Start Date: 02/23/2020
Course Type: Specialization Course |
Course Link: https://www.coursera.org/specializations/data-science
Explore 1600+ online courses from top universities. Join Coursera today to learn data science, programming, business strategy, and more.Learn scalable data management, evaluate big data technologies, and design effective visualizations. This Specialization covers intermediate topics in data science. You will gain hands-on experience with scalable SQL and NoSQL data management solutions, data mining algorithms, and practical statistical and machine learning concepts. You will also learn to visualize data and communicate results, and you’ll explore legal and ethical issues that arise in working with big data. In the final Capstone Project, developed in partnership with the digital internship platform Coursolve, you’ll apply your new skills to a real-world data science project.
Data Manipulation at Scale: Systems and Algorithms
Practical Predictive Analytics: Models and Methods
Communicating Data Science Results
Data Science at Scale - Capstone Project
Tackle Real Data Challenges. Master computational, statistical, and informational data science in three courses. Data Science at Scale Specialization This Capstone Course for the Data Science Specialization aims to apply the knowledge gained in the previous four courses of the Specialization: "Introduction to Data Science", "Introduction to Machine Learning", "Introduction to Data Analysis", and "Data Exploration and Visualization". The specialization requires the completion of the following handbook: - The Data Science Handbook (2nd edition) - LIDAR (3rd edition) - CCD (4th edition) The course is also available in Italiano (free), French (hachette), Portuguesa (Praha), and Chinese (he). Course overview: - The course starts with a brief overview of the data science field, including an introduction to the special topic of machine learning. - The following courses are part of the specialization: - Introduction to Machine Learning (I), Linear Models, Discrete Optimization, Discrete Optimization on GCP, Discrete Optimization for Dense Data (DET), Discrete Optimization for Performance (DOPS) - A quick overview of the data science field, including linear models, optimization, optimization on GCP, and performance evaluation. Course summary: - The course provides an introduction to the data science field, with a brief overview of the special topic of machine learning. - The course includes linear models, random forests, and optimization. - The course includes random forests, trees
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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 | 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 | 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, 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 | 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. |
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 | 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. |
Vernier scale | Direct verniers are the most common. The indicating scale is constructed so that when its zero point is coincident with the start of the data scale, its graduations are at a slightly smaller spacing than those on the data scale and so none but the last graduation coincide with any graduations on the data scale. N graduations of the indicating scale cover N−1 graduations of the data scale. |
Specialization (pre)order | The specialization order is often considered in applications in computer science, where T spaces occur in denotational semantics. The specialization order is also important for identifying suitable topologies on partially ordered sets, as it is done in order theory. |
Specialization (functional) | Specialization is when people specialize in one thing or another which they are good at. |
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. |
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. |
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. |
National Space Science Data Center | provides access to portions of their database contains information about data archived at NSSDC (and, in some cases, other facilities), the spacecraft which generate space science data and experiments which generate space science data. NSSDC services also included are data management standards and technologies. |
Scale factor (computer science) | To illustrate the use of powers of two in the scale factor, let's use a factor of 1/16 with the above data set. The binary value for our original data set is given below: |
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. |