Ph.D Softwares and Tools

PYTHON


Python can accomplish most day-to-day research tasks and can be used at multiple steps of the research pipeline. Instead of using different software programs to accomplish different tasks, Python can save researchers a significant amount of time and frustration.

Features:

  • Easy to Code: Python is a very high-level programming language, yet it is effortless to learn.
  • Easy to Read: Python code looks like simple English words.
  • Free and Open-Source.
  • Robust Standard Library.

Pricing:

FREE

Link:

https://www.python.org/downloads/

MATLAB


Research organizations use MATLAB and Simulink to apply deep learning, predictive modeling, and statistical analysis techniques. You and your team can share a common set of products for exchanging work and ideas.

Features:

  • MATLAB is a high-level language: MATLAB Supports Object oriented programming
  • Interactive graphics: MATLAB has inbuilt graphics to enhance user experience.
  • A large library of Mathematical functions: MATLAB has a huge inbuilt library of functions required for mathematical analysis of any data.

Pricing:

PAID

Link:

https://www.mathworks.com/

IBM SPSS


SPSS Statistics is a statistical software suite developed by IBM for data management, advanced analytics, multivariate analysis, business intelligence, and criminal investigation. Long produced by SPSS Inc., it was acquired by IBM in 2009. Current versions (post 2015) have the brand name: IBM SPSS Statistics.

Features:

  • All data in SPSS is stored in SAV format.
  • SPSS helps you unambiguously get data from your mission-critical data.
  • SPSS offers users deep statistical capabilities to analyze accurate results.
  • It helps to get the data management system and editing tools easily.

Pricing:

FREE with minimum functionality

Link:

https://ibm-spss-statistics-base.en.uptodown.com/windows/download

NS2


A network simulator is a software program that can predict the performance of a computer network or a wireless communication network. Since communication networks have become too complex for traditional analytical methods to provide an accurate understanding of system behavior, network simulators are used.

Features:

  • It is a discrete event simulator for networking research.
  • It provides substantial support to simulate bunch of protocols like TCP, FTP, UDP, https and DSR.
  • It simulates wired and wireless network.
  • It is primarily Unix based.

Pricing:

FREE

Link:

https://www.isi.edu/nsnam/ns/

POWER BI


Power BI, from Microsoft, is a suite of business analytics tools that is used to analyze data and share insights in the form of reports and dashboards. User data in various forms – spreadsheets, text files, databases, etc. form the input for Power BI. Datasets are formed by transforming the data provided by the users.

Features:

  • Explore statistical summary.
  • Identify outliers with Power BI visuals.
  • Group and bin data for analysis.
  • Apply clustering techniques.

Pricing:

FREE with Minimum Functionality

Link:

https://powerbi.microsoft.com/

MS EXCEL


Microsoft Excel is a spreadsheet developed by Microsoft for Windows, macOS, Android and iOS. It features calculation or computation capabilities, graphing tools, pivot tables, and a macro programming language called Visual Basic for Applications (VBA). Excel forms part of the Microsoft Office suite of software.

Features:

  • Add Multiple Rows.
  • Absolute References.
  • Print Optimizations.
  • Extend formula across/down.

Pricing:

FREE with Minimum Functionality

Link:

https://www.microsoft.com/en-ww/microsoft-365/excel

R-PROGRAMMING


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.

Features:

  • Basic Statistics: The most common basic statistics terms are the mean, mode, and median. These are all known as Measures of Central Tendency. So using the R language we can measure central tendency very easily.
  • Probability distributions: Probability distributions play a vital role in statistics and by using R we can easily handle various types of probability distribution such as Binomial Distribution, Normal Distribution, Chi-squared Distribution and many more.
  • Data analysis: It provides a large, coherent and integrated collection of tools for data analysis.
  • One of the major features of R is it has a wide availability of libraries. R has CRAN(Comprehensive R Archive Network), which is a repository holding more than 10,0000 packages.

Pricing:

FREE

Link:

https://www.r-project.org/

WEKA


Weka contains a collection of visualization tools and algorithms for data analysis and predictive modeling, together with graphical user interfaces for easy access to these functions. The original non-Java version of WEKA was a TCL/TK front-end to modeling algorithms implemented in other programming languages, and a make file-based system for running machine learning experiments.

Features:

  • Machine Learning
  • Data Mining
  • Preprocessing
  • Classification and Clustering

Pricing:

FREE

Link:

https://waikato.github.io/weka-wiki/downloading_weka/

ANOVA


ANOVA is helpful for testing three or more variables. It is similar to multiple two-sample t-tests. However, it results in fewer type I errors and is appropriate for a range of issues. ANOVA groups differences by comparing the means of each group and includes spreading out the variance into diverse sources

Features:

  • The dependent variable must be a continuous (interval or ratio) level of measurement.
  • The independent variables in anova must be categorical (nominal or ordinal) variables.
  • Anova is also a parametric test and has some assumptions.

Pricing:

FREE with Minimum Functionality

Link:

https://www.xlstat.com/

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