Chelsea Parlett-Pelleriti

Chelsea Parlett-Pelleriti

Ph.D, stats lover/writer✍🏼, #statistics #scicomm #datascience #statstiktok 👩🏻‍💻 she/her

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20+ Book Recommendations by Chelsea Parlett-Pelleriti

  • Fun guide to learning Bayesian statistics and probability through unusual and illustrative examples. Probability and statistics are increasingly important in a huge range of professions. But many people use data in ways they don't even understand, meaning they aren't getting the most from it. Bayesian Statistics the Fun Way will change that. This book will give you a complete understanding of Bayesian statistics through simple explanations and un-boring examples. Find out the probability of UFOs landing in your garden, how likely Han Solo is to survive a flight through an asteroid shower, how to win an argument about conspiracy theories, and whether a burglary really was a burglary, to name a few examples. By using these off-the-beaten-track examples, the author actually makes learning statistics fun. And you'll learn real skills, like how to: - How to measure your own level of uncertainty in a conclusion or belief - Calculate Bayes theorem and understand what it's useful for - Find the posterior, likelihood, and prior to check the accuracy of your conclusions - Calculate distributions to see the range of your data - Compare hypotheses and draw reliable conclusions from them Next time you find yourself with a sheaf of survey results and no idea what to do with them, turn to Bayesian Statistics the Fun Way to get the most value from your data.

    @_TanHo @moriah_taylor58 Like JUST the ideas? @willkurt’s book Bayesian Statistics the Fun Way is good for this!😊

  • Models can be used in almost any domain for purposes including prediction, inference, or simply describing data. In all these cases, the predictive capacity of a model can be used to evaluate it, and we can build better, more useful models by adhering to good statistical practice. The tidymodels framework harmonizes the heterogeneous model interfaces in R and offers a consistent, flexible framework for modeling suitable for beginners as well as the very experienced. This book provides a practical introduction to how to use R software to create models, focusing on a dialect of the R programming language called the tidyverse. Software that adopts tidyverse principles shares a high-level design philosophy and low-level grammar and data structures, so learning one piece of the ecosystem makes it easier to learn the next. The tidymodels framework for modeling is built to be easily understood and used by a broad range of people.

    Can confirm this is a great book for tidy modeling! 📚#rstats #RStudioConf2022 https://t.co/Ed7Jz98Au9

  • The Effect

    Nick Huntington-Klein

    The Effect: An Introduction to Research Design and Causality is about research design, specifically concerning research that uses observational data to make a causal inference. It is separated into two halves, each with different approaches to that subject. The first half goes through the concepts of causality, with very little in the way of estimation. It introduces the concept of identification thoroughly and clearly and discusses it as a process of trying to isolate variation that has a causal interpretation. Subjects include heavy emphasis on data-generating processes and causal diagrams. Concepts are demonstrated with a heavy emphasis on graphical intuition and the question of what we do to data. When we "add a control variable" what does that actually do? Key Features: - Extensive code examples in R, Stata, and Python - Chapters on overlooked topics in econometrics classes: heterogeneous treatment effects, simulation and power analysis, new cutting-edge methods, and uncomfortable ignored assumptions - An easy-to-read conversational tone - Up-to-date coverage of methods with fast-moving literatures like difference-in-differences

    Finally got around to picking up a hard copy of @nickchk’s “The Effect”🥳 I’m about 1/3 of the way through @causalinf’s Mixtape, and figured WHY WAIT, these two books seem like they’d be great to read together! “The Effect” is a thick/chunky book! Can’t wait to dig in 📙📘 https://t.co/ksb6oNLVW8

  • Causal Inference

    Scott Cunningham

    An accessible, contemporary introduction to the methods for determining cause and effect in the social sciences "Causation versus correlation has been the basis of arguments--economic and otherwise--since the beginning of time. Causal Inference: The Mixtape uses legit real-world examples that I found genuinely thought-provoking. It's rare that a book prompts readers to expand their outlook; this one did for me."--Marvin Young (Young MC) Causal inference encompasses the tools that allow social scientists to determine what causes what. In a messy world, causal inference is what helps establish the causes and effects of the actions being studied--for example, the impact (or lack thereof) of increases in the minimum wage on employment, the effects of early childhood education on incarceration later in life, or the influence on economic growth of introducing malaria nets in developing regions. Scott Cunningham introduces students and practitioners to the methods necessary to arrive at meaningful answers to the questions of causation, using a range of modeling techniques and coding instructions for both the R and the Stata programming languages.

    Finally got around to picking up a hard copy of @nickchk’s “The Effect”🥳 I’m about 1/3 of the way through @causalinf’s Mixtape, and figured WHY WAIT, these two books seem like they’d be great to read together! “The Effect” is a thick/chunky book! Can’t wait to dig in 📙📘 https://t.co/ksb6oNLVW8

  • From the #1 New York Times bestselling author of Then She Was Gone comes “her best thriller yet” (Harlan Coben, New York Times bestselling author) about a young couple’s disappearance on a gorgeous summer night, and the mother who will never give up trying to find them. On a beautiful summer night in a charming English suburb, a young woman and her boyfriend disappear after partying at the massive country estate of a new college friend. One year later, a writer moves into a cottage on the edge of the woods that border the same estate. Known locally as the Dark Place, the dense forest is the writer’s favorite place for long walks and it’s on one such walk that she stumbles upon a mysterious note that simply reads, “DIG HERE.” Could this be a clue towards what has happened to the missing young couple? And what exactly is buried in this haunted ground? “Utterly gripping with richly drawn, hugely compelling characters, this is a first-class thriller with heart” (Lucy Foley, New York Times bestselling author) that will keep you on the edge of your seat.

    @datingdecisions The Night She Disappeared: twisty, couldn't put it down. Overall a great book. https://t.co/HRm7wJDkmR

  • An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra. This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.

    Y'all, my love for this book is unending💙 ty * ♾ @daniela_witten @GarethMJam @robtibshirani @HastieTrevor https://t.co/TcLNfBQVvD

  • Linear algebra is something all mathematics undergraduates and many other students, in subjects ranging from engineering to economics, have to learn. The fifth edition of this hugely successful textbook retains all the qualities of earlier editions while at the same time seeing numerous minor improvements and major additions. The latter include: • A new chapter on singular values and singular vectors, including ways to analyze a matrix of data • A revised chapter on computing in linear algebra, with professional-level algorithms and code that can be downloaded for a variety of languages • A new section on linear algebra and cryptography • A new chapter on linear algebra in probability and statistics. A dedicated and active website also offers solutions to exercises as well as new exercises from many different sources (e.g. practice problems, exams, development of textbook examples), plus codes in MATLAB, Julia, and Python.

    If you’ve ever wanted to learn more Linear Algebra to help you understand statistics better, Gil Strang’s MIT Course and book are so good. Even my math prof Grandpa recommended it to me years ago. https://t.co/s0yO5Hy5tt https://t.co/fPiUMdyo4j

  • Longlisted for the National Book Award New York Times Bestseller A former Wall Street quant sounds an alarm on the mathematical models that pervade modern life -- and threaten to rip apart our social fabric We live in the age of the algorithm. Increasingly, the decisions that affect our lives--where we go to school, whether we get a car loan, how much we pay for health insurance--are being made not by humans, but by mathematical models. In theory, this should lead to greater fairness: Everyone is judged according to the same rules, and bias is eliminated. But as Cathy O'Neil reveals in this urgent and necessary book, the opposite is true. The models being used today are opaque, unregulated, and uncontestable, even when they're wrong. Most troubling, they reinforce discrimination: If a poor student can't get a loan because a lending model deems him too risky (by virtue of his zip code), he's then cut off from the kind of education that could pull him out of poverty, and a vicious spiral ensues. Models are propping up the lucky and punishing the downtrodden, creating a "toxic cocktail for democracy." Welcome to the dark side of Big Data. Tracing the arc of a person's life, O'Neil exposes the black box models that shape our future, both as individuals and as a society. These "weapons of math destruction" score teachers and students, sort r sum s, grant (or deny) loans, evaluate workers, target voters, set parole, and monitor our health. O'Neil calls on modelers to take more responsibility for their algorithms and on policy makers to regulate their use. But in the end, it's up to us to become more savvy about the models that govern our lives. This important book empowers us to ask the tough questions, uncover the truth, and demand change. -- Longlist for National Book Award (Non-Fiction) -- Goodreads, semi-finalist for the 2016 Goodreads Choice Awards (Science and Technology) -- Kirkus, Best Books of 2016 -- New York Times, 100 Notable Books of 2016 (Non-Fiction) -- The Guardian, Best Books of 2016 -- WBUR's "On Point," Best Books of 2016: Staff Picks -- Boston Globe, Best Books of 2016, Non-Fiction

    @Emil_Hvitfeldt @mathbabedotorg OMG I loved Cathy's first book. What's your favorite thing about this one so far?

  • The sole survivor on a desperate, last-chance mission to save both humanity and the earth, Ryland Grace is hurtled into the depths of space when he must conquer an extinction-level threat to our species.

    @vboykis THAT WAS SUCH a good book , I finished it in one day

  • The Martian

    Andy Weir

    Stranded on Mars by a dust storm that compromised his space suit and forced his crew to leave him behind, astronaut Watney struggles to survive in spite of minimal supplies and harsh environmental challenges that test his ingenuity in unique ways. A first novel.

    non-stats book rec: I finished this in a single day! It was engaging, well written, and keeps you on the edge of your seat. Like Arrival + The Martian. Neural Networks even get a shoutout (for being black boxes lol) https://t.co/s6JnEkH2F7 https://t.co/fgBv85WHwK

  • The sole survivor on a desperate, last-chance mission to save both humanity and the earth, Ryland Grace is hurtled into the depths of space when he must conquer an extinction-level threat to our species.

    non-stats book rec: I finished this in a single day! It was engaging, well written, and keeps you on the edge of your seat. Like Arrival + The Martian. Neural Networks even get a shoutout (for being black boxes lol) https://t.co/s6JnEkH2F7 https://t.co/fgBv85WHwK

  • The Lady Tasting Tea

    David Salsburg

    Examines the works of statistics pioneer Ronald Fisher as well as other revolutionary thinkers in the field, covering the rise and fall of Karl Pearson's theories, the methods that contributed to Japan's post-war rebuilding, a pivotal early study on a Guinness beer cask, and more. Reprint. 15,000 first printing.

    @IsabelAphrael @joftius History stuff: 💙 https://t.co/igbPUGn3L3 💙 https://t.co/P5WCoqlJvk 💙 https://t.co/zrpkbyjJSg

  • The Theory That Would Not Die

    Sharon Bertsch McGrayne

    @IsabelAphrael @joftius History stuff: 💙 https://t.co/igbPUGn3L3 💙 https://t.co/P5WCoqlJvk 💙 https://t.co/zrpkbyjJSg

  • Enchantress of Numbers

    Jennifer Chiaverini

    Educated in math and science by her mother, the only legitimate child of Lord Byron is introduced into London society before forging a bond with Charles Babbage and using her talents to become the world's first computer programmer

    @IsabelAphrael @joftius History stuff: 💙 https://t.co/igbPUGn3L3 💙 https://t.co/P5WCoqlJvk 💙 https://t.co/zrpkbyjJSg

  • The groundbreaking NEW YORK TIMES and WALL STREET JOURNAL BESTSELLER that taught a generation how to earn more, save more, and live a rich life—now in a revised 2nd edition. Buy as many lattes as you want. Choose the right accounts and investments so your money grows for you—automatically. Best of all, spend guilt-free on the things you love. Personal finance expert Ramit Sethi has been called a “wealth wizard” by Forbes and the “new guru on the block” by Fortune. Now he’s updated and expanded his modern money classic for a new age, delivering a simple, powerful, no-BS 6-week program that just works. I Will Teach You to Be Rich will show you: • How to crush your debt and student loans faster than you thought possible • How to set up no-fee, high-interest bank accounts that won’t gouge you for every penny • How Ramit automates his finances so his money goes exactly where he wants it to—and how you can do it too • How to talk your way out of late fees (with word-for-word scripts) • How to save hundreds or even thousands per month (and still buy what you love) • A set-it-and-forget-it investment strategy that’s dead simple and beats financial advisors at their own game • How to handle buying a car or a house, paying for a wedding, having kids, and other big expenses—stress free • The exact words to use to negotiate a big raise at work Plus, this 10th anniversary edition features over 80 new pages, including: • New tools • New insights on money and psychology • Amazing stories of how previous readers used the book to create their rich lives Master your money—and then get on with your life.

    Hey if you’re a grad student or were recently and are having an “oh sh*t” moment realizing you never had enough money to really save and invest and now you are starting to💰… This book isn’t perfect but was so helpful. I learned about retirement, HSAs…index funds…etc🥳 https://t.co/9fc7giuuug

  • @M1tchRosenthal @PhDemetri That’s a great book! Super dense, and packed with good info. Maybe paired with Deep Learning by @goodfellow_ian et al after that? And of course, eventually a good Bayesian primer like @rlmcelreath’s or Andrew Gelmans??🥳 I better stop now, I could talk about books for days...

  • Statistical Rethinking

    Richard McElreath

    Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds your knowledge of and confidence in making inferences from data. Reflecting the need for scripting in today's model-based statistics, the book pushes you to perform step-by-step calculations that are usually automated. This unique computational approach ensures that you understand enough of the details to make reasonable choices and interpretations in your own modeling work. The text presents causal inference and generalized linear multilevel models from a simple Bayesian perspective that builds on information theory and maximum entropy. The core material ranges from the basics of regression to advanced multilevel models. It also presents measurement error, missing data, and Gaussian process models for spatial and phylogenetic confounding. The second edition emphasizes the directed acyclic graph (DAG) approach to causal inference, integrating DAGs into many examples. The new edition also contains new material on the design of prior distributions, splines, ordered categorical predictors, social relations models, cross-validation, importance sampling, instrumental variables, and Hamiltonian Monte Carlo. It ends with an entirely new chapter that goes beyond generalized linear modeling, showing how domain-specific scientific models can be built into statistical analyses. Features Integrates working code into the main text Illustrates concepts through worked data analysis examples Emphasizes understanding assumptions and how assumptions are reflected in code Offers more detailed explanations of the mathematics in optional sections Presents examples of using the dagitty R package to analyze causal graphs Provides the rethinking R package on the author's website and on GitHub

    @M1tchRosenthal @PhDemetri That’s a great book! Super dense, and packed with good info. Maybe paired with Deep Learning by @goodfellow_ian et al after that? And of course, eventually a good Bayesian primer like @rlmcelreath’s or Andrew Gelmans??🥳 I better stop now, I could talk about books for days...

  • An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

    @M1tchRosenthal @PhDemetri That’s a great book! Super dense, and packed with good info. Maybe paired with Deep Learning by @goodfellow_ian et al after that? And of course, eventually a good Bayesian primer like @rlmcelreath’s or Andrew Gelmans??🥳 I better stop now, I could talk about books for days...

  • Home Before Dark

    Riley Sager

    "In the latest thriller from New York Times bestseller Riley Sager, a woman returns to the house made famous by her father's bestselling horror memoir. Is the place really haunted by evil forces, as her father claimed? Or are there more earthbound-and dangerous-secrets hidden within its walls?"--

    @vboykis Home before Dark-Riley Sager My Husbands Wife-Jane Corry The Hunting Party- Lucy Foley The 7 1/2 Deaths of Evelyn Hardcastle-Stuart Turton The Heart Goes Last-Margaret Atwood The Girl in the Mirror-Rose Carlyle (Sorry this are mostly thrillers, I’m obsessed rn😅)

  • @vboykis Home before Dark-Riley Sager My Husbands Wife-Jane Corry The Hunting Party- Lucy Foley The 7 1/2 Deaths of Evelyn Hardcastle-Stuart Turton The Heart Goes Last-Margaret Atwood The Girl in the Mirror-Rose Carlyle (Sorry this are mostly thrillers, I’m obsessed rn😅)

  • "My favorite kind of whodunit, kept me guessing all the way through, and reminiscent of Agatha Christie at her best -- with an extra dose of acid." -- Alex Michaelides, author of the #1 New York Times bestseller The Silent Patient Everyone's invited...everyone's a suspect... For fans of Ruth Ware and Tana French, a shivery, atmospheric, page-turning novel of psychological suspense in the tradition of Agatha Christie, in which a group of old college friends are snowed in at a hunting lodge . . . and murder and mayhem ensue. All of them are friends. One of them is a killer. During the languid days of the Christmas break, a group of thirtysomething friends from Oxford meet to welcome in the New Year together, a tradition they began as students ten years ago. For this vacation, they've chosen an idyllic and isolated estate in the Scottish Highlands--the perfect place to get away and unwind by themselves. They arrive on December 30th, just before a historic blizzard seals the lodge off from the outside world. Two days later, on New Year's Day, one of them is dead. The trip began innocently enough: admiring the stunning if foreboding scenery, champagne in front of a crackling fire, and reminiscences about the past. But after a decade, the weight of secret resentments has grown too heavy for the group's tenuous nostalgia to bear. Amid the boisterous revelry of New Year's Eve, the cord holding them together snaps. Now one of them is dead . . . and another of them did it. Keep your friends close, the old adage goes. But just how close is too close?

    @vboykis Home before Dark-Riley Sager My Husbands Wife-Jane Corry The Hunting Party- Lucy Foley The 7 1/2 Deaths of Evelyn Hardcastle-Stuart Turton The Heart Goes Last-Margaret Atwood The Girl in the Mirror-Rose Carlyle (Sorry this are mostly thrillers, I’m obsessed rn😅)

  • "Agatha Christie meets Groundhog Day...quite unlike anything I've ever read, and altogether triumphant." -- A. J. Finn, #1 New York Times-bestselling author of The Woman in the Window Shortlisted for the Costa Award One of Stylist Magazine's 20 Must-Read Books of 2018 One of Harper's Bazaar's 10 Must-Read Books of 2018 One of Guardian's Best Books of 2018 The Rules of Blackheath Evelyn Hardcastle will be murdered at 11:00 p.m. There are eight days, and eight witnesses for you to inhabit. We will only let you escape once you tell us the name of the killer. Understood? Then let's begin... *** Evelyn Hardcastle will die. Every day until Aiden Bishop can identify her killer and break the cycle. But every time the day begins again, Aiden wakes up in the body of a different guest. And some of his hosts are more helpful than others... The most inventive debut of the year twists together a mystery of such unexpected creativity it will leave readers guessing until the very last page.

    @vboykis Home before Dark-Riley Sager My Husbands Wife-Jane Corry The Hunting Party- Lucy Foley The 7 1/2 Deaths of Evelyn Hardcastle-Stuart Turton The Heart Goes Last-Margaret Atwood The Girl in the Mirror-Rose Carlyle (Sorry this are mostly thrillers, I’m obsessed rn😅)

  • The Heart Goes Last

    Margaret Atwood

    @vboykis Home before Dark-Riley Sager My Husbands Wife-Jane Corry The Hunting Party- Lucy Foley The 7 1/2 Deaths of Evelyn Hardcastle-Stuart Turton The Heart Goes Last-Margaret Atwood The Girl in the Mirror-Rose Carlyle (Sorry this are mostly thrillers, I’m obsessed rn😅)

  • "In the vein of The Wife Between Us and Something in the Water, a debut thriller about beautiful identical twin sisters sailing a luxury yacht and racing toward a one-hundred-million-dollar inheritance"--

    @vboykis Home before Dark-Riley Sager My Husbands Wife-Jane Corry The Hunting Party- Lucy Foley The 7 1/2 Deaths of Evelyn Hardcastle-Stuart Turton The Heart Goes Last-Margaret Atwood The Girl in the Mirror-Rose Carlyle (Sorry this are mostly thrillers, I’m obsessed rn😅)

  • Linear algebra is something all mathematics undergraduates and many other students, in subjects ranging from engineering to economics, have to learn. The fifth edition of this hugely successful textbook retains all the qualities of earlier editions while at the same time seeing numerous minor improvements and major additions. The latter include: • A new chapter on singular values and singular vectors, including ways to analyze a matrix of data • A revised chapter on computing in linear algebra, with professional-level algorithms and code that can be downloaded for a variety of languages • A new section on linear algebra and cryptography • A new chapter on linear algebra in probability and statistics. A dedicated and active website also offers solutions to exercises as well as new exercises from many different sources (e.g. practice problems, exams, development of textbook examples), plus codes in MATLAB, Julia, and Python.

    @BertieArbon Hahaha that book and his lectures are pretty good😂 my grandpa is a mathematician and i think he is the one who convinced me to check it out (I used the David lay book when I took Linear Algebra, but Strang when I reviewed it on my own)