Data Journalism - Cleaning Data
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This document provides an overview of cleaning and analyzing messy data using OpenRefine. It discusses profiling data to assess its current state, cleaning data by correcting issues, exploring data by checking, filtering, and cleaning it. It also discusses sorting, faceting, detecting blanks and duplicates, removing blanks and duplicates, performing simple and advanced transformations like clustering and using regular expressions. Resources for learning more about OpenRefine and data cleaning are also provided.
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Dats nih-dccpc-kc7-april2018-prs-uoxf
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Googleslides [https://goo.gl/cd5KKa] or Slideshare [https://goo.gl/c8DH5N]
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Part1
The document provides an overview of data mining techniques and related concepts. It defines data mining and compares it to knowledge discovery in databases (KDD). It discusses the basic data mining tasks of classification, clustering, association rule mining, and summarization. It also covers related areas like databases, statistics, machine learning, and visualization techniques used in data mining. Finally, it provides an overview of common data mining techniques including decision trees, neural networks, genetic algorithms, and others.
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This document discusses data mining and its applications. It notes that large amounts of data are being collected from various sources and stored. Data mining can help analyze this data by discovering patterns and relationships that would be difficult for humans to find manually. The document provides an overview of data mining techniques like classification and discusses software used for data mining like SAS Enterprise Miner, R, and Weka.
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The document discusses key concepts related to data processing including data, variables, cases, information, the steps of data processing, elements of data processing such as coding and tabulation, common problems, and software used for processing such as SPSS, SAS, and Quantum. Data processing converts raw data into usable information through steps like coding, cleaning, validating, classifying, tabulating, and analyzing the data. Tables are an important output and must be clearly formatted and labeled.
Data-Mining-ppt (1).pdf
The document provides an overview of data mining, including its definition, key components, techniques, processes, uses and challenges. Data mining involves using algorithms to identify patterns in large data sets and inferring rules to extract useful information. It has applications in various domains like marketing, risk analysis, health and science. While data mining has advantages in gaining insights, it also faces challenges of dealing with large, complex and heterogeneous data sources.
Odam: Open Data, Access and Mining
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Database Management Systems
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Dwdmunit1 a
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Data Journalism - Cleaning Data
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- 2. DATA is o(en ugly & MESSY
- 3. Data Profiling Assess current state of your data. Data Cleaning Correct the issues you found during‘data profiling’.
- 4. Exploring data Checking data Filtering data Cleaning data Reshaping data Annotating data Linking data
- 5. Dataset Powerhouse Museum objects collection Download from: http://data.freeyourmetadata.org/powerhouse- museum/phm-collection.tsv Open Refine and load the dataset.
- 6. Sorting data
- 7. Faceting data To select a subset of your data to work on. To get useful insight into your data. To apply a transformation to a subset of your data.
- 8. Types of Facets Text facets for text Numeric facets for number and dates Predefined/customised facets
- 9. Text facets Text facets used for faceting text Examples:County or city names, TD names
- 10. Text facets
- 11. Numeric facets Numeric facets used for faceting numerical values and ranges. Examples:Expenditure,crime rate
- 12. Numeric facets
- 13. Detecting blanks
- 14. Removing blanks
- 16. Removing duplicates Warning: If we remove all the original records will also be removed!
- 18. Removing duplicates Now you can remove. Facet by blank
- 19. Congratulations you have removed all blank and duplicate values.
- 21. Advanced data operations Clustering Transformations Multi-valued cells Derived columns Splitting data across columns Regular Expressions GREL(General Refine Expression Language)
- 22. Multi-valued cells To split a cell in
- 23. Clustering To cluster similar (syntactically) items together. To be used to fix inconsistencies,typos,etc. Examples in the dataset: Agricultural equipment Agricultural Equipment Costume Costumes
- 24. Clustering
- 25. Clustering
- 27. Transforming cell valuesGREL (General Refine Expression Language)
- 28. Resources Using OpenRefine by RubbenVerborgh and Max DeWilde http://freeyourmetadata.org/cleanup/ Cleaning Data with Refine, School of Data The Bastard Book of Regular Expressions by Dan Nguyen GREL:https://github.com/OpenRefine/OpenRefine/wiki/General-Refine-Expression-Language
- 29. Ques8ons? Bahareh R. Heravi @Bahareh360