over half of tasks are not AI-enabled, but this is expected to decrease in the coming year. ChatGPT is the most popular AI tool currently. Business leaders are more actively exploring AI benefits than individual contributors. Most respondents do not believe AI will impact workforce size in 2024. However, some inhibitions still exist around AI accuracy and lack of understanding. Creatives primarily want to use AI to save time on mundane tasks and boost productivity.
Organizational culture includes values, norms, systems, symbols, language, assumptions, beliefs, and habits that influence employee behaviors and how people interpret those behaviors. It is important because culture can help or hinder a company's success. Some key aspects of Netflix's culture that help it achieve results include hiring smartly so every position has stars, focusing on attitude over just aptitude, and having a strict policy against peacocks, whiners, and jerks.
PEPSICO Presentation to CAGNY Conference Feb 2024Neil Kimberley
PepsiCo provided a safe harbor statement noting that any forward-looking statements are based on currently available information and are subject to risks and uncertainties. It also provided information on non-GAAP measures and directing readers to its website for disclosure and reconciliation. The document then discussed PepsiCo's business overview, including that it is a global beverage and convenient food company with iconic brands, $91 billion in net revenue in 2023, and nearly $14 billion in core operating profit. It operates through a divisional structure with a focus on local consumers.
Content Methodology: A Best Practices Report (Webinar)contently
This document provides an overview of content methodology best practices. It defines content methodology as establishing objectives, KPIs, and a culture of continuous learning and iteration. An effective methodology focuses on connecting with audiences, creating optimal content, and optimizing processes. It also discusses why a methodology is needed due to the competitive landscape, proliferation of channels, and opportunities for improvement. Components of an effective methodology include defining objectives and KPIs, audience analysis, identifying opportunities, and evaluating resources. The document concludes with recommendations around creating a content plan, testing and optimizing content over 90 days.
How to Prepare For a Successful Job Search for 2024Albert Qian
The document provides guidance on preparing a job search for 2024. It discusses the state of the job market, focusing on growth in AI and healthcare but also continued layoffs. It recommends figuring out what you want to do by researching interests and skills, then conducting informational interviews. The job search should involve building a personal brand on LinkedIn, actively applying to jobs, tailoring resumes and interviews, maintaining job hunting as a habit, and continuing self-improvement. Once hired, the document advises setting new goals and keeping skills and networking active in case of future opportunities.
A report by thenetworkone and Kurio.
The contributing experts and agencies are (in an alphabetical order): Sylwia Rytel, Social Media Supervisor, 180heartbeats + JUNG v MATT (PL), Sharlene Jenner, Vice President - Director of Engagement Strategy, Abelson Taylor (USA), Alex Casanovas, Digital Director, Atrevia (ES), Dora Beilin, Senior Social Strategist, Barrett Hoffher (USA), Min Seo, Campaign Director, Brand New Agency (KR), Deshé M. Gully, Associate Strategist, Day One Agency (USA), Francesca Trevisan, Strategist, Different (IT), Trevor Crossman, CX and Digital Transformation Director; Olivia Hussey, Strategic Planner; Simi Srinarula, Social Media Manager, The Hallway (AUS), James Hebbert, Managing Director, Hylink (CN / UK), Mundy Álvarez, Planning Director; Pedro Rojas, Social Media Manager; Pancho González, CCO, Inbrax (CH), Oana Oprea, Head of Digital Planning, Jam Session Agency (RO), Amy Bottrill, Social Account Director, Launch (UK), Gaby Arriaga, Founder, Leonardo1452 (MX), Shantesh S Row, Creative Director, Liwa (UAE), Rajesh Mehta, Chief Strategy Officer; Dhruv Gaur, Digital Planning Lead; Leonie Mergulhao, Account Supervisor - Social Media & PR, Medulla (IN), Aurelija Plioplytė, Head of Digital & Social, Not Perfect (LI), Daiana Khaidargaliyeva, Account Manager, Osaka Labs (UK / USA), Stefanie Söhnchen, Vice President Digital, PIABO Communications (DE), Elisabeth Winiartati, Managing Consultant, Head of Global Integrated Communications; Lydia Aprina, Account Manager, Integrated Marketing and Communications; Nita Prabowo, Account Manager, Integrated Marketing and Communications; Okhi, Web Developer, PNTR Group (ID), Kei Obusan, Insights Director; Daffi Ranandi, Insights Manager, Radarr (SG), Gautam Reghunath, Co-founder & CEO, Talented (IN), Donagh Humphreys, Head of Social and Digital Innovation, THINKHOUSE (IRE), Sarah Yim, Strategy Director, Zulu Alpha Kilo (CA).
Trends In Paid Search: Navigating The Digital Landscape In 2024Search Engine Journal
The search marketing landscape is evolving rapidly with new technologies, and professionals, like you, rely on innovative paid search strategies to meet changing demands.
It’s important that you’re ready to implement new strategies in 2024.
Check this out and learn the top trends in paid search advertising that are expected to gain traction, so you can drive higher ROI more efficiently in 2024.
You’ll learn:
- The latest trends in AI and automation, and what this means for an evolving paid search ecosystem.
- New developments in privacy and data regulation.
- Emerging ad formats that are expected to make an impact next year.
Watch Sreekant Lanka from iQuanti and Irina Klein from OneMain Financial as they dive into the future of paid search and explore the trends, strategies, and technologies that will shape the search marketing landscape.
If you’re looking to assess your paid search strategy and design an industry-aligned plan for 2024, then this webinar is for you.
5 Public speaking tips from TED - Visualized summarySpeakerHub
From their humble beginnings in 1984, TED has grown into the world’s most powerful amplifier for speakers and thought-leaders to share their ideas. They have over 2,400 filmed talks (not including the 30,000+ TEDx videos) freely available online, and have hosted over 17,500 events around the world.
With over one billion views in a year, it’s no wonder that so many speakers are looking to TED for ideas on how to share their message more effectively.
The article “5 Public-Speaking Tips TED Gives Its Speakers”, by Carmine Gallo for Forbes, gives speakers five practical ways to connect with their audience, and effectively share their ideas on stage.
Whether you are gearing up to get on a TED stage yourself, or just want to master the skills that so many of their speakers possess, these tips and quotes from Chris Anderson, the TED Talks Curator, will encourage you to make the most impactful impression on your audience.
See the full article and more summaries like this on SpeakerHub here: https://speakerhub.com/blog/5-presentation-tips-ted-gives-its-speakers
See the original article on Forbes here:
http://www.forbes.com/forbes/welcome/?toURL=http://www.forbes.com/sites/carminegallo/2016/05/06/5-public-speaking-tips-ted-gives-its-speakers/&refURL=&referrer=#5c07a8221d9b
ChatGPT and the Future of Work - Clark Boyd Clark Boyd
Everyone is in agreement that ChatGPT (and other generative AI tools) will shape the future of work. Yet there is little consensus on exactly how, when, and to what extent this technology will change our world.
Businesses that extract maximum value from ChatGPT will use it as a collaborative tool for everything from brainstorming to technical maintenance.
For individuals, now is the time to pinpoint the skills the future professional will need to thrive in the AI age.
Check out this presentation to understand what ChatGPT is, how it will shape the future of work, and how you can prepare to take advantage.
The document provides career advice for getting into the tech field, including:
- Doing projects and internships in college to build a portfolio.
- Learning about different roles and technologies through industry research.
- Contributing to open source projects to build experience and network.
- Developing a personal brand through a website and social media presence.
- Networking through events, communities, and finding a mentor.
- Practicing interviews through mock interviews and whiteboarding coding questions.
Google's Just Not That Into You: Understanding Core Updates & Search IntentLily Ray
1. Core updates from Google periodically change how its algorithms assess and rank websites and pages. This can impact rankings through shifts in user intent, site quality issues being caught up to, world events influencing queries, and overhauls to search like the E-A-T framework.
2. There are many possible user intents beyond just transactional, navigational and informational. Identifying intent shifts is important during core updates. Sites may need to optimize for new intents through different content types and sections.
3. Responding effectively to core updates requires analyzing "before and after" data to understand changes, identifying new intents or page types, and ensuring content matches appropriate intents across video, images, knowledge graphs and more.
A brief introduction to DataScience with explaining of the concepts, algorithms, machine learning, supervised and unsupervised learning, clustering, statistics, data preprocessing, real-world applications etc.
It's part of a Data Science Corner Campaign where I will be discussing the fundamentals of DataScience, AIML, Statistics etc.
Time Management & Productivity - Best PracticesVit Horky
Here's my presentation on by proven best practices how to manage your work time effectively and how to improve your productivity. It includes practical tips and how to use tools such as Slack, Google Apps, Hubspot, Google Calendar, Gmail and others.
The six step guide to practical project managementMindGenius
The six step guide to practical project management
If you think managing projects is too difficult, think again.
We’ve stripped back project management processes to the
basics – to make it quicker and easier, without sacrificing
the vital ingredients for success.
“If you’re looking for some real-world guidance, then The Six Step Guide to Practical Project Management will help.”
Dr Andrew Makar, Tactical Project Management
2024 State of Marketing Report – by HubspotMarius Sescu
https://www.hubspot.com/state-of-marketing
· Scaling relationships and proving ROI
· Social media is the place for search, sales, and service
· Authentic influencer partnerships fuel brand growth
· The strongest connections happen via call, click, chat, and camera.
· Time saved with AI leads to more creative work
· Seeking: A single source of truth
· TLDR; Get on social, try AI, and align your systems.
· More human marketing, powered by robots
ChatGPT is a revolutionary addition to the world since its introduction in 2022. A big shift in the sector of information gathering and processing happened because of this chatbot. What is the story of ChatGPT? How is the bot responding to prompts and generating contents? Swipe through these slides prepared by Expeed Software, a web development company regarding the development and technical intricacies of ChatGPT!
Product Design Trends in 2024 | Teenage EngineeringsPixeldarts
The realm of product design is a constantly changing environment where technology and style intersect. Every year introduces fresh challenges and exciting trends that mold the future of this captivating art form. In this piece, we delve into the significant trends set to influence the look and functionality of product design in the year 2024.
How Race, Age and Gender Shape Attitudes Towards Mental HealthThinkNow
Mental health has been in the news quite a bit lately. Dozens of U.S. states are currently suing Meta for contributing to the youth mental health crisis by inserting addictive features into their products, while the U.S. Surgeon General is touring the nation to bring awareness to the growing epidemic of loneliness and isolation. The country has endured periods of low national morale, such as in the 1970s when high inflation and the energy crisis worsened public sentiment following the Vietnam War. The current mood, however, feels different. Gallup recently reported that national mental health is at an all-time low, with few bright spots to lift spirits.
To better understand how Americans are feeling and their attitudes towards mental health in general, ThinkNow conducted a nationally representative quantitative survey of 1,500 respondents and found some interesting differences among ethnic, age and gender groups.
Technology
For example, 52% agree that technology and social media have a negative impact on mental health, but when broken out by race, 61% of Whites felt technology had a negative effect, and only 48% of Hispanics thought it did.
While technology has helped us keep in touch with friends and family in faraway places, it appears to have degraded our ability to connect in person. Staying connected online is a double-edged sword since the same news feed that brings us pictures of the grandkids and fluffy kittens also feeds us news about the wars in Israel and Ukraine, the dysfunction in Washington, the latest mass shooting and the climate crisis.
Hispanics may have a built-in defense against the isolation technology breeds, owing to their large, multigenerational households, strong social support systems, and tendency to use social media to stay connected with relatives abroad.
Age and Gender
When asked how individuals rate their mental health, men rate it higher than women by 11 percentage points, and Baby Boomers rank it highest at 83%, saying it’s good or excellent vs. 57% of Gen Z saying the same.
Gen Z spends the most amount of time on social media, so the notion that social media negatively affects mental health appears to be correlated. Unfortunately, Gen Z is also the generation that’s least comfortable discussing mental health concerns with healthcare professionals. Only 40% of them state they’re comfortable discussing their issues with a professional compared to 60% of Millennials and 65% of Boomers.
Race Affects Attitudes
As seen in previous research conducted by ThinkNow, Asian Americans lag other groups when it comes to awareness of mental health issues. Twenty-four percent of Asian Americans believe that having a mental health issue is a sign of weakness compared to the 16% average for all groups. Asians are also considerably less likely to be aware of mental health services in their communities (42% vs. 55%) and most likely to seek out information on social media (51% vs. 35%).
AI Trends in Creative Operations 2024 by Artwork Flow.pdfmarketingartwork
Creative operations teams expect increased AI use in 2024. Currently, over half of tasks are not AI-enabled, but this is expected to decrease in the coming year. ChatGPT is the most popular AI tool currently. Business leaders are more actively exploring AI benefits than individual contributors. Most respondents do not believe AI will impact workforce size in 2024. However, some inhibitions still exist around AI accuracy and lack of understanding. Creatives primarily want to use AI to save time on mundane tasks and boost productivity.
Organizational culture includes values, norms, systems, symbols, language, assumptions, beliefs, and habits that influence employee behaviors and how people interpret those behaviors. It is important because culture can help or hinder a company's success. Some key aspects of Netflix's culture that help it achieve results include hiring smartly so every position has stars, focusing on attitude over just aptitude, and having a strict policy against peacocks, whiners, and jerks.
PEPSICO Presentation to CAGNY Conference Feb 2024Neil Kimberley
PepsiCo provided a safe harbor statement noting that any forward-looking statements are based on currently available information and are subject to risks and uncertainties. It also provided information on non-GAAP measures and directing readers to its website for disclosure and reconciliation. The document then discussed PepsiCo's business overview, including that it is a global beverage and convenient food company with iconic brands, $91 billion in net revenue in 2023, and nearly $14 billion in core operating profit. It operates through a divisional structure with a focus on local consumers.
Content Methodology: A Best Practices Report (Webinar)contently
This document provides an overview of content methodology best practices. It defines content methodology as establishing objectives, KPIs, and a culture of continuous learning and iteration. An effective methodology focuses on connecting with audiences, creating optimal content, and optimizing processes. It also discusses why a methodology is needed due to the competitive landscape, proliferation of channels, and opportunities for improvement. Components of an effective methodology include defining objectives and KPIs, audience analysis, identifying opportunities, and evaluating resources. The document concludes with recommendations around creating a content plan, testing and optimizing content over 90 days.
How to Prepare For a Successful Job Search for 2024Albert Qian
The document provides guidance on preparing a job search for 2024. It discusses the state of the job market, focusing on growth in AI and healthcare but also continued layoffs. It recommends figuring out what you want to do by researching interests and skills, then conducting informational interviews. The job search should involve building a personal brand on LinkedIn, actively applying to jobs, tailoring resumes and interviews, maintaining job hunting as a habit, and continuing self-improvement. Once hired, the document advises setting new goals and keeping skills and networking active in case of future opportunities.
A report by thenetworkone and Kurio.
The contributing experts and agencies are (in an alphabetical order): Sylwia Rytel, Social Media Supervisor, 180heartbeats + JUNG v MATT (PL), Sharlene Jenner, Vice President - Director of Engagement Strategy, Abelson Taylor (USA), Alex Casanovas, Digital Director, Atrevia (ES), Dora Beilin, Senior Social Strategist, Barrett Hoffher (USA), Min Seo, Campaign Director, Brand New Agency (KR), Deshé M. Gully, Associate Strategist, Day One Agency (USA), Francesca Trevisan, Strategist, Different (IT), Trevor Crossman, CX and Digital Transformation Director; Olivia Hussey, Strategic Planner; Simi Srinarula, Social Media Manager, The Hallway (AUS), James Hebbert, Managing Director, Hylink (CN / UK), Mundy Álvarez, Planning Director; Pedro Rojas, Social Media Manager; Pancho González, CCO, Inbrax (CH), Oana Oprea, Head of Digital Planning, Jam Session Agency (RO), Amy Bottrill, Social Account Director, Launch (UK), Gaby Arriaga, Founder, Leonardo1452 (MX), Shantesh S Row, Creative Director, Liwa (UAE), Rajesh Mehta, Chief Strategy Officer; Dhruv Gaur, Digital Planning Lead; Leonie Mergulhao, Account Supervisor - Social Media & PR, Medulla (IN), Aurelija Plioplytė, Head of Digital & Social, Not Perfect (LI), Daiana Khaidargaliyeva, Account Manager, Osaka Labs (UK / USA), Stefanie Söhnchen, Vice President Digital, PIABO Communications (DE), Elisabeth Winiartati, Managing Consultant, Head of Global Integrated Communications; Lydia Aprina, Account Manager, Integrated Marketing and Communications; Nita Prabowo, Account Manager, Integrated Marketing and Communications; Okhi, Web Developer, PNTR Group (ID), Kei Obusan, Insights Director; Daffi Ranandi, Insights Manager, Radarr (SG), Gautam Reghunath, Co-founder & CEO, Talented (IN), Donagh Humphreys, Head of Social and Digital Innovation, THINKHOUSE (IRE), Sarah Yim, Strategy Director, Zulu Alpha Kilo (CA).
Trends In Paid Search: Navigating The Digital Landscape In 2024Search Engine Journal
The search marketing landscape is evolving rapidly with new technologies, and professionals, like you, rely on innovative paid search strategies to meet changing demands.
It’s important that you’re ready to implement new strategies in 2024.
Check this out and learn the top trends in paid search advertising that are expected to gain traction, so you can drive higher ROI more efficiently in 2024.
You’ll learn:
- The latest trends in AI and automation, and what this means for an evolving paid search ecosystem.
- New developments in privacy and data regulation.
- Emerging ad formats that are expected to make an impact next year.
Watch Sreekant Lanka from iQuanti and Irina Klein from OneMain Financial as they dive into the future of paid search and explore the trends, strategies, and technologies that will shape the search marketing landscape.
If you’re looking to assess your paid search strategy and design an industry-aligned plan for 2024, then this webinar is for you.
5 Public speaking tips from TED - Visualized summarySpeakerHub
From their humble beginnings in 1984, TED has grown into the world’s most powerful amplifier for speakers and thought-leaders to share their ideas. They have over 2,400 filmed talks (not including the 30,000+ TEDx videos) freely available online, and have hosted over 17,500 events around the world.
With over one billion views in a year, it’s no wonder that so many speakers are looking to TED for ideas on how to share their message more effectively.
The article “5 Public-Speaking Tips TED Gives Its Speakers”, by Carmine Gallo for Forbes, gives speakers five practical ways to connect with their audience, and effectively share their ideas on stage.
Whether you are gearing up to get on a TED stage yourself, or just want to master the skills that so many of their speakers possess, these tips and quotes from Chris Anderson, the TED Talks Curator, will encourage you to make the most impactful impression on your audience.
See the full article and more summaries like this on SpeakerHub here: https://speakerhub.com/blog/5-presentation-tips-ted-gives-its-speakers
See the original article on Forbes here:
http://www.forbes.com/forbes/welcome/?toURL=http://www.forbes.com/sites/carminegallo/2016/05/06/5-public-speaking-tips-ted-gives-its-speakers/&refURL=&referrer=#5c07a8221d9b
ChatGPT and the Future of Work - Clark Boyd Clark Boyd
Everyone is in agreement that ChatGPT (and other generative AI tools) will shape the future of work. Yet there is little consensus on exactly how, when, and to what extent this technology will change our world.
Businesses that extract maximum value from ChatGPT will use it as a collaborative tool for everything from brainstorming to technical maintenance.
For individuals, now is the time to pinpoint the skills the future professional will need to thrive in the AI age.
Check out this presentation to understand what ChatGPT is, how it will shape the future of work, and how you can prepare to take advantage.
The document provides career advice for getting into the tech field, including:
- Doing projects and internships in college to build a portfolio.
- Learning about different roles and technologies through industry research.
- Contributing to open source projects to build experience and network.
- Developing a personal brand through a website and social media presence.
- Networking through events, communities, and finding a mentor.
- Practicing interviews through mock interviews and whiteboarding coding questions.
Google's Just Not That Into You: Understanding Core Updates & Search IntentLily Ray
1. Core updates from Google periodically change how its algorithms assess and rank websites and pages. This can impact rankings through shifts in user intent, site quality issues being caught up to, world events influencing queries, and overhauls to search like the E-A-T framework.
2. There are many possible user intents beyond just transactional, navigational and informational. Identifying intent shifts is important during core updates. Sites may need to optimize for new intents through different content types and sections.
3. Responding effectively to core updates requires analyzing "before and after" data to understand changes, identifying new intents or page types, and ensuring content matches appropriate intents across video, images, knowledge graphs and more.
A brief introduction to DataScience with explaining of the concepts, algorithms, machine learning, supervised and unsupervised learning, clustering, statistics, data preprocessing, real-world applications etc.
It's part of a Data Science Corner Campaign where I will be discussing the fundamentals of DataScience, AIML, Statistics etc.
Time Management & Productivity - Best PracticesVit Horky
Here's my presentation on by proven best practices how to manage your work time effectively and how to improve your productivity. It includes practical tips and how to use tools such as Slack, Google Apps, Hubspot, Google Calendar, Gmail and others.
The six step guide to practical project managementMindGenius
The six step guide to practical project management
If you think managing projects is too difficult, think again.
We’ve stripped back project management processes to the
basics – to make it quicker and easier, without sacrificing
the vital ingredients for success.
“If you’re looking for some real-world guidance, then The Six Step Guide to Practical Project Management will help.”
Dr Andrew Makar, Tactical Project Management
7. Định nghĩa về khoảng cách
• Cho 2 vector có các thành phaLn là
• Khoảng cách bậc của 2 vector này là con so)
đươ
̣ c ký hiệu là, ngươ
̀ i ta còn gọi là khoảng
cách Miskowsky
• Trong một so) trươ
̀ ng hơ
̣ p riêng
X, Y
X = (x1, x2, . . . , xn)T
, Y = (y1, y2, . . . , yn)T
p
∥X − Y∥p =
(
N
∑
i=1
|xi − yi |p
)
1/p
7
• Trong một so) trươ
̀ ng hơ
̣ p riêng, có
• Khoảng cách Euclide (hay là chuaEn )
• Khoảng cách tuyệt đo)i ( ) hay còn
gọi là khoảng cách Hamming, hay
Manhattan
L2
∥X − Y∥2 =
N
∑
i=1
|xi − yi |2
p = 1
∥X − Y∥ =
N
∑
i=1
|xi − yi |
8. Ví dụ KNN
• Khoảng cách Chebyshev
∥X − Y∥ = lim
p→∞
∥X − Y∥p
=
(
N
∑
i=1
|xi − yi |p
)
1/p
= max
1≤i≤n
|xi − yi |
8
• La)y dư
̃ liệu bệnh tieEu đươ
̀ ng của ngươ
̀ i Aj n
Độ (https://www.kaggle.com/uciml/
pima-indians-diabetes-database)
• Dư
̃ liệu bao goLm:
• Pregnancies: So) laLn mang thai
• Glucose: Lươ
̣ ng Glucose
• BloodPressure: An p huye)t tâm trương
• SkinThickness: VeL da
• Insulin: NoLng độ insulin
• BMI: Body mass index (tỷ lệ giư
̃ a trọng
lươ
̣ ng và chieLu cao)
• DiabetesPedigreeFunction: loại tieEu
đươ
̀ ng
• Age: TuoEi
• Outcome: Ke)t quả 1 hoặc 0
9. Python với sci-kit learn
• Mô tả và chuaEn bị dư
̃ liệu
import pandas as pd
import numpy as np
from sklearn import model_selection,metrics,neighbors
import matplotlib.pyplot as plt
data = pd.read_csv( '../dataset/diabetes.csv' )
N = len( data.columns ) - 1
X = data.iloc[:,:-1].values
y = data.iloc[:,N].values
X_train, X_test, y_train, y_test =
model_selection.train_test_split( X,y,test_size=0.3,random_state=42 )
9
• Tạo mô hı̀nh và thư
̉ nghiệm
NUM_K = 3
model = neighbors.KNeighborsClassifier( n_neighbors=NUM_K,p=2 )
model.fit( X_train,y_train )
y_pred = model.predict( X_test )
10. • Thư
̉ vơ
́ i nhieLu K khác nhau đeE bieEu dieYn thành
đoL thị, qua đó bie)t đươ
̣ c vơ
́ i K baVng ma)y thı̀ có
tỷ lệ so) trươ
̀ ng hơ
̣ p dư
̣ đoán sai
NUM_K, error = 50, []
for i in range(1,NUM_K):
model = neighbors.KNeighborsClassifier(
n_neighbors=i )
model.fit( X_train,y_train )
y_pred = model.predict( X_test )
error.append( np.mean(y_pred != y_test) )
10
plt.figure( figsize=(8,6) )
plt.title( "Đồ thị số lượng khác nhau giữa kết quả đự đoán và dataset" )
plt.plot( range(1,NUM_K),error,color='red',linestyle='dashed',marker='o',markerfacecolor='blue',
markersize=5 )
plt.xlabel( "Số láng giềng K" )
plt.ylabel( "Số lượng khác trên tổng số dữ liệu cần test" )
plt.show()
File: KNN-sklearn.py
11. KNN dùng TensorFlow ver 2
data = pd.read_csv( '../dataset/diabetes.csv' )
N = len( data.columns ) - 1
X = data.iloc[:,:-1].values
y = data.iloc[:,N].values
X_train,X_test,Y_train,Y_test =
model_selection.train_test_split( X,y,test_size=0.3
3,random_state=42 )
• Ơ
u đây ta định nghı̃a hàm đeE tı̀m khoảng cách nhỏ
nha)t tư
̀ các phaLn tư
̉ trong tập kieEm tra đe)n tập
hua)n luyện.
def mindist( x_test, x_train ):
L = tf.sqrt( tf.reduce_sum(
tf.square(tf.subtract(x_test,x_train)),1 ) )
return tf.argmin( L,0 ).numpy()
• La)y lại dư
̃ liệu đã có trong vı́ dụ trươ
́ c veL
bệnh tieEu đươ
̀ ng.
• Mô tả thêm thư viện TensorFlow
import tensorflow as tf
import pandas as pd
import numpy as np
from sklearn import model_selection, metrics
• Load dư
̃ liệu tư
̀ tập tin sau đó dành ra
30% làm dư
̃ liệu kieEm tra, và dư
̃ liệu này
co) định trong các laLn thư
̣ c thi khác nhau
của chương trı̀nh
11
12. • Gọi hàm mindist() ơ
̉ trên đeE laLn lươ
̣ t tı̀m phaLn tư
̉ nào trong tập
hua)n luyện mà gaLn vơ
́ i tư
̀ ng phaLn tư
̉ của tập kieEm tra nha)t, sau
đó la)y nhãn của phaLn tư
̉ tương ư
́ ng cuả tập hua)n luyện gán cho
tập dư
̣ đoán này.
• Tư
̀ đó tı́nh độ chı́nh xác
y_pred = tf.Variable( [1]*len(X_test) )
for i in range( len(X_test) ):
k = mindist( X_test[i,:],X_train )
y_pred[i].assign( Y_train[k] )
print( "Accuracy: ",
metrics.accuracy_score(y_pred.numpy(),Y_test) )
• Ke)t quả
Accuracy: 0.6968503937007874
12
File: KNN-tensorHlow.py
13. • Tổng kết lại: KNN đươ
̣ c sư
̉ dụng cho việc dư
̣ báo cả phân loại và hoLi quy.
• Tuy nhiên, nó đươ
̣ c sư
̉ dụng rộng rãi cho phân loại, và thươ
̀ ng đươ
̣ c sư
̉ dụng trong các ư
́ ng
dụng tı̀m kie)m.
• 1-NN đeE tạo một mô hı̀nh có các lơ
́ p dư
̣ a trên 1 đieEm dư
̃ liệu ơ
̉ khoảng cách nhỏ nha)t. Tương
tư
̣ , 2-NN có nghı̃a là chúng ta phải tạo một mô hı̀nh có các lơ
́ p dư
̣ a trên 2 đieEm dư
̃ liệu vơ
́ i
khoảng cách nhỏ nha)t.
• Các bươ
́ c thuật toán bao goLm:
• Tı́nh khoảng cách giư
̃ a các đieEm dư
̃ liệu mơ
́ i so vơ
́ i ta)t cả các dư
̃ liệu hua)n luyện
• Chọn ra K mục có trong dư
̃ liệu hua)n luyện gaLn nha)t vơ
́ i đieEm dư
̃ liệu mơ
́ i.
• Bı̀nh chọn lơ
́ p hay nhãn phoE bie)n nha)t trong so) các mục K , đó là lơ
́ p của đieEm dư
̃ liệu
mơ
́ i.
13
14. • Thuật toán KNN đươ
̣ c chọn khi các so) lươ
̣ ng các lơ
́ p dư
̃ liệu tương ư
́ ng nhau veL kı́ch cơ
̃
• Khi một đieEm dư
̃ liệu phụ thuộc nhieLu tham so) thı̀ KNN sẽ phải tı́nh khoảng cách giư
̃ a các
đieEm ra)t chậm
• Chọn K baVng bao nhiêu cũng là va)n đeL khó trên một dataset cụ theE
14
15. Thao tác thông dụng về TensorFlow version 2
• Khi dùng TensorFlow chúng ta
phải khai báo trong vùng của nó.
• Khai báo bie)n dùng tf.Variable()
• Khai báo haVng, dùng
tf.constant()
• Truy cập một phaLn tư
̉ của
tensor, vı́ dụ:
• t4[1,1,2] có giá trị là 11.0
• t3[0,3] có giá trị là 4
15
16. • ChuyeEn một bie)n của TensorFlow trơ
̉ thành một
bie)n của Python dùng phương thư
́ c numpy() của
đo)i tươ
̣ ng đó
• Ngươ
̣ c lại, dùng phương thư
́ c
convert_to_tensor() của TensorFlow
16
• ChuyeEn vị ma trận, nhân ma trận
• Tı́nh giá trị trung bı̀nh
17. Thuật toán K-means
• Trong thư
̣ c te) thươ
̀ ng hay có việc phân loại mang tı́nh tương đo)i, cha{ng hạn trong một toE
chư
́ c do con ngươ
̀ i đieLu hành và quản lý, thı̀ có như
̃ ng nhóm ngươ
̀ i có cùng một nhận định
veL một sư
̣ kiện nào đó.
• Va)n đeL đặt ra là caLn phân loại đeE bie)t một ngươ
̀ i trong toE chư
́ c này cơ bản là thuộc veL
nhóm nào.
• Cũng như vậy, có bao nhiêu ngươ
̀ i trong cùng một loại bệnh trong một cộng đoLng dân cư
• Va)n đeL phư
́ c tạp ơ
̉ đây là không bie)t trươ
́ c các đặc trưng của nhóm đeE phân lơ
́ p như trong
thuật toán KNN.
• K-means hay K-means clustering, là thuật toán gom cụm K nhóm theo trung bình.
• Thuật toán giúp cho chúng ta giải quye)t va)n đeL này. Đây chı́nh là thuật toán thuộc loại học
không giám sát
17
18. Minh hoạ
• Phân cụm K-means là thuật toán đơn giản
trong unsupervised learning.
• Trong K-means clustering, nhãn của tư
̀ ng
đieEm dư
̃ liệu (Data point) không bie)t
trươ
́ c.
• Va)n đeL là làm theE nào đeE phân dư
̃ liệu
thành các nhóm/cụm (cluster) sao cho
dư
̃ liệu trong cùng một cụm có như
̃ ng
tı́nh cha)t gio)ng nhau.
18
• Trong KNN, sau khi phân lơ
́ p xong thı̀ moYi
đieEm dư
̃ liệu chı̉ thuộc một lơ
́ p hay nhãn
duy nha)t.
• Trong khi đó vơ
́ i K-means, moYi phaLn tư
̉ có
theE thuộc nhieLu nhóm hay cụm.
• Nhóm/cụm ơ
̉ đây là tập hơ
̣ p các đieEm có các
vector đặc trưng gaLn nhau.
• Việc đo khoảng cách giư
̃ a các vector thươ
̀ ng
đươ
̣ c thư
̣ c hiện dư
̣ a trên các chuaYn như đã
trı̀nh bày, trong đó khoảng cách Euclide
đươ
̣ c sư
̉ dụng phoE bie)n nha)t.
20. Ứng dụng
• ĐeE deY hı̀nh dung ta giả sư
̉ một
cá theE (một ngươ
̀ i dân, một
ngươ
̀ i làm việc, ...) trong quaLn
theE (một cộng đoLng dân cư, một
toE chư
́ c, ...) đươ
̣ c so) hoá baVng
một toạ độ
• Cha{ng hạn ta có 600 đieEm dư
̃
liệu đươ
̣ c phân boE như hı̀nh
(xi, yi)
20
21. • Dư
̃ liệu này đươ
̣ c tạo ngaYu nhiên baVng phân pho)i Gauss xung quanh 3 đieEm chı́nh (ta tạo
ra đeE định hươ
́ ng) vơ
́ i ma trận hiệp phương sai chı̉ định
centroids = [[5,15],[12,5],[20,20]]
DATASIZE = 600
K = 3
def myrand(centroid):
return np.random.multivariate_normal( mean=centroid,cov=[[10,0],[0,10]],size=DATASIZE )
X0 = myrand( centroids[0] )
X1 = myrand( centroids[1] )
X2 = myrand( centroids[2] )
X = np.concatenate((X0, X1, X2), axis = 0)
21
22. • ĐeE hua)n luyện baVng K-means
model = cluster.KMeans( n_clusters=3 )
model.fit(X)
y_pred = model.predict(X)
• Sau khi có ke)t quả dư
̣ báo, ta thư
̉ coi lại các đieEm trung tâm
print( "Các điểm trọng tâm làn", model.cluster_centers_ )
print( "Kết quả phân nhómn", y_pred )
for i in range(len(y_pred)):
print( y_pred[i], end=' ' )
• Coi qua hı̀nh minh hoạ
22
23. Dùng TensorFlow
• La)y dư
̃ liệu như trong vı́
dụ dùng phương thư
́ c của
sklearn, ơ
̉ đây cũng gio)ng
như khi dùng TensorFlow
cho KNN, ta khai báo thêm
các bie)n Tensor như sau:
X = tf.Variable( X_train )
• La)y thành phaLn trong
tập hua)n luyện đeE làm
trọng tâm
cents = tf.Variable( X[0:K]
)
K
X K
23
• Xây dư
̣ ng hàm hua)n luyện
def pred(tt):
cents_expanded = tf.expand_dims( tt,1 )
L = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(
X,cents_expanded)),axis=2))
y = tf.argmin( L,0 )
means = []
for c in range(K):
means.append( tf.reduce_mean(tf.gather(X,tf.reshape(tf.where(tf.
equal(y,c)),[1,-1])),[1]) )
new_cents = tf.concat( means,0 )
return new_cents, y
24. • Lặp lại 1000 laLn
for _ in range(1000):
cents, y_pred = pred( cents )
• Vẽ lại hı̀nh đeE trư
̣ c quan hoá
plt.figure( figsize=(10,6) )
plt.title( "Có 3 nhóm cụ thể sau 1000 lần lặp" )
plt.xlabel('Trục x')
plt.ylabel('Trục y')
plt.scatter( X_train[:,0],X_train[:,1],c=y_pred,s=20 )
plt.plot( cents[:,0],cents[:,1],'k*',color='blue',markersize=15)
plt.show()
24
File: Kmeans-tensorHlow.py
25. Ví dụ thực tế về K-means
• Trên internet có ra)t nhieLu
dataset đeE cho phép chúng ta
thư
̣ c nghiệm các phương pháp,
cũng như đeE daLn quen vơ
́ i việc
giải quye)t như
̃ ng va)n đeL thư
̣ c
te) baVng các tı́nh toán thông
minh.
• Cha{ng hạn, https://
www.kaggle.com/datasets
25
26. • Hay tại kho veL Machine Learning của
Center for Machine Learning and
Intelligent Systems (http://
archive.ics.uci.edu/ml/datasets.php)
thuộc University of California-Irvine.
• Tại đây lưu trư
̃ dataset của ra)t nhieLu
lı̃nh vư
̣ c tư
̀ năm 1987 đe)n nay. NhaVm
đeE phân tı́ch thư
̣ c nghiệm các thuật
toán học máy.
26
28. • Chúng ta có theE đọc trư
̣ c tie)p Hile này tư
̀ internet cũng vơ
́ i
như
̃ ng import như vı́ dụ trươ
́ c
dataset = pd.read_csv( "http://archive.ics.uci.edu/ml/
machine-learning-databases/00451/dataR2.csv" )
• Có theE dư
̃ liệu không đaLy đủ, baVng cách kieEm tra, roLi sau
đó có theE xư
̉ lý baVng cách thay giá trị trung bı̀nh tương
ư
́ ng,
dataset.isnull().any()
data = dataset.fillna( dataset.mean() )
dataset.isnull().any()
28
29. • Do cột cuo)i cùng là nhãn đươ
̣ c gán (đã phân lơ
́ p, nên ta dùng cột này như là dư
̃ liệu đeE so sánh
ke)t quả), tư
̀ đây tạo ra X_train và y_train như cách làm trong các vị dụ trươ
́ c
N = len(data.columns)-1
X_train = data.iloc[:,0:-1].values
y_train = data.iloc[:,N].values
• Chọn so) cụm là 2, và lặp lại 100 laLn to)i đa
model = cluster.KMeans( n_clusters=2, max_iter=100 ).fit(X_train)
y_pred = model.predict( X_train )
print( "Number of iterations run: ", model.n_iter_ )
print( "Sum of squared distances of samples to their closest cluster center: ", model.inertia_ )
print( "Coordinates of",NUM_CLUSTERS,"cluster centersn", model.cluster_centers_ )
y_pred += 1 # Do nhãn lưu giá trị là 1 và 2
print( "Accuracy: ", metrics.accuracy_score(y_train,y_pred) )
29
30. • Ke)t quả sau khi hua)n luyện baVng K-means
30
File: Kmeans-sklearn2.py
31. Tóm lại
• Vơ
́ i ý tươ
̉ ng của thuật toán là bộ dư
̃ liệu khảo sát (mà ta hay gọi là dataset) đươ
̣ c phân thành
nhóm.
• MoYi nhóm tı̀m centroid (trọng tâm) tương ư
́ ng
• Sau đó tı́nh toEng khoảng cách (sum of squared distances) của ta)t cả các dư
̃ liệu thuộc nhóm
này đe)n centroid; sau đó cộng doLn đeE đươ
̣ c toEng của ta)t cả (Total sum of squared distances -
TSS).
• Mục tiêu phân bộ dư
̃ liệu ban đaLu thành cụm khác nhau sao cho các dư
̃ liệu thuộc cùng
một cụm là tương đoLng nha)t; đieLu đó có nghı̃a TSS là nhỏ nha)t.
• Khó khác của sư
̉ dụng K-means là lư
̣ a chọn sao cho to)i ưu.
• Một hạn che) của K-means đó là việc gom cụm mang tı́nh to)i ưu cục bộ vı̀ lơ
̀ i giải tı̀m đươ
̣ c
căn cư
́ vào đieEm trọng tâm đươ
̣ c "định hươ
́ ng" ban đaLu
K
K
K
31
32. • Do TSS giảm khi tăng, và giảm cho đe)n không ne)u baVng so) dư
̃ liệu.
• Mặt khác khi tăng (có nghı̃a là so) cụm tăng), nên thuật toán trơ
̉ nên không có ý nghı̃a
khi so) cụm là quá nhieLu.
• Chọn baVng đoL thị Elbow: khi đoL thị có choY ga)p này rõ nét nha)t, đó là so) to)i ưu.
• Cha{ng hạn vơ
́ i vı́ dụ trươ
́ c, cho K đươ
̣ c thư
̉ tư
̀ 2 cho đe)n 40
tss = []
K = range(2,41)
for k in K:
model = cluster.KMeans( n_clusters=k, max_iter=100 ).fit(X)
tss.append( model.inertia_ )
K K
K
K K
32
33. • Nhı̀n vào đoL thị Elbow, ta tha)y K = 3 là choY
bị ga)p khúc nha)t
plt.figure( figsize=(10,6) )
plt.plot( K,tss )
plt.title( "Đồ thị Elbow để chọn số K" )
plt.xlabel( "Số cụm" )
plt.ylabel( "Tổng của tất cả tổng các
khoảng cách đến các điểm trung tâm" )
plt.grid( True )
plt.show())
33
34. • Nhưng do ta không theE chı̉ cho máy "nhı̀n vào đoL thị" đươ
̣ c như ngươ
̀ i, mà phải dùng tie)ng nào đó đeE
chı̉, ơ
̉ đây ta lại dùng "tie)ng Python".
• Nhưng trươ
́ c he)t phải dùng Toán học đeE nhận bie)t đâu là Elbow Point.
• ĐieEm Elbow là đieEm mà khoảng cách tư
̀ nó đe)n đươ
̀ ng tha{ng đi qua 2 đieEm đaLu và cuo)i của đươ
̀ ng
cong là lơ
́ n nha)t.
• Trươ
́ c tiên: đây là phương trı̀nh đươ
̀ ng tha{ng qua 2 đieEm A, B (là mảng các giá trị tung độ)
def fx(X,A,B):
y = []
for i in range(len(X)):
x = X[i]
temp = A[1] + (X[i]-A[0])*(B[1]-A[1])/(B[0]-A[0])
y.append( temp )
return y
34
35. • Tı́nh khoảng cách giư
̃ a như
̃ ng đieEm trên đươ
̀ ng tha{ng này và như
̃ ng đieEm ơ
̉ trên đươ
̀ ng cong mà có
cùng hoành độ, sau đó tı̀m đieEm có khoảng cách lơ
́ n nha)t đeE trả veL.
import numpy as np
from scipy.spatial import distance
def ElbowPoint( x_axis, curve ):
N = len(curve)
S = [x_axis[0],curve[0]]
E = [x_axis[N-1],curve[N-1]]
y = fx( K,S,E )
dist = []
for i in range(N):
x = x_axis[i]
dist.append( distance.cdist([[x,curve[i]]],[[x,y[i]]]) )
return np.argmax( dist )
35
• So) lươ
̣ ng cụm caLn phân nhóm đó là vị trı́
của khoảng cách lơ
́ n nha)t này
NUM_CLUSTERS = ElbowPoint( K, tss ) + 1
• Do chı̉ so) màng ba•t đaLu tư
̀ 0 nên phải cộng
thêm 1 là vậy đó !
File: Kmeans-sklearn.py