Data Science in Real Life

Start Date: 05/19/2019

Course Type: Common Course

Course Link: https://www.coursera.org/learn/real-life-data-science

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About Course

Have you ever had the perfect data science experience? The data pull went perfectly. There were no merging errors or missing data. Hypotheses were clearly defined prior to analyses. Randomization was performed for the treatment of interest. The analytic plan was outlined prior to analysis and followed exactly. The conclusions were clear and actionable decisions were obvious. Has that every happened to you? Of course not. Data analysis in real life is messy. How does one manage a team facing real data analyses? In this one-week course, we contrast the ideal with what happens in real life. By contrasting the ideal, you will learn key concepts that will help you manage real life analyses. This is a focused course designed to rapidly get you up to speed on doing data science in real life. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We've left the technical information aside so that you can focus on managing your team and moving it forward. After completing this course you will know how to: 1, Describe the “perfect” data science experience 2. Identify strengths and weaknesses in experimental designs 3. Describe possible pitfalls when pulling / assembling data and learn solutions for managing data pulls. 4. Challenge statistical modeling assumptions and drive feedback to data analysts 5. Describe common pitfalls in communicating data analyses 6. Get a glimpse into a day in the life of a data analysis manager. The course will be taught at a conceptual level for active managers of data scientists and statisticians. Some key concepts being discussed include: 1. Experimental design, randomization, A/B testing 2. Causal inference, counterfactuals, 3. Strategies for managing data quality. 4. Bias and confounding 5. Contrasting machine learning versus classical statistical inference Course promo: https://www.youtube.com/watch?v=9BIYmw5wnBI Course cover image by Jonathan Gross. Creative Commons BY-ND https://flic.kr/p/q1vudb

Course Syllabus

This course is one module, intended to be taken in one week. Please do the course roughly in the order presented. Each lecture has reading and videos. Except for the introductory lecture, every lecture has a 5 question quiz; get 4 out of 5 or better on the quiz.

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Course Introduction

Have you ever had the perfect data science experience? The data pull went perfectly. There were no m

Course Tag

Statistics Data Science Data Analysis Data Management

Related Wiki Topic

Article Example
Data science In 2013, the IEEE Task Force on Data Science and Advanced Analytics was launched, and the first international conference: IEEE International Conference on Data Science and Advanced Analytics was launched in 2014. In 2014, the American Statistical Association section on Statistical Learning and Data Mining renamed its journal to "Statistical Analysis and Data Mining: The ASA Data Science Journal" and in 2016 changed its section name to "Statistical Learning and Data Science". In 2015, the International Journal on Data Science and Analytics was launched by Springer to publish original work on data science and big data analytics. 2013 the first "European Conference on Data Analysis (ECDA)" was organised in Luxembourg establishing the European Association for Data Science (EuADS) in August 2015. In September 2015 the Gesellschaft für Klassifikation (GfKl) added to the name of the Society "Data Science Society" at the third ECDA conference at the University of Essex, Colchester, UK.
Data science he initiated the modern, non-computer science, usage of the term "data science" and advocated that statistics be renamed data science and statisticians data scientists.
Data science Data science, also known as data-driven science, is an interdisciplinary field about scientific methods, processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to Knowledge Discovery in Databases (KDD).
Data science The term "data science" (originally used interchangeably with "datalogy") has existed for over thirty years and was used initially as a substitute for computer science by Peter Naur in 1960. In 1974, Naur published "Concise Survey of Computer Methods", which freely used the term data science in its survey of the contemporary data processing methods that are used in a wide range of applications.
Data science Turing award winner Jim Gray imagined data science as a "fourth paradigm" of science (empirical, theoretical, computational and now data-driven) and asserted that "everything about science is changing because of the impact of information technology" and the data deluge.
Data science Although use of the term "data science" has exploded in business environments, many academics and journalists see no distinction between data science and statistics. Writing in Forbes, Gil Press argues that data science is a buzzword without a clear definition and has simply replaced “business analytics” in contexts such as graduate degree programs. In the question-and-answer section of his keynote address at the Joint Statistical Meetings of American Statistical Association, noted applied statistician Nate Silver said, “I think data-scientist is a sexed up term for a statistician...Statistics is a branch of science. Data scientist is slightly redundant in some way and people shouldn’t berate the term statistician.”
Data science Data science is a "concept to unify statistics, data analysis and their related methods" in order to "understand and analyze actual phenomena" with data.
Data science In April 2002, the International Council for Science: Committee on Data for Science and Technology (CODATA) started the "Data Science Journal", a publication focused on issues such as the description of data systems, their publication on the internet, applications and legal issues. Shortly thereafter, in January 2003, Columbia University began publishing "The Journal of Data Science", which provided a platform for all data workers to present their views and exchange ideas. The journal was largely devoted to the application of statistical methods and quantitative research. In 2005, The National Science Board published "Long-lived Digital Data Collections: Enabling Research and Education in the 21st Century" defining data scientists as "the information and computer scientists, database and software and programmers, disciplinary experts, curators and expert annotators, librarians, archivists, and others, who are crucial to the successful management of a digital data collection" whose primary activity is to "conduct creative inquiry and analysis."
Data science In 2001, William S. Cleveland introduced data science as an independent discipline, extending the field of statistics to incorporate "advances in computing with data" in his article "Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics," which was published in Volume 69, No. 1, of the April 2001 edition of the International Statistical Review / Revue Internationale de Statistique. In his report, Cleveland establishes six technical areas which he believed to encompass the field of data science: multidisciplinary investigations, models and methods for data, computing with data, pedagogy, tool evaluation, and theory.
Data science "Data Scientist" has become a popular occupation with Harvard Business Review dubbing it "The Sexiest Job of the 21st Century" and McKinsey & Company projecting a global excess demand of 1.5 million new data scientists. Universities are offering masters courses in data science. Shorter private bootcamps are also offering data science certificates including student-paid programs like General Assembly to employer-paid programs like The Data Incubator.
Data science In 1996, members of the International Federation of Classification Societies (IFCS) met in Kobe for their biennial conference. Here, for the first time, the term data science is included in the title of the conference ("Data Science, classification, and related methods"), after the term was introduced in a roundtable discussion by Chikio Hayashi.
In Real Life In Real Life (formerly known as In the Real World) is a Canadian reality show where eighteen young contestants aged 12–14 race across North America and compete in a series of real-life tasks, aimed to "discover the skills, strength, and stamina it takes to make it in real life." The show is developed and produced by Apartment 11 Productions. The show is hosted by Canadian comedian and actress, Sabrina Jalees.
Real data type A real data type is a data type used in a computer program to represent an approximation of a real number.
Data science It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science, in particular from the subdomains of machine learning, classification, cluster analysis, data mining, databases, and visualization.
Open science data In 2015 the World Data System of the International Council for Science adopted a new set of Data Sharing Principles to embody the spirit of 'open science'. These Principles are in line with data policies of national and international initiatives and they express core ethical commitments operationalized in the WDS Certification of trusted data repositories and service.
Data science In November 1997, C.F. Jeff Wu gave the inaugural lecture entitled "Statistics = Data Science?" for his appointment to the H. C. Carver Professorship at the University of Michigan.
Open science data In 2010 the Panton Principles launched, advocating Open Data in science and setting out for principles to which providers must comply to have their data Open.
Real life Similarly, the phrase can be used to distinguish an actor from a character, e.g. "In real life, he has a British accent" or "In real life, he lives in Los Angeles."
In Real Life (season 1) In Real Life (formerly known as In the Real World) is a Canadian reality show where eighteen young contestants aged 12–14 race across North America and compete in a series of real-life jobs, aimed to "discover the skills, strength, and stamina it takes to make it in real life." The show is developed and produced by Apartment 11 Productions.
Real life On the Internet, "real life" refers to life offline. Online, the acronym "IRL" stands for "in real life", with the meaning "not on the Internet". For example, while Internet users may speak of having "met" someone that they have contacted via online chat or in an online gaming context, to say that they met someone "in real life" is to say that they literally encountered them in a common physical location. Some, arguing that the Internet is part of real life, prefer to use "away from the keyboard" (AFK), e.g. the documentary "TPB AFK".