S1-Introduction_to_Computational_physics.pdf

S1-Introduction_to_Computational_physics.pdf

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  Programa de Física Docente:Carlos Andrés Vidal Betancourt Física Computacional 1 S1 - Introduction
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  Overview Fundamental Bibliography Foreword Chapter 1– Introduction 1.1 Who should read this book? 1.2 What is data science? 1.3 Is data science new? 1.4 What can I expect from this book? 1.5 What will this book expect from me? Physicists
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  … a quote…
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  Foreword <<As we oftenhear, we live in an era where data can be collected, stored, and processed at an unprecedented (and rapidly accelerating) scale. Whether or not that happens in a way that can properly be called science, however, is a critical issue for our society.>>
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  Foreword <<…become better informedabout the role of our discipline in this emerging field. Doing so will help prepare our students for careers that will increasingly depend on some level of competency in understanding how to use data to inform decisions, regardless of the specific discipline or industry in which they find themselves.>>
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  Foreword << I believeit is an ethical and, in fact, an existential imperative for the mathematical sciences community to develop a deeper understanding of the role of our disciplines in data science and to change our educational programs to enable our students to engage with data effectively, and with integrity.>>
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  1.1 Who shouldread this book? << The job market continues to demand data scientists…>> << Practitioners in these fields have seen the value of evidence-based decision making and communication.>> << Data science has in common with academia a quest for answers. >> “There aren’t any experts; its just who’s the fastest learner” Eric Place
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  1.2 What isdata science? << In 2001, William Cleveland published a paper in International Statistical Review that named a new field, “Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics.” >> << In Doing Data Science, Cathy O’Neil and Rachel Schutt say that the term “data scientist” wasn’t coined until seven years after Cleveland’s article, in 2008.>>
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  1.2 What isdata science? << In fact, reading data and computing answers from it sometimes involves unexpected and clever repurposing of data or tools, which the word “hacking” describes very well. >>
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  1.2 What isdata science? << But the gist is that machine learning differs from traditional mathematical modeling in that the analyst does not impose as much structure when using machine learning as he or she would when doing mathematical modeling, thus requiring less domain knowledge. Instead, the machine infers more of the structure on its own. >>
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  1.2 What isdata science? << 1. A question arises that could be answered with data. 2. The data scientist prepares to do an analysis. This includes finding relevant data, understanding its origins and meaning, converting it to a useful format, cleaning it, and other necessary precursors to analysis of that data. This includes two famous acronyms, ETL (extract, transform, and load the data) and EDA (exploratory data analysis). 3.The data scientist performs an analysis based on the data. Satisfying necessary mathematical assumptions, ensuring computations are feasible, complying with software license agreements, using data only in legal and ethical ways, and more. 4. The data scientist reports any useful results. If no useful results were obtained, returning to an earlier step may be necessary. >>
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  1.3 Is datascience new? << From statistics to paradigm shift... 1. The proliferation of data-generating devices (Big Data). ”Data analysis has replaced data acquisition as the new bottleneck to discoveries” Bill Howe 2. Advances in hardware and software have made it possible to implement ideas that where once outside the capabilities.>>
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  1.3 Is datascience new? << Donoho say this is a “game changer”, turning algorithms, which where once things described only in natural language in statistics papers, into things that are downloadable, actionable and testable Shared more often on code websites than academic papers Git-hub, cloud services and dashboards… Data products. Builds tools can answering questions on demand >>
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  1.4 What canwe expect from this book? << The chapters represents the essentials of data science, and each one provides references to dive deeper. Each chapter covers all the most common concepts within its topic so that you have a broad understanding of that topic. Chapter typically give only large project-sized assignments. Every reader should do these projects. Skipping them will only give the illusion of understanding.>> Chapter 2 – Programming with Data Chapter 3 – Linear Algebra Chapter 4 – Basic Statistics Chapter 5 – Cluster Analysis Chapter 6 – Operations Research Chapter 7 – Dimensionality Reduction Chapter 8 – Machine Learning Chapter 9 – Deep Learning Chapter 10 – Topological Data Analysis
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  1.4 What canwe expect from this book?
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  1.5 What willthis book expect from me? << O’Neil and Schutt “ A data-savvy, quantitatively minded, coding-literate and problem solver”. You need to know or be willing to learn those domains to which you want to apply data science. INFORMS defines analytics as “The scientific process of transforming data into insight for making better decisions”. Rob Gould emphasized “How challenging it can be to shift from deductive to inductive reasoning”. The data science job is to apply to the available data appropriate mathematical and statistical theory, usually using computer code in a way that yields useful results.”>>
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  A data scienceinterview…