Better question... Where did you get your #dataslice?

he doesn't tho. check for yourself: '{ "jsonrpc": "2.0", "id": 1, "method": "getProgramAccounts", "params": [ "11111111111111111111111111111111", { "encoding": "base64", "commitment": "finalized", "withContext": true, "dataSlice": { "offset": 0, "length": 0 }, "filters": [ { "dataSize": 80 }, { "memcmp": { "offset": 8, "bytes": "E5xrcuJNWzthXh61wuEKbvLLtJ3kBqZbxaEtimWjqdQk" } } ] } ] }'

Bowl game....hahahahaha. What a bitch ass #dataslice, you f'in tsip.

since you want to #dataslice, how about you compare #sarkypoos first two seasons to Elko's first two seasons. Then you'll really get slapped in the face with some real data. #elko - .730 #career500sark - .520 (in the #little12)

When it comes to learning data science, statistics, and programming, having the right resources is essential. Here are some of my top picks that helped me enhance my own skills over the past years: ✔️ Blogs: 1️⃣ Statistics by Jim: Jim Frost explains statistical concepts and methodologies like linear regression and hypothesis testing. 2️⃣ Machine Learning Mastery: Jason Brownlee provides over 1000 tutorials on machine learning and AI for all skill levels. 3️⃣ Bruno Rodrigues’ Blog: Advanced articles on statistical methods in R, perfect for those looking to deepen their knowledge. 4️⃣ Data36: Tomi Mester offers tips on becoming a data scientist with practical programming instructions in Python, SQL, and Bash. 5️⃣ Data Science for Social Scientists: Richard N. Landers offers a course with materials and videos tailored for those familiar with social scientific methods but new to programming. ✔️ YouTube Channels: 1️⃣ Statistics Globe: My channel with over 1200 videos on R programming, Python, and statistics, aimed at solving specific problems. 2️⃣ Milos Makes Maps: Milos Popovic guides you through creating various types of maps in R. 3️⃣ Data Professor: Chanin Nantasenamat’s channel covers big data, machine learning, and web applications, with tutorials in R, Python, and Weka. 4️⃣ dataslice: Comprehensive guides for R programming, including data visualization and web scraping. 5️⃣ R Programming 101: Greg Martin’s tutorials on basic R programming and graphics creation using ggplot2. 6️⃣ Bryan Jenks: Diverse topics including Markdown, Obsidian, JavaScript, and R packages. ✔️ Books: 1️⃣ The Big Book of R: A free resource curated by Oscar Baruffa, listing books on various topics related to data science and statistics in R. You can find all links and further descriptions here: statisticsglobe.com/statisti… I know there are many other great resources, but these helped me tremendously in my own career. If you have any other suggestions, feel free to share in the comments! 😊 If you are looking for a structured course that helps you get started with these topics, you may check out my introduction to R programming course. More information: statisticsglobe.com/online-c… #datasciencetraining #Python #Data #DataViz #RStats #pythonlearning #datastructure

Blahahahahaha ... don't forget the "at the time" dataslice. This is classic #tsipassery

y'all dumbases want to keep the red river pillow fight in Dallas, not our fault. But it sure helps your #dataslice, doesn't it. #queensoflackingcontext

When it comes to learning data science, statistics, and programming, having the right resources is essential. Here are some of my top picks that helped me enhance my own skills over the past years: ✔️ Blogs: 1️⃣ Statistics by Jim: Jim Frost explains statistical concepts and methodologies like linear regression and hypothesis testing. 2️⃣ Machine Learning Mastery: Jason Brownlee provides over 1000 tutorials on machine learning and AI for all skill levels. 3️⃣ Bruno Rodrigues’ Blog: Advanced articles on statistical methods in R, perfect for those looking to deepen their knowledge. 4️⃣ Data36: Tomi Mester offers tips on becoming a data scientist with practical programming instructions in Python, SQL, and Bash. 5️⃣ Data Science for Social Scientists: Richard N. Landers offers a course with materials and videos tailored for those familiar with social scientific methods but new to programming. ✔️ YouTube Channels: 1️⃣ Statistics Globe: My channel with over 1200 videos on R programming, Python, and statistics, aimed at solving specific problems. 2️⃣ Milos Makes Maps: Milos Popovic guides you through creating various types of maps in R. 3️⃣ Data Professor: Chanin Nantasenamat’s channel covers big data, machine learning, and web applications, with tutorials in R, Python, and Weka. 4️⃣ dataslice: Comprehensive guides for R programming, including data visualization and web scraping. 5️⃣ R Programming 101: Greg Martin’s tutorials on basic R programming and graphics creation using ggplot2. 6️⃣ Bryan Jenks: Diverse topics including Markdown, Obsidian, JavaScript, and R packages. ✔️ Books: 1️⃣ The Big Book of R: A free resource curated by Oscar Baruffa, listing books on various topics related to data science and statistics in R. You can find all links and further descriptions here: statisticsglobe.com/statisti… I know there are many other great resources, but these helped me tremendously in my own career. If you have any other suggestions, feel free to share in the comments! 😊 If you are looking for a structured course that helps you get started with these topics, you may check out my introduction to R programming course. See this link for additional information: statisticsglobe.com/online-c… #statisticsclass #statisticians #rstudioglobal #RStats #Python #DataVisualization

When it comes to learning data science, statistics, and programming, having the right resources is essential. Here are some of my top picks that helped me enhance my own skills over the past years: ✔️ Blogs: 1️⃣ Statistics by Jim: Jim Frost explains statistical concepts and methodologies like linear regression and hypothesis testing. 2️⃣ Machine Learning Mastery: Jason Brownlee provides over 1000 tutorials on machine learning and AI for all skill levels. 3️⃣ Bruno Rodrigues’ Blog: Advanced articles on statistical methods in R, perfect for those looking to deepen their knowledge. 4️⃣ Data36: Tomi Mester offers tips on becoming a data scientist with practical programming instructions in Python, SQL, and Bash. 5️⃣ Data Science for Social Scientists: Richard N. Landers offers a course with materials and videos tailored for those familiar with social scientific methods but new to programming. ✔️ YouTube Channels: 1️⃣ Statistics Globe: My channel with over 1200 videos on R programming, Python, and statistics, aimed at solving specific problems. 2️⃣ Milos Makes Maps: Milos Popovic guides you through creating various types of maps in R. 3️⃣ Data Professor: Chanin Nantasenamat’s channel covers big data, machine learning, and web applications, with tutorials in R, Python, and Weka. 4️⃣ dataslice: Comprehensive guides for R programming, including data visualization and web scraping. 5️⃣ R Programming 101: Greg Martin’s tutorials on basic R programming and graphics creation using ggplot2. 6️⃣ Bryan Jenks: Diverse topics including Markdown, Obsidian, JavaScript, and R packages. ✔️ Books: 1️⃣ The Big Book of R: A free resource curated by Oscar Baruffa, listing books on various topics related to data science and statistics in R. You can find all links and further descriptions here: statisticsglobe.com/statisti… I know there are many other great resources, but these helped me tremendously in my own career. If you have any other suggestions, feel free to share in the comments! 😊 If you are looking for a structured course that helps you get started with these topics, you may check out my introduction to R programming course. Further details: statisticsglobe.com/online-c… #DataAnalytics #Statistical #database #DataVisualization #statisticsclass #RStats

Not even most watched WCWS game... Nice #dataslice

When it comes to learning data science, statistics, and programming, having the right resources is essential. Here are some of my top picks that helped me enhance my own skills over the past years: ✔️ Blogs: 1️⃣ Statistics by Jim: Jim Frost explains statistical concepts and methodologies like linear regression and hypothesis testing. 2️⃣ Machine Learning Mastery: Jason Brownlee provides over 1000 tutorials on machine learning and AI for all skill levels. 3️⃣ Bruno Rodrigues’ Blog: Advanced articles on statistical methods in R, perfect for those looking to deepen their knowledge. 4️⃣ Data36: Tomi Mester offers tips on becoming a data scientist with practical programming instructions in Python, SQL, and Bash. 5️⃣ Data Science for Social Scientists: Richard N. Landers offers a course with materials and videos tailored for those familiar with social scientific methods but new to programming. ✔️ YouTube Channels: 1️⃣ Statistics Globe: My channel with over 1200 videos on R programming, Python, and statistics, aimed at solving specific problems. 2️⃣ Milos Makes Maps: Milos Popovic guides you through creating various types of maps in R. 3️⃣ Data Professor: Chanin Nantasenamat’s channel covers big data, machine learning, and web applications, with tutorials in R, Python, and Weka. 4️⃣ dataslice: Comprehensive guides for R programming, including data visualization and web scraping. 5️⃣ R Programming 101: Greg Martin’s tutorials on basic R programming and graphics creation using ggplot2. 6️⃣ Bryan Jenks: Diverse topics including Markdown, Obsidian, JavaScript, and R packages. ✔️ Books: 1️⃣ The Big Book of R: A free resource curated by Oscar Baruffa, listing books on various topics related to data science and statistics in R. You can find all links and further descriptions here: statisticsglobe.com/statisti… I know there are many other great resources, but these helped me tremendously in my own career. If you have any other suggestions, feel free to share in the comments! 😊 If you are looking for a structured course that helps you get started with these topics, you may check out my introduction to R programming course. For more information, visit this link: statisticsglobe.com/online-c… #database #VisualAnalytics #datavis #Statistical #DataScientist #DataAnalytics

Every SVM developer has traumatic memories of working with VersionedTransaction. Or getSignatureForAddress. Or base58 encoding a dataSlice for the getProgramAccounts memcmp filter. Writing client code doesn't have to be painful. 🙂‍↕️🥲

Every SVM developer has traumatic memories of working with VersionedTransaction. Or getSignatureForAddress. Or base58 encoding a dataSlice for the getProgramAccounts memcmp filter. Writing client code doesn't have to be painful. 😭😭😭

When it comes to learning data science, statistics, and programming, having the right resources is essential. Here are some of my top picks that helped me enhance my own skills over the past years: ✔️ Blogs: 1️⃣ Statistics by Jim: Jim Frost explains statistical concepts and methodologies like linear regression and hypothesis testing. 2️⃣ Machine Learning Mastery: Jason Brownlee provides over 1000 tutorials on machine learning and AI for all skill levels. 3️⃣ Bruno Rodrigues’ Blog: Advanced articles on statistical methods in R, perfect for those looking to deepen their knowledge. 4️⃣ Data36: Tomi Mester offers tips on becoming a data scientist with practical programming instructions in Python, SQL, and Bash. 5️⃣ Data Science for Social Scientists: Richard N. Landers offers a course with materials and videos tailored for those familiar with social scientific methods but new to programming. ✔️ YouTube Channels: 1️⃣ Statistics Globe: My channel with over 1200 videos on R programming, Python, and statistics, aimed at solving specific problems. 2️⃣ Milos Makes Maps: Milos Popovic guides you through creating various types of maps in R. 3️⃣ Data Professor: Chanin Nantasenamat’s channel covers big data, machine learning, and web applications, with tutorials in R, Python, and Weka. 4️⃣ dataslice: Comprehensive guides for R programming, including data visualization and web scraping. 5️⃣ R Programming 101: Greg Martin’s tutorials on basic R programming and graphics creation using ggplot2. 6️⃣ Bryan Jenks: Diverse topics including Markdown, Obsidian, JavaScript, and R packages. ✔️ Books: 1️⃣ The Big Book of R: A free resource curated by Oscar Baruffa, listing books on various topics related to data science and statistics in R. You can find all links and further descriptions here: statisticsglobe.com/statisti… I know there are many other great resources, but these helped me tremendously in my own career. If you have any other suggestions, feel free to share in the comments! 😊 If you are looking for a structured course that helps you get started with these topics, you may check out my introduction to R programming course. Further details: statisticsglobe.com/online-c… #database #DataVisualization #datascienceenthusiast #statisticians

When it comes to learning data science, statistics, and programming, having the right resources is essential. Here are some of my top picks that helped me enhance my own skills over the past years: ✔️ Blogs: 1️⃣ Statistics by Jim: Jim Frost explains statistical concepts and methodologies like linear regression and hypothesis testing. 2️⃣ Machine Learning Mastery: Jason Brownlee provides over 1000 tutorials on machine learning and AI for all skill levels. 3️⃣ Bruno Rodrigues’ Blog: Advanced articles on statistical methods in R, perfect for those looking to deepen their knowledge. 4️⃣ Data36: Tomi Mester offers tips on becoming a data scientist with practical programming instructions in Python, SQL, and Bash. 5️⃣ Data Science for Social Scientists: Richard N. Landers offers a course with materials and videos tailored for those familiar with social scientific methods but new to programming. ✔️ YouTube Channels: 1️⃣ Statistics Globe: My channel with over 1200 videos on R programming and statistics, aimed at solving specific problems. 2️⃣ Milos Makes Maps: Milos Popovic guides you through creating various types of maps in R. 3️⃣ Data Professor: Chanin Nantasenamat’s channel covers big data, machine learning, and web applications, with tutorials in R, Python, and Weka. 4️⃣ dataslice: Comprehensive guides for R programming, including data visualization and web scraping. 5️⃣ R Programming 101: Greg Martin’s tutorials on basic R programming and graphics creation using ggplot2. 6️⃣ Bryan Jenks: Diverse topics including Markdown, Obsidian, JavaScript, and R packages. ✔️ Books: 1️⃣ The Big Book of R: A free resource curated by Oscar Baruffa, listing books on various topics related to data science and statistics in R. You can find all links and further descriptions here: statisticsglobe.com/statisti… I know there are many other great resources, but these helped me tremendously in my own career. If you have any other suggestions, feel free to share in the comments! 😊 If you are looking for a structured course that helps you get started with these topics, you may check out my introduction to R programming course. More info: statisticsglobe.com/online-c… #statistics #datascience #rstats #python #programming

Hahaha... The f'in tsips are hilarious. They are the #dataslice queens of the internet. You can't make this shit up, the jokes write themselves. #comedyGOLD #quadfourlosers

From 2017-2021, the #infant mortality rate in the U.S. was highest in the #South and lowest in the #Northeast and #West regions. Learn more in this @MaxwellSU #SULerner #dataslice by @ArtSciencesSU PhD Student Marissa Merrifield buff.ly/3P35IAv

U.S #maternal death rates have steadily increased for all ethnoracial groups since 2018, with especially high rates for Black & American Indian women. Learn more in this @MaxwellSU #SULerner #pophealth #dataslice by Tori-Ann Haywood: buff.ly/3SWGgiB

#Anxiety levels increased substantially among #Asian American adults between 2020 and 2022. #COVID19 #pophealth #mentalhealth Learn more in this @MaxwellSU #SULerner #dataslice by Tianqi Zhou: buff.ly/3SmKlwh