Factor analysis is a common term used for a family of statistical techniques associated with the reduction of a set of observable variables in terms of a small number of latent factors. The main goal of factor analysis is data reduction and summarization.
Sas Programming 2 Data Manipulation Techniques Pdf 17
Additionally, the platform provides flexible deployment options to support multiple scenarios, business sizes and use cases. For example, for supply chain analysis or cybercrime prevention, among many others. Flexible data integration and manipulation is another important feature included in this software. Unstructured and structured data, including text data, from multiple sources, can be analyzed for predictive modeling that will translate into intelligent business outcomes.
Jupyter Notebook is an open source web based interactive development environment used to generate and share documents called notebooks, containing live codes, data visualizations, and text in a simple and streamlined way. Its name is an abbreviation of the core programming languages it supports: Julia, Python, and R and, according to its website, it has a flexible interface that enables users to view, execute and share their code all in the same platform. Notebooks allow analysts, developers, and anyone else to combine code, comments, multimedia, and visualizations in an interactive document that can be easily shared and reworked directly in your web browser.
Even though it works by default on Python, Jupyter Notebook supports over 40 programming languages and it can be used in multiple scenarios. Some of them include sharing notebooks with interactive visualizations, avoiding the static nature of other software, live documentation to explain how specific Python modules or libraries work, or simply sharing code and data files with others. Notebooks can be easily converted into different output formats such as HTML, LaTeX, PDF, and more. This level of versatility has earned the tool 4.7 stars rating on Capterra and 4.5 in G2Crowd.
Next, in our insightful list of data analyst tools we are going to touch on data mining. In short, data mining is an interdisciplinary subfield of computer science that uses a mix of statistics, artificial intelligence and machine learning techniques and platforms to identify hidden trends and patterns in large, complex data sets. To do so, analysts have to perform various tasks including data classification, cluster analysis, association analysis, regression analysis, and predictive analytics using professional data mining software. Businesses rely on these platforms to anticipate future issues and mitigate risks, make informed decisions to plan their future strategies, and identify new opportunities to grow. There are multiple data mining solutions in the market at the moment, most of them relying on automation as a key feature. We will focus on Orange, one of the leading mining software at the moment.
SAS is a powerful and flexible statistical package that runs on many platforms,including Windows and Unix. This class is designed for anyone interested in learninghow to write basic SAS programs. Some familiarity with SAS is recommended. If youare new to SAS you may want to review ourIntroduction to SAS Seminar. It is expected that thoseattending this course have the ability to navigate to and access data files ontheir own operating system. The students in the class will have hands-onexperience using SAS for data manipulation including use of arithmeticoperators, conditional processing, using SAS built-in functions, merging,appending, formatting and different options for modifying SAS output. It is ourhope that after this seminar you will be able to:
The objective of this webinar series is to provide members of the Canadian pediatric cancer community a broad understanding of how to access and utilize the Cancer in Young People in Canada data for research purposes. This series will include an overview of how to (1) develop an appropriate research question, (2) complete the data access process and (3) perform basic data manipulation and analysis using SAS and Microsoft Access.
As shown above, SAS extracts raw data from different sources, cleans the data, and stores or loads it in a database. SAS extracts and categorizes data in tables that help you identify and analyze data patterns. Using this tool will allow you to increase employee productivity and business profits through qualitative techniques and procedures like advanced analytics, multivariate analysis, business intelligence, handling data management functions, or predictive analytics. SAS is driven by SAS programmers, who perform a series of operations on SAS datasets in order to generate reliable statistical data reports for taking business decisions. Non-technical users have access to a graphical interface with point-and-click functionality and more advanced options with SAS language.
R is a programming language for statistical computing and graphics supported by the R Core Team and the R Foundation for Statistical Computing. Created by statisticians Ross Ihaka and Robert Gentleman, R is used among data miners, bioinformaticians and statisticians for data analysis and developing statistical software.[7] Users have created packages to augment the functions of the R language.
According to user surveys and studies of scholarly literature databases, R is one of the most commonly used programming languages in data mining.[8] As of December 2022,[update] R ranks 11th in the TIOBE index, a measure of programming language popularity, in which the language peaked in 8th place in August 2020.[9][10]
R was started by professors Ross Ihaka and Robert Gentleman as a programming language to teach introductory statistics at the University of Auckland.[12] The language took heavy inspiration from the S programming language with most S programs able to run unaltered in R[5] as well as from Scheme's lexical scoping allowing for local variables.[1] The name of the language comes from being an S language successor and the shared first letter of the authors, Ross and Robert.[13] Ihaka and Gentleman first shared binaries of R on the data archive StatLib and the s-news mailing list in August 1993.[14] In June 1995, statistician Martin Mächler convinced Ihaka and Gentleman to make R free and open-source under the GNU General Public License.[14][15] The first official 1.0 version was released on 29 February 2000.[12]
R supports procedural programming with functions and, for some functions, object-oriented programming with generic functions.[31] Due to its S heritage, R has stronger object-oriented programming facilities than most statistical computing languages.[citation needed] Extending it is facilitated by its lexical scoping rules, which are derived from Scheme.[32] R uses S syntax (not to be confused with S-expressions) to represent both data and code .[33] R's extensible object system includes objects for (among others): regression models, time-series and geo-spatial coordinates. Advanced users can write C, C++,[34] Java,[35] .NET[36] or Python code to manipulate R objects directly.[37]
R's capabilities are extended through user-created[40] packages, which offer statistical techniques, graphical devices, import/export, reporting (RMarkdown, knitr, Sweave), etc. These packages and their easy installation and use has been cited as driving the language's widespread adoption in data science.[41][42][43][44][45] The packaging system is also used by researchers to organize research data, code, and report files in a systematic way for sharing and archiving.[46]
In 2007, Richard Schultz, Martin Schultz, Steve Weston, and Kirk Mettler founded Revolution Analytics to provide commercial support for Revolution R, their distribution of R, which includes components developed by the company. Major additional components include ParallelR, the R Productivity Environment IDE, RevoScaleR (for big data analysis), RevoDeployR, web services framework, and the ability for reading and writing data in the SAS file format.[107] Revolution Analytics offers an R distribution designed to comply with established IQ/OQ/PQ criteria that enables clients in the pharmaceutical sector to validate their installation of REvolution R.[108] In 2015, Microsoft Corporation acquired Revolution Analytics[109] and integrated the R programming language into SQL Server, Power BI, Azure SQL Managed Instance, Azure Cortana Intelligence, Microsoft ML Server and Visual Studio 2017.[110] 2ff7e9595c
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