Data Mining Specialization

Start Date: Unknown

Course Type: Specialization Course

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About Course

The Data Mining Specialization teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp. Courses 2 - 5 of this Specialization form the lecture component of courses in the online Master of Computer Science Degree in Data Science. You can apply to the degree program either before or after you begin the Specialization.

Course Syllabus

Deep Learning Specialization on Coursera

Course Introduction

Data Mining Specialization-Analyze Text, Discover Patterns, Visualize Data。Solve real-world data mining challenges.

Course Tag

Data Science Data Visualization Text Retrieval Search Engines Text Mining Pattern Discovery Data Mining

Related Wiki Topic

Article Example
Data mining Data mining is used wherever there is digital data available today. Notable examples of data mining can be found throughout business, medicine, science, and surveillance.
Data mining Data mining is about "analyzing" data; for information about extracting information out of data, see:
Data mining Computer science conferences on data mining include:
Data mining prescription information to data mining companies who in turn provided the data
Relational data mining Relational data mining is the data mining technique for relational
Data mining Before data mining algorithms can be used, a target data set must be assembled. As data mining can only uncover patterns actually present in the data, the target data set must be large enough to contain these patterns while remaining concise enough to be mined within an acceptable time limit. A common source for data is a data mart or data warehouse. Pre-processing is essential to analyze the multivariate data sets before data mining. The target set is then cleaned. Data cleaning removes the observations containing noise and those with missing data.
Data mining In the 1960s, statisticians used terms like "Data Fishing" or "Data Dredging" to refer to what they considered the bad practice of analyzing data without an a-priori hypothesis. The term "Data Mining" appeared around 1990 in the database community. For a short time in 1980s, a phrase "database mining"™, was used, but since it was trademarked by HNC, a San Diego-based company, to pitch their Database Mining Workstation; researchers consequently turned to "data mining". Other terms used include Data Archaeology, Information Harvesting, Information Discovery, Knowledge Extraction, etc. Gregory Piatetsky-Shapiro coined the term "Knowledge Discovery in Databases" for the first workshop on the same topic (KDD-1989) and this term became more popular in AI and Machine Learning Community. However, the term data mining became more popular in the business and press communities. Currently, Data Mining and Knowledge Discovery are used interchangeably.
Data mining The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining, sequential pattern mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, but do belong to the overall KDD process as additional steps.
Data stream mining Data stream mining can be considered a subfield of data mining, machine learning, and knowledge discovery.
Oracle Data Mining As of release 11gR1 Oracle Data Mining contains the following data mining functions:
Data mining Data mining topics are also present on many data management/database conferences such as the ICDE Conference, SIGMOD Conference and International Conference on Very Large Data Bases
Data mining Data mining is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. It is an interdisciplinary subfield of computer science. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD.
Data mining While the term "data mining" itself may have no ethical implications, it is often associated with the mining of information in relation to peoples' behavior (ethical and otherwise).
Examples of data mining In the context of combating terrorism, two particularly plausible methods of data mining are "pattern mining" and "subject-based data mining".
Data Mining Extensions Data Mining Extensions (DMX) is a query language for Data Mining Models supported by Microsoft's SQL Server Analysis Services product.
Data mining In the Academic community, the major forums for research started in 1995 when the First International Conference on Data Mining and Knowledge Discovery (KDD-95) was started in Montreal under AAAI sponsorship. It was co-chaired by Usama Fayyad and Ramasamy Uthurusamy. A year later, in 1996, Usama Fayyad launched the journal by Kluwer called Data Mining and Knowledge Discovery as its founding Editor-in-Chief. Later he started the SIGKDDD Newsletter SIGKDD Explorations. The KDD International conference became the primary highest quality conference in Data Mining with an acceptance rate of research paper submissions below 18%. The Journal Data Mining and Knowledge Discovery is the primary research journal of the field.
Data mining or a simplified process such as (1) pre-processing, (2) data mining, and (3) results validation.
Oracle Data Mining Oracle Data Mining also supports a Java API consistent with the Java Data Mining (JDM) standard for data mining (JSR-73) for enabling integration with web and Java EE applications and to facilitate portability across platforms.
Oracle Data Mining Most Oracle Data Mining functions accept as input one relational table or view. Flat data can be combined with transactional data through the use of nested columns, enabling mining of data involving one-to-many relationships (e.g. a star schema). The full functionality of SQL can be used when preparing data for data mining, including dates and spatial data.
Data mining There have been some efforts to define standards for the data mining process, for example the 1999 European Cross Industry Standard Process for Data Mining (CRISP-DM 1.0) and the 2004 Java Data Mining standard (JDM 1.0). Development on successors to these processes (CRISP-DM 2.0 and JDM 2.0) was active in 2006, but has stalled since. JDM 2.0 was withdrawn without reaching a final draft.