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2. Data analysis - Wikipedia

   en.wikipedia.org/wiki/Data_analysis

   Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]

3. WorldQuant University - Wikipedia

   en.wikipedia.org/wiki/WorldQuant_University

   The Applied Data Science Lab is a free, credentialed offering where students use data analysis to solve real-world meaningful, and complex problems. During this self-paced course, students complete eight projects that range from exploring housing prices in Mexico to predicting air quality in Kenya.

4. Exploratory data analysis - Wikipedia

   en.wikipedia.org/wiki/Exploratory_data_analysis

   Exploratory data analysis is an analysis technique to analyze and investigate the data set and summarize the main characteristics of the dataset. Main advantage of EDA is providing the data visualization of data after conducting the analysis.

5. List of statistical software - Wikipedia

   en.wikipedia.org/wiki/List_of_statistical_software

   Pandas – High-performance computing (HPC) data structures and data analysis tools for Python in Python and Cython (statsmodels, scikit-learn) Perl Data Language – Scientific computing with Perl; Ploticus – software for generating a variety of graphs from raw data; PSPP – A free software alternative to IBM SPSS Statistics

6. List of volunteer computing projects - Wikipedia

   en.wikipedia.org/wiki/List_of_volunteer...

   Data analysis, machine learning: Research in the various fields of data analysis and machine learning, such as prediction of market value of companies'shares and analysis of medical data [181] Yes DistrRTgen 2008-01-12 [182] 2014-06 [183] Independent [14] Cryptography: Analysis of hash strength for password security by developing rainbow tables ...

7. Cross-industry standard process for data mining - Wikipedia

   en.wikipedia.org/wiki/Cross-industry_standard...

   A review and critique of data mining process models in 2009 called the CRISP-DM the "de facto standard for developing data mining and knowledge discovery projects." [16] Other reviews of CRISP-DM and data mining process models include Kurgan and Musilek's 2006 review, [8] and Azevedo and Santos' 2008 comparison of CRISP-DM and SEMMA. [9]