Introduction to Data Science in Python

Start Date: 05/19/2019

Course Type: Common Course

Course Link: https://www.coursera.org/learn/python-data-analysis

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About Course

This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses. This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python.

Course Syllabus

In this week you'll get an introduction to the field of data science, review common Python functionality and features which data scientists use, and be introduced to the Coursera Jupyter Notebook for the lectures. All of the course information on grading, prerequisites, and expectations are on the course syllabus, and you can find more information about the Jupyter Notebooks on our Course Resources page.

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Course Introduction

This course will introduce the learner to the basics of the python programming environment, includin

Course Tag

Python Programming Numpy Pandas Data Cleansing

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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 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 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 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 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 "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 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.”
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.
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 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.
Introduction to the Science of Hadith Nūr al-Dīn `Itr, in the introduction to his edition of the "Introduction", concluded that its actual name is either "ʻUlūm al-Ḥadīth" ("The Sciences of Hadith") or "Ma`rifah Anwā`i `Ilm al-Ḥadīth" ("Familiarity with the Types of the Science of Hadith"). This is based upon the author's own usage in his own introduction in addition to the usage of other scholars in the centuries after the authoring of the book. Similar to Bint `Abd al-Raḥmān, he acknowledged that the book is most commonly referred to as "Muqaddimah Ibn al-Ṣalāḥ" ("The Introduction of Ibn al-Ṣalāḥ").
National Space Science Data Center The National Space Science Data Center serves as the permanent archive for NASA space science mission data. "Space science" pertains to astronomy and astrophysics, solar and space plasma physics, and planetary and lunar science. As the permanent archive, NSSDC teams with NASA's discipline-specific space science "active archives" which provide access to data to researchers and, in some cases, to the general public. NSSDC also serves as NASA's permanent archive for space physics mission data. It provides access to several geophysical models and to data from some non-NASA mission data.
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.
Introduction to the Science of Hadith The "Introduction" has been translated into English and published as "An Introduction to the Science of Hadith", by Eerik Dickinson, as part of the Great Books of Islamic Civilization series. The translator has provided a biography of Ibn al-Ṣalāḥ derived from numerous sources, in addition to copious footnotes throughout. It is published by Garnet Publishing Limited, Reading, 2006, 356 pgs.
Open science data The concept of open access to scientific data was institutionally established with the formation of the World Data Center system (now the World Data System), in preparation for the International Geophysical Year of 1957–1958. The International Council of Scientific Unions (now the International Council for Science) established several World Data Centers to minimize the risk of data loss and to maximize data accessibility, further recommending in 1955 that data be made available in machine-readable form.
Open science data Open science data is a type of open data focused on publishing observations and results of scientific activities available for anyone to analyze and reuse. While the "idea" of open science data has been actively promoted since the 1950s, the rise of the Internet has significantly lowered the cost and time required to publish or obtain data.
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.