🍗 Deathfat Anna o' Brien / Glitter + Lazers / GlitterandLazers - Fat, drunk, consoomer attention whore who would rather eat and drink herself to death than endure a single negative emotion

Anna's formula:
1. loudly shriek
2. drunkenly and rapidly mutter
3. don't forget to incorporate young person phrases
4. throw in some sassy nigger hand gestures/music to really and truly show that BLM
 
GenociderSyo said:
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Being an influencer is a LEGITIMATE JOB you guys! It's extremely difficult and all you critics are just too stupid and un-influential to get how TALENTED Anna is at what she does. Jesus Christ. If you don't think Anna is CrEaTiVe and FUN and quirky uWu, the problem is with you, pal. Maybe try to crawl out of your shitty cave once in awhile to gain some culture, because maybe then you might be able to grasp a single iota of the magnitude of Anna's artistic contributions. Probably not, though, because you don't really seem like the kind of person who appreciates the creative spirit of the artiste.

Anyway, you fucking philistine shitbag, you probably cannot appreciate this, either, but Anna is so superior to you that she's not even bothered by your suggestion that her content is lacking. Your inability to appreciate something exquisite when you see it is Not Her Problem and therefore it does not bother her at all, as this series of televised diatribes makes clear. You might watch these videos, but you cannot even begin to understand how unbothered Anna is.
 
Anybody notice her new "bestie" (the fat married lesbian one; I've already forgotten her name) hasn't been seen since the Disney vacation? She got her freebie trip, suffered and sacrificed for it and now apparently has disappeared without a trace. I am curious if she'll show up again, since Curveblob decided to dip out slowly after HER free vacation, coming around now and then for free meals and social media spottings but has also been around less and less.
 
Only mention of Anna by Emely was a few pics advertising photopass (Actually showing magic-shots unlike Anna's which is one of their selling points)
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lonelygorl15 said:
Anybody notice her new "bestie" (the fat married lesbian one; I've already forgotten her name) hasn't been seen since the Disney vacation? She got her freebie trip, suffered and sacrificed for it and now apparently has disappeared without a trace. I am curious if she'll show up again, since Curveblob decided to dip out slowly after HER free vacation, coming around now and then for free meals and social media spottings but has also been around less and less.
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Anna’s first assistant
The sister/sister-in-law
Eat it Katy
Curve model
Her black friends she twerked with on the boat in Mexico
Emely from Disney
 
So Anna has a master's degree in statistics. I didn't know that, but think of the actual work she could be doing. And probably good paying too, but I don't know much about stats.
 
Mandowhorian said:
So Anna has a master's degree in statistics. I didn't know that, but think of the actual work she could be doing. And probably good paying too, but I don't know much about stats.
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Her degree is:
"Quantitative Methods in the Social Sciences"
It is entirely pass/fail and only required 30 credits. She chose no specialization.

  • Receive a passing grade in the five required QMSS courses:
    • QMSS GR5010 Quantitative Theory and Methodology
      • This interdisciplinary course, taken in the fall semester, is a comprehensive introduction to quantitative research in the social sciences. The course focuses on foundational ideas of social science research, including strengths and weaknesses of different research designs, interpretation of data drawn from contemporary and historical contexts, and strategies for evaluating evidence. The majority of the course is comprised of two-week units examining particular research designs, with a set of scholarly articles that utilize that design. Topics include: the “science” of social science and the role of statistical models, causality and causal inference, concepts and measurement, understanding human decision making, randomization and experimental methods, observation and quasi-experimentation, sampling, survey research, and working with archival data.
    • QMSS GR5015 Data Analysis for the Social Sciences (or Focus equivalent)
      • The data analysis course covers specific statistical tools used in social science research using the statistical program R. Topics to be covered include statistical data structures, and basic descriptives, regression models, multiple regression analysis, interactions, polynomials, Gauss-Markov assumptions and asymptotics, heteroskedasticity and diagnostics, models for binary outcomes, naive Bayes classifiers, models for ordered data, models for nominal data, first difference analysis, factor analysis, and a review of models that build upon OLS. Prerequisite: introductory statistics course that includes linear regression.
    • QMSS GR5021 Research Seminar I & QMSS GR5022 Research Seminar II
      • This course is designed to expose students in the QMSS degree program to different methods and practices of social science research. Seminar presentations are given on a wide range of topics by faculty from Columbia and other New York City universities, as well as researchers from private, government, and non-profit settings. QMSS students participate in a weekly seminar. Speakers include faculty from Columbia and other universities, and researchers from the numerous corporate, government, and non-profit settings where quantitative research tools are used. Topics have included: Now-Casting and the Real-Time Data-Flow; Art, Design & Science in Data Visualization; Educational Attainment and School Desegregation: Evidence from Randomized Lotteries; Practical Data Science: North American Oil and Gas Drilling Data.
    • QMSS GR5999 Master's Thesis
      • All students must complete an MA thesis, which involves original statistical analysis, under the supervision of the student's advisor and the QMSS program director. Students should register for this course in the last semester of their program
  • Complete at least 4 approved electives
    • GR5010 QUANTITATIVE THEORY & METHODOLOGY
      • This interdisciplinary course, taken in the fall semester, is a comprehensive introduction to quantitative research in the social sciences. The course focuses on foundational ideas of social science research, including strengths and weaknesses of different research designs, interpretation of data drawn from contemporary and historical contexts, and strategies for evaluating evidence. The majority of the course is comprised of two-week units examining particular research designs, with a set of scholarly articles that utilize that design. Topics include: the “science” of social science and the role of statistical models, causality and causal inference, concepts and measurement, understanding human decision making, randomization and experimental methods, observation and quasi-experimentation, sampling, survey research, and working with archival data.
    • GR5016 REGRESSION MODEL-TEMP PROCESS
      • This course will introduce students to the main concepts and methods behind regression analysis of temporal processes and highlight the benefits and limitations of using temporally ordered data. Students study the complementary areas of time series data and longitudinal (or panel) data. There are no formal prerequisites for the course, but a solid understanding of the mechanics and interpretation of OLS regression will be assumed (we will briefly review it at the beginning of the course). Topics to be covered include regression with panel data, probit and logit regression of pooled cross-sectional data, difference-in-difference models, time series regression, dynamic causal effects, vector autoregressions, cointegration, and GARCH models. Statistical computing will be carried out in R.
    • GR5070 GIS & SPATIAL ANALYSIS-SOC SCI
      • This course introduces students to basic spatial analytic skills. It covers introductory concepts and tools in Geographic Information Systems (GIS) and database management. As well, the course introduces students to the process of developing and writing an original spatial research project. Topics to be covered include: social theories involving space, place and reflexive relationships; social demography concepts and databases; visualizing social data using geographic information systems; exploratory spatial data analysis of social data and spatially weighted regression models, spatial regression models of social data, and space-time models. Use of open-source software (primarily the R software package) will be taught as well..
    • GR5058 DATA MINING FOR SOCIAL SCIENCE
      • The class is roughly divided into two parts: 1. programming best practices, exploratory data analysis (EDA), and unsupervised learning 2. supervised learning including regression and classification methods In the first part of the course we will focus writing R programs in the context of simulations, data wrangling, and EDA. Unsupervised learning is focused on problems where the outcome variable is not known and the goal of the analysis is to find hidden structure in data such as different market segments from buying patterns or human population structure from genetic data. Supervised learning deals with prediction problems where the outcome variable is known such as predicting the price of a house in a certain neighborhood or an outcome of a congressional race.
    • GR5018 ADV ANALYTIC TECHNIQUES
      • This course is meant to train students in advanced quantitative techniques in the social sciences. Statistical computing will be carried out in R. Topics include: review of multiple/linear regression, review of logistic regression, generalized linear models, models with limited dependent variables, first differences analysis, fixed effects, random effects, lagged dependent variables, growth curve analysis, instrumental variable and two-stage least squares, natural experiments, regression discontinuity, propensity score matching, multilevel models or hierarchical linear models, and text-based quantitative analysis.
    • GR5062 SOCIAL NETWORK ANALYSIS
      • The course is designed to teach students the foundations of network analysis including how to manipulate, analyze and visualize network data themselves using statistical software. We will focus on using the statistical program R for most of the work. Topics will include measures of network size, density, and tie strength, measures of network diversity, sampling issues, making ego-nets from whole networks, distance, dyads, homophily, balance and transitivity, structural holes, brokerage, measures of centrality (degree, betweenness, closeness, eigenvector, beta/Bonacich), statistical inference using network data, community detection, affiliation/bipartite networks, clustering and small worlds; positions, roles and equivalence; visualization, simulation, and network evolution over time.
    • GR5063 DATA VISUALIZATION
      • This course is designed to the interdisciplinary and emerging field of data science. It will cover techniques and algorithms for creating effective visualizations based on principles from graphic design, visual art, perceptual psychology, and cognitive science to enhance the understanding of complex data. Students will be required to complete several scripting, data analysis and visualization design assignments as well as a final project. Topics include: data and image models, social and interactive visualizations, principles and designs, perception and attention, mapping and cartography, network visualization. Computational methods are emphasized and students will be expected to program in R, Javascript, D3, HTML and CSS and will be expected to submit and peer review work through Github. Students will be expected to write up the results of the project in the form of a conference paper submission.
    • GR5065 BAYESIAN STATS FOR THE SOC SCI
      • An introduction to Bayesian statistical methods with applications to the social sciences. Considerable emphasis will be placed on regression modeling and model checking. The primary software used will be Stan, which students do not need to be familiar with in advance. Students in the course will access the Stan library via R, so some experience with R would be helpful but not required. Any QMSS student is presumed to have sufficient background. Any non-QMSS students interested in taking this course should have a comparable background to a QMSS student in basic probability. Topics to be covered are a review of calculus and probability, Bayesian principles, prediction and model checking, linear regression models, Bayesian data collection, Bayesian calculations, Stan, the BUGS language and JAGS, hierarchical linear models, nonlinear regression models, missing data, stochastic processes, and decision theory.
    • GR5069 APPLIED DATA SCI FOR SOC SCIENTISTS
      • In his now classic Venn diagram, Drew Conway described Data Science as sitting at the intersection between good hacking skills, math and statistics knowledge, and sub- stantive expertise. As a result of normal instruction, social scientists possess a uid combination of all three but also bring an additional layer to the mix. We have acquired slightly dierent training, skills and expertise tailored to understand human behavior, and to explain why things happen the way they do. Social scientists are, thus, a particular kind of data scientist. This course is a collection of topics that ll very specic gaps identied over the years on what a social scientist should know at minimum when entering data science, and what a data scientist should know to hit the ground running and add immediate value to their teams.
  • Complete a minimum of 30 graduate course credits
  • Maintain a 3.0 or above average GPA.
  • Complete payment for the equivalenceof two full Residence Units.
    • "In addition to registering for individual courses, students in the Graduate School of Arts and Sciences are required to register for an enrollment category. In all PhD and almost all MA programs, this is typically done by registering for Residence Units, which provide the basis for tuition charges."
 
Mandowhorian said:
So Anna has a master's degree in statistics. I didn't know that, but think of the actual work she could be doing. And probably good paying too, but I don't know much about stats.
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She had a good job at Sprinklr but ruined it by accusing coworkers of leering at her candy juicy thighs. It would probably be difficult for her to get another job in that niche as she is an HR risk (in several ways: mega-obese, obvious alcoholic, alleges sexual harassment if anyone looks at her funny) and hasn't worked in the field since 2017.
 
rocknrollmartian said:
She had a good job at Sprinklr but ruined it by accusing coworkers of leering at her candy juicy thighs. It would probably be difficult for her to get another job in that niche as she is an HR risk (in several ways: mega-obese, obvious alcoholic, alleges sexual harassment if anyone looks at her funny) and hasn't worked in the field since 2017.
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Also, she broke rules by sneaking her dog or blatantly taking her dog into the office and letting it have diahrea shits all over that she refused to clean.
 
lonelygorl15 said:
Anybody notice her new "bestie" (the fat married lesbian one; I've already forgotten her name) hasn't been seen since the Disney vacation? She got her freebie trip, suffered and sacrificed for it and now apparently has disappeared without a trace. I am curious if she'll show up again, since Curveblob decided to dip out slowly after HER free vacation, coming around now and then for free meals and social media spottings but has also been around less and less.
Click to expand...

Guntzilla said:
Anna’s first assistant
The sister/sister-in-law
Eat it Katy
Curve model
Her black friends she twerked with on the boat in Mexico
Emely from Disney
Click to expand...
Don't forget Skinny Legend Asian Woman who "helped" Anna assemble her overpriced #sponcon sofa.
 
@GenociderSyo re: Anna’s masters

It looks like just how to qualify data but not necessarily how to interpret it. I'm not sure what you use this for except maybe marketing and you're looking for in-depth demographical info. Or for the same in politics.

Do we have any idea what her thesis was? With Masters I don't think they have to be published but I think you do still have to defend them.
 
Cripple said:
@GenociderSyo re: Anna’s masters

It looks like just how to qualify data but not necessarily how to interpret it. I'm not sure what you use this for except maybe marketing and you're looking for in-depth demographical info. Or for the same in politics.

Do we have any idea what her thesis was? With Masters I don't think they have to be published but I think you do still have to defend them.
Click to expand...
No idea but their requirements are lax and short.
 

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