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Analytical tools
The document provides an overview of various analytical tools, highlighting features, capabilities, and comparative popularity among them. It reviews software such as SAS Enterprise Miner, IBM’s SPSS, R, Apache Hadoop, Microstrategy, Statistica, KXEN, and Tableau, focusing on their usability, statistical capabilities, and cost-effectiveness. R emerges as a dominant programming language in data analytics, with user preferences reflected in competitions and community engagement, despite a decline in activity in some years due to the rise of user-friendly interfaces.
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Analytical tools
- 1. Analytical Tools By :Aniket Joshi
- 2. • A GUI(Graphic User Interface) based analytics software • Versatile, powerful & user friendly • Handles large amount of data easily • Easy to learn • Popularity of SAS E-Miner doesn’t match its capability due to high price tag • One of the most expensive softwares’ which a very few companies can afford SAS Enterprise Miner
- 3. SAS Enterprise Miner FEATURES •Data Preparation, Summarization & Exploration i. Access & integrate structured and unstructured data sources ii. Outlier filtering iii. Data Partitioning iv. Integration with R v. Merge & Append Tools vi. Univariate & Bivariate statistics and plots vii. Interactive Variable Binning
- 4. • Advanced Predictive& Descriptive Modeling i. Clustering & Self Organizing Maps ii. Market Basket Analysis iii. Dimension Reduction Techniques iv. Linear & Logistic Regression v. Decision Trees vi. Neural Networks vii. Time Series Data Mining viii.Survival Analysis SAS Enterprise Miner
- 5. BASE SAS + •An analytics software pre loaded with functions to perform statistical analysis • Not user friendly, involves coding • Cheaper software as compared to SAS Enterprise Miner but still expensive • SAS+ comprises of BASE SAS, SAS STAT & SAS Access to ODBC
- 6. WPS • An analyticssoftware which reads, understands and executes the language of SAS • Comes loaded with built in functions & procedures to perform statistical analysis • Inspired by BASE SAS • Interface similar to BASE SAS • Cheaper than BASE SAS + • Version 3 (WPS) offers WPS Workbench User Interface to connect & run programs in server, cluster and cloud environment
- 7. IBM’s SPSS • IBM’sSPSS is an equivalent of BASE SAS • Popular Software in Market Research • Can handle small to mid size data sets
- 8. • IBM’s SPSSModeler is an equivalent of SAS Enterprise Miner • SPSS Modeler offers features such as : i. Accessing Data ii. Data Exploration iii. Summarization & Preparation iv. Predictive Modeling Techniques such as regression, clustering, decision trees, neural networks, self organizing maps etc IBM’s SPSS Modeler/Clementine
- 9. R • World’s mostpopular open source analytics tool • Evolved from a language called S, then converted to a product called S+ (GUI based) • R offers more than 3000 packages • R package is a collection of functions which enable: i. Make computations in descriptive statistics ii. Data Manipulation iii. Regression Analysis iv. Advanced Visualization
- 10. • R 3.4.3(Latest Version) • Developed by practitioners themselves • BASE SAS involves coding in R Language, interface is similar to BASE SAS • R language is concise and elegant, uses pre developed packages • Not easy to learn, steep learning curve • Performs super complex statistical analytics quickly R
- 11. • Excellent statistical& visualization capability • Faces problems in handling large data sets • Integration of R with HADOOP can handle large data sets • Adoption of R has increased due to use by Facebook, Google, Bing, Mozilla etc • Graphs can be created with several layers, scales, coordinate systems, smoothing curves R
- 12. Apache HADOOP • Opensource data management software • Helps companies in analyzing massive data volumes (Structured & Unstructured) • Used by Ebay, Yahoo, Facebook • One of the most desired technical skills in the industry
- 13. MICROSTRATEGY • Business Intelligenceproduct with limited analytics capability • Easy to learn tool • Excellent Visualization • Advanced Integration Capability with R & HADOOP
- 14. STATISTICA • Statistics &Analytics software package developed by STATSOFT • Features are data analysis, data management, statistics, data mining, data visualization etc • GUI based product similar to SAS Enterprise Miner • Procedure involves : i. Loading table of data ii. Applying statistical functions from drop down menus • User friendly, Easy to learn • Advanced analytics capabilities (with large data)
- 15. KXEN • KXEN =Knowledge Extraction Engines • Automated Analytics • Reduces work of analysts • Products are based on algorithms developed by Russian Mathematician Vladimir Vapnik • Easy to use, easy to learn, fast & can handle large data sets • Can produce a large number of models quickly • Works like a Black Box
- 16. • KXEN SoftwarePackages offer: i. Data Manipulation ii. Classification iii. Regression iv. Clustering v. Variable Importance vi. Segmentation vii. Time Series viii.Association Rules ix. Data Fusion KXEN
- 17. TABLEAU • GUI baseddata visualization product similar to MICROSTRATEGY, is focused on Business Intelligence • Drag & Drop feature offered to analyze data • Visualizes & creates interactive dash boards • Easy to learn • Gives a good understanding of data • Not capable of Predictive Analytics
- 18. Comparison of AnalyticsTools • Measures used to compare the popularity of Analytics tools are: i. Level of Activity on E-mails or Discussion Lists devoted to these tools ii. Number of Users (Data Analytics Competitions) iii. Languages used in Data Mining or Analysis
- 19. Based on Levelof Activity on E- Mails/Discussion Lists
- 20. • R &SAS Tools are most popular • R has dominated in the last few years • R shows decline in 2011 due to : i. Migration to other forums ii. Emergence of easy to use User Interface in R such as R Commander, Deducer (a GUI for R) & Rattle (a GUI for Data Mining using R) Based on Level of Activity on E- Mails/Discussion Lists
- 21. Based on Numberof Users (Data Analytics Competitions
- 22. • “Kaggle.com” sponsorsdata analysis contests • Companies post Data Analytics Problems with certain prize money • R is the most preferred language in these competitions and even externally • 50% of the contest winners were found to be using R • Other tools often have prohibitions, due to licenses etc, so R is naturally preferred Based on Number of Users (Data Analytics Competitions
- 23. Based on Languagesused in Data Mining or Analytics
- 24. Based on Softwaresused in Data Mining or Analytics
- 25. • R isthe leader in programming languages used followed by Python (2015) • R is the leader in the Softwares’ used followed by Rapid Minder (2015) • The usage of these languages and softwares’ has a direct relationship with the number of jobs which have a programming language as their requirement Based on Languages & Softwares’ used in Data Mining or Analytics
- 26.
Editor's Notes
- #3 Most analytics tools involve coding, and hence it is a trade off between user friendliness and scalability. GUI based analytics products work well with limited data but become unviable for large data. But SAS Enterprise Miner can handle large amounts of data.
- #4 Results coming out of these models still need to be interpreted and insights derived by an analyst who understands the business.
- #5 Results coming out of these models still need to be interpreted and insights derived by an analyst who understands the business.
- #16 Works like a Black Box – if one needs to explain the algorithm or methodology to an analyst or end user, it is unexplainable and secret. It only gives results but doesn’t offer the explanation behind reaching those inferences or conclusions.