I then begin the data analysis script with

was created by Guido Van Rossem in 1991 and emphasizes productivity and code readability. Programmers that want to delve into data analysis or apply statistical techniques are some of the main users of Python for statistical purposes.

Now we are ready to start learning data analysis with R : )

6) Do you have access to number of data analytic softwares for doing your assignment?

R-ientation: An introduction to data analysis using R

Python is generally used when integration between data analysis tasks and web applications. They are also used when statistics code needs to be incorporated into a production database. R is mainly used when the data analysis tasks require standalone computing or analysis on individual servers.

Exploratory Data Analysis Using R - Udacity

A cautious data analyst pursues the question at hand with the appropriate type of analysis and will avoid going further than the available data allows.

I have found version control for solo data analysis projects to be very useful.
RDQA is a package for Qualitative Data Analysis, a free (free as freedom) qualitative analysis software application ( license). It works on Windows, Linux/ and the Mac OSX platforms. RQDA is an easy to use tool to assist in the analysis of textual data. At the moment it only supports plain text formatted data. All the information is stored in a database via the R package of . The GUI is based on , via the aid of . It includes a number of standard Computer-Aided Qualitative Data Analysis features. In addition it seamlessly integrates with R, which means that a) statistical analysis on the coding is possible, and b) functions for data manipulation and analysis can be easily extended by writing R functions. To some extent, RQDA and R make an integrated platform for both quantitative and qualitative data analysis.Starting with the basics of R and statistical reasoning, Data Analysis with R dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples.I’ve been writing software to help others do data analysis for a number of years and at the same time trying to work up my nerve to try my own analysis. Why let other people have all the fun? So, when I saw that , biostatistician at Johns Hopkins and coauthor of , was teaching an , I signed up. Data analysis has recently emerged as a very important focus for a huge range of organizations and businesses. makes detailed data analysis easier, making advanced data exploration and insight accessible to anyone interested in learning it.This book empowers you by showing you ways to use to generate professional analysis reports. It provides examples for various important analysis and machine-learning tasks that you can try out with associated and readily available data. The book also teaches you to quickly adapt the example code for your own needs and save yourself the time needed to construct code from scratch.Viswa Viswanathan is an associate professor of Computing and Decision Sciences at the Stillman School of Business in Seton Hall University. After completing his PhD in artificial intelligence, Viswa spent a decade in academia and then switched to a leadership position in the software industry for a decade.Shanthi Viswanathan is an experienced technologist who has delivered technology management and enterprise architecture consulting to many enterprise customers. She has worked for Infosys Technologies, Corporation, and Accenture. As a consultant, Shanthi has helped several large organizations, such as Canon, Cisco, Celgene, Amway, Time Warner Cable, and GE among others, in areas such as data architecture and analytics, master data management, service-oriented architecture, business process management, and modeling.
I've blogged about my experience  from the perspective of data analysis projects.

Exploratory Data Analysis with R - Matthew Renze

R is very often used in banks and other institutions to build predictive models, analyse data, test statistical hypotheses or automate data analysis workflows. Even is R is not the main tool in an institution then it is an excellent complement of it.

- C. Lauro, V. Esposito: Non-Symmetrical Data Analysis Approaches: Recent Developments and Perspectives

Functional Data Analysis with R and MATLAB (Use R!) [James O

3 What is a good strategy for getting started with version control for data analysis with R (e.g., examples, workflow ideas, software, links to guides)?

Learn how to tackle data analysis problems using the powerful open source language R

Applied Spatial Data Analysis with R

A tacit assumption of investigators is that they have received training sufficient to demonstrate a high standard of research practice. Unintentional ‘scientific misconduct' is likely the result of poor instruction and follow-up. A number of studies suggest this may be the case more often than believed (Nowak, 1994; Silverman, Manson, 2003). For example, Sica found that adequate training of physicians in medical schools in the proper design, implementation and evaluation of clinical trials is “abysmally small” (Sica, cited in Nowak, 1994). Indeed, a single course in biostatistics is the most that is usually offered (Christopher Williams, cited in Nowak, 1994). A common practice of investigators is to defer the selection of analytic procedure to a research team ‘statistician’. Ideally, investigators should have substantially more than a basic understanding of the rationale for selecting one method of analysis over another. This can allow investigators to better supervise staff who conduct the data analyses process and make informed decisions