Start Date: 07/05/2020
Course Type: Common Course
Course Link: https://www.coursera.org/learn/what-is-datascience
The art of uncovering the insights and trends in data has been around since ancient times. The ancient Egyptians used census data to increase efficiency in tax collection and they accurately predicted the flooding of the Nile river every year. Since then, people working in data science have carved out a unique and distinct field for the work they do. This field is data science. In this course, we will meet some data science practitioners and we will get an overview of what data science is today. LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate.
In this module, you will go over the course syllabus to learn what will be taught in this course. Also, you will hear from data science professionals to learn what data science is, what data scientists do, and what tools and algorithms data scientists use on a daily basis. Finally, you will be required to complete a reading assignment to learn why data science is considered the sexiest job in the 21st century.
What is Data Science?
Data science is all around us. Technology is transforming the world of business, education, health, and culture. To be part of this transformation, it is critical that you are able to read and analyze data.
This course will cover the basic components of data science, including data collection and management, data analysis and visualization, data pipelines, data science management, and advanced coursework on Google Cloud's Big Data Institute (BDI) for Data Science and Machine Learning courses.
After completing this course, you will be able to:
• Describe the purpose of Big Data and use data to gain business advantage.
• Explore and analyze datasets.
• Apply statistical methods to analyze and visualize datasets.
• Analyze and interpret data from publicly-available or proprietary sources.
• Explore and apply machine learning methods to analyze and visualize datasets.
• Discuss and apply the BDI's Data Science Practitioner Certificate.
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|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 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||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||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 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||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||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||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.|
|E-Science librarianship||An example of librarians reconfiguring traditional librarian skills to meet the needs of researchers engaging in e-Science is Witt & Carlson’s adaptation of the traditional reference interview into a “data interview” in order to provide effective data management and e-Science services. This interview consists of ten practical queries necessary for understanding the provenance and expectations for the preservation of datasets typical of e-Science that also help illustrate some of the educational tools and skills needed by a librarian new to e-Science. "What is the story of the data? What form and format are the data in? What is the expected lifespan of the dataset? How could the data be used, reused, and repurposed? How large is the dataset, and what is its rate of growth? Who are the potential audiences for the data? Who owns the data? Does the dataset include any sensitive information? What publications or discoveries have resulted from the data? How should the data be made accessible?"|
|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.|
|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.|
|What Is This Thing Called Science?||"What Is This Thing Called Science?" was first published in 1976, and has been translated into many languages.|
|Indian Space Science Data Centre||The Indian Space Science Data Center (ISSDC) is a new ground segment facility being established by ISRO, as the primary data center for the payload data archives of Indian Space Science Missions. This data center, located at the Indian Deep Space Network (IDSN) campus in Bangalore, is responsible for the ingestion, archive, and dissemination of the payload data and related ancillary data for Space Science missions. The principal investigators of the science payloads as well as scientists from other institutions and general public will use this facility. The facility will be supporting Chandrayaan-1, ASTROSAT and Megha-tropiques and any other future space science missions.|
|What Is This Thing Called Science?||What Is This Thing Called Science? is a best-selling textbook by Alan Chalmers. It is a guide to the philosophy of science which outlines the shortcomings of naive empiricist accounts of science, and describes and assesses modern attempts to replace them. The book is written with minimal use of technical terms.|
|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.|
|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.|
|Open science data||Publishers recognise that in many disciplines data itself, in various forms, is now a key output of research. Data searching and mining tools permit increasingly sophisticated use of raw data. Of course, journal articles provide one ‘view’ of the significance and interpretation of that data – and conference presentations and informal exchanges may provide other ‘views’ – but data itself is an increasingly important community resource. Science is best advanced by allowing as many scientists as possible to have access to as much prior data as possible; this avoids costly repetition of work, and allows creative new integration and reworking of existing data.|