1.JMP (John’s Macintosh Project) – Statistical Software
JMP is an on-premise data analytics solution that helps scientists, engineers and data explorers understand complex data relationships and visualize them via interactive dashboards. JMP can be automated with its proprietary scripting language, JMP Scripting Language (JSL).
Operating System: Windows, Macintosh
Type: Statistical package, visualization, multivariate analysis, genomics, biomarkers, clinical.
Latest Version: JMP® 16.1
FEATURED INDUSTRIES
- Chemical
- Consumer Products
- Semiconductor
- Pharma & Biotech
APPLICATION AREAS
- Design of Experiments
- Quality Engineering, Reliability and Six Sigma
- Statistics, Predictive Modeling and Data Mining
- Data Visualization and Exploratory Data Analysis
- Consumer and Market Research
- Dashboard Building
2.SAS (Statistical Analysis Software)
It is used for, business intelligence, Predictive Analysis, Descriptive and Prescriptive Analysis, data management etc. SAS is a software suite that can mine, alter, manage and retrieve data from a variety of sources and perform statistical analysis on it. SAS provides a graphical point-and-click user interface for non-technical users and more through the SAS language. A point-and-click interface was added in version 9 in 2004. A social media analytics product was added in 2010.
SAS DLPy- SAS Deep Learning with Python
The DLPy package provides a way to apply deep learning functionalities to solve computer vision, NLP, forecasting and speech processing problems. DLPy allows for reading in and building deep learning models for image, text, audio and time series data. Keras and Caffe modelers will find the DLPy interface familiar. DLPy is an open source package that data scientists can download to apply SAS deep learning algorithms to image, text and audio data. And you don’t need to write SAS code to reap the benefits of deep learning. DLPy is a toolset in a Python-style shell to SAS scripting language and the SAS deep learning actions from SAS® Visual Data Mining and Machine Learning. DLPy is available in SAS Viya 3.4 and accessed via Jupyter Notebook. DLPy is designed to provide an efficient way to apply deep learning functionalities to solve computer vision, natural language processing, forecasting, and speech processing problems. DLPy APIs are created following the Keras APIs with a touch of PyTorch flavour. This library is available on GitHub.
Operating System: Windows, IBM Mainframe, Unix/Linux, OpenVMS Alpha.
Type: Numerical analysis
Latest Version: SAS® 9.4 x86/x64 Latest Version
SAS Applications
- Multivariate Analysis
- Business Intelligence
- Predictive Analytics
- Clinical Research and Forecasting
3.Julia
Julia is a high-level programming language for computational science and numerical analysis. It comes with a large mathematical feature library, a parallel and distributed execution program, a sophisticated compiler, and numerical precision. Julia is intended to overcome the limitations of Python and other computational programming and data processing languages and applications.
Julia’s main advantage over Python when it comes to machine learning is speed. Julia is out-of-the-box and more performant than Python alone. However, with optimized interpreters and various packages, Python can come to compete with Julia. Julia has the distinct advantage of being specifically designed with machine learning and data analytics in mind. In machine learning, speed makes Julia the winner. But Python’s flexibility can make it more useful if you don’t have rigid speed requirements.
Operating System: Linux, macOS, Windows and FreeBSD
4.R-Programming
R is a popular open-source data visualization-driven language that focuses on statistical computing and reigns high in the machine learning environment. R is known for implementing machine learning methodologies like classification, regression, decision tree formation, etc. R programming language is a fantastic choice when it comes to crunching large numbers and is the preferred choice for machine learning applications that use a lot of statistical data. With user-friendly IDE’s like RStudio and various tools to draw graphs and manage libraries – R is a must-have programming language in a machine learning engineer’s toolkit. Here’s what makes R one of the most effective machine learning languages for cracking business problems.
Machine learning engineers need to train algorithms and bring in automation to make accurate predictions. R language provides a variety of tools to train and evaluate machine learning algorithms for predicting future events making machine learning easy and approachable. R has an exhaustive list of packages for machine learning.
Latest Version: R version 4.2.1 (Funny-Looking Kid) has been released on 2022-06-23.
5.OPNET Network Simulator (Optimized Network Engineering Tools)
OPNET Network simulator is a tool to simulate the behaviour and performance of any type of network. The main difference between Opnet Network Simulator compared to other simulators lies in its power and versatility. Opnet Network Simulator is open free software. Opnet Projects simulate the various networks and retrieve the original results. OPNET is a high-level event-based network level simulation tool. It operates on the packet level simulations in all networks. In general, it has a huge library and model to build fixed network hardware and protocols. By using C or C++ coding languages, we can develop networking projects.
Example: In telecommunications, 4G is the fourth generation of cellular wireless standards and sdn project.
Latest Version: Riverbed Modeler (Linux, Windows) Version 18.10.0
6.NetSim
NetSim is an end-to-end, full-stack, packet level network simulator and emulator. It provides network engineers with a technology development environment for protocol modelling, network R&D and military communications. The behaviour and performance of new protocols and devices can be investigated in a virtual network within NetSim at a significantly lower cost and in less time than with hardware prototypes.
Latest Version: NetSim v13.1
Applications:
- IoT-Attack Emulation
- Manpack UHF Radios
- UAV Drone Communications
- MANET Vehicular Radio
- Tactical Data Links
- Utilities Distribution
- Inflight Communication
- Metro Rail
7.QualNet -Digital Twin Technology for Development and Analysis
QualNet® software (QualNet) is a tool for scientists, engineers and network planners to create virtual models of all types of data, voice and video networks that “mimic” the behaviour of a real communications network. QualNet is a testing and simulation tool owned and provided by Scalable Network Technologies, Inc. As network simulation software, it acts as planning, testing, and training tool which mimics the behaviour of a physical communications network. QualNet can easily build comprehensive network models and generate statistics that reflect actual (or projected) performance. QualNet includes three user-friendly design, visualization, and analysis modes, allowing engineers to build models, evaluate network functions, and design new protocols in the same package.
Use cases:
- Autonomous Mining
- Intelligent transportation
- Network simulation models for the virtual world
Applications
- Designing new architecture or operating modes
- Running “what-if” scenarios with a different model, network, and traffic parameters
- Testing new equipment under realistic conditions
- Simulating the effect of a network upgrade
8.IBM PowerAI
IBM PowerAI provides software packages for several Deep Learning frameworks, supporting libraries, and tools. PowerAI makes deep learning, machine learning, and AI more accessible and more performant. By combining this software platform for deep learning with IBM® Power Systems™, enterprises can rapidly deploy a fully optimized and supported platform for machine learning with blazing performance. The PowerAI platform includes the most popular machine learning frameworks and their dependencies, and it is built for easy and rapid deployment. PowerAI requires installation on IBM Power Systems S822LC for HPC server infrastructure. Includes popular machine learning frameworks such as TensorFlow, Caffe, SparkML, etc. Supports multi-node clusters for Distributed Deep Learning (DDL) with Infiniband interconnects.
Use cases
PowerAI has several applications in medicine and biology fields.
- Universities are using PowerAI to increase the speed and accuracy of complex simulations for physics and fluid dynamics.
- Researchers are also using PowerAI frameworks to more accurately model patients’ hearts. This includes mapping unique patterns of blood flow through blood vessels and valves before starting a surgical procedure, which can lead to better patient outcomes.
- Further, PowerAI is helping with cancer cell detection and diabetic drug discovery.
Latest Version: 7.1
9.Jenkins
The leading open source automation server, Jenkins provides hundreds of plugins to support building, deploying and automating any project. Jenkins is a continuous integration tool that enables continuous testing, build, and deployment of iterative code changes.
Operating System: Windows, Linux, Mac OS, Unix.
Latest Version: 2.319. 3 and 2.332
Sources:
https://www.jmp.com/en_in/software/data-analysis-software.html
https://www.sas.com/en_us/home.html
https://www.tetcos.com/netsim-pro.html
https://www.ibm.com/docs/en/cloud-private/3.2.x?topic=paks-powerai