If you are looking for free data analysis software download, you may have to take your search well beyond the internet. Before we begin, let me start off by saying that there is no ‘one size fits all’ in data analysis plugins. A good analyst can learn to use even the most expensive tools into creating accurate predictions and insights, while a newbie can mess up even the simplest of them. That being said, you are probably wondering why I am talking about free software data analysis here in my blog? I mean, isn’t this supposed to be about “Gauge Data Analysis”? It is true that I am primarily going to talk about Gauge Data Analysis software for now – however, I want to share my knowledge with others. It’s kind of like charity.
Want to learn about free software data analysis? What free software data analysis is? Are softwares data analysis free? The truth is that you can use a number of different tools to help you with your analysis. You will just need to know where to look. We live in a world where we have access to all sorts of different tools that can help us with our day-to-day tasks.
Excel
Excel still attracts people to do data analysis and yes it is indispensable still as an analytics tool. There are many free online tutorials available that teach about Excel and VBA through which you can master excel. All the features such as exploring data, summarizing data, and visualizing data through various graphical tools are done in excel.
It is very easy to learn and master excel. Excel is still a basic tool in data science and analytics. Knowledge of excel will help you in your data science career. Though Microsoft Excel is not free, there are similar tools like spreadsheets, open offices and may others in the market which provides the same features as excel. One small drawback of excel is that it can’t be used for very large datasets.
Tableau
- Tableau is a free tool for data visualization from simple data to complex data. It is kind of interactive and we can suggest labels, tools, size of the column, and almost anything we can customize. The drag and drop interface is really helpful in this software and calculations can also be done in Tableau. Anyone who doesn’t have any idea of analytics can see and understand data from the Tableau platform.
- Dashboards and worksheets are created in Tableau for data analysis and visualization. Tableau helps see data from a different perspective through its dashboards. One can easily enter into the world of data science through Tableau. Also, Tableau integrates with Python and R programming language.
Trifacta
Trifacta is an open-source tool for data wrangling which makes data preparation easy for data analysis. Trifacta helps to transform, explore and analyze data from raw data format to clean, arranged format. It uses machine learning techniques to help users in data analysis and exploration. The other name of Trifacta is Data Wrangler which makes it clear that it is most useful in data cleaning.
It was developed in 2012 by Joe Hellerstein, Jeffrey Heer, and Sean Kandel. Trifacta works with the cloud and is collaborated with AWS. It has bagged an award for machine learning deployment from AWS. Trifacta helps you to work with large datasets, unlike Excel. Also, text editing suggestions are incredible in Trifacta.
RapidMiner
RapidMiner is an integration tool for data preparation, machine learning, deep learning, and other data analysis techniques. The workflow is called process and the output of one process becomes the input of others. This can be extended via either programming languages or their own plugins. Some versions of RapidMiner are free.
The products of RapidMiner include RapidMiner Studio, RapidMiner Auto Model, RapidMiner Turbo Prep, RapidMiner Server, and RapidMiner Radoop. We can inspect data by loading data into RapidMiner and do calculations or sort the data inside the tool. RapidMiner is mainly designed for non-programmers. RapidMiner also helps in data cleaning and preparing charts.
Orange Data mining
Orange is an open source data visualization and analysis tool. Orange is developed at the Bioinformatics Laboratory at the Faculty of Computer and Information Science, University of Ljubljana, Slovenia, along with open source community. Data mining is done through visual programming or Python scripting. The tool has components for machine learning, add-ons for bioinformatics and text mining and it is packed with features for data analytics. Orange is a Python library. Python scripts can run in a terminal window, integrated environments like PyCharm and PythonWin, or shells like iPython. Orange consists of a canvas interface onto which the user places…
Overview
Features
• Open Source
• Interactive Data Visualization
• Visual Programming
• Supports Hands-on Training and Visual Illustrations
• Add-ons Extend Functionality
What is best?
• Open Source
• Interactive Data Visualization
• Visual Programming
What are the benefits?
•For everyone- beginners and professionals
•Execute simple and complex data analysis
•Create beautiful and interesting graphics
Bottom Line
Orange is an open source data visualization and analysis tool, where data mining is done through visual programming or Python scripting. The tool has components for machine learning, add-ons for bioinformatics and text mining and it is packed with features for data analytics.
Anaconda
Anaconda is an open data science platform powered by Python. The open source version of Anaconda is a high performance distribution of Python and R and includes over 100 of the most popular Python, R and Scala packages for data science. There is also access to over 720 packages that can easily be installed with conda, the package, dependency and environment manager, that is included in Anaconda. Includes the most popular Python, R & Scala packages for stats, data mining, machine learning, deep learning, simulation & optimization, geospatial, text & NLP, graph & network, image analysis. Featured packages include: NumPy,…
Overview
Features
• Analytics Workflows
• Analytics Interaction
• High Performance Distribution
• Data Engineering
• Advanced Analytics
• High Performance Scale Up
• Reproducibility
• Analytics Deployment
What is best?
• Analytics Workflows
• Analytics Interaction
• High Performance Distribution
What are the benefits?
• Accelerate streamline of data science workflow from ingest through deployment
• Connect all data sources to extract the most value from data
• Create, collaborate and share with the entire team
Bottom Line
Anaconda Distribution gives superpowers to people that change the world with high performance, cross-platform Python and R that includes the best innovative data science from open source.
R Software Environment
R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. R is an integrated suite of software facilities for data manipulation, calculation and graphical display. Some of the functionalities include an effective data handling and storage facility, a suite of operators for calculations on arrays, in particular matrices, a large, coherent, integrated collection of intermediate tools for data analysis, graphical facilities for data analysis and display either directly at the computer or on hardcopy, and well developed, simple and effective programming language which includes conditionals,…
Overview
Features
• Open Source – Free Software
• Provides a wide variety of Statistical (linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering) and Graphical Techniques
• Effective data handling and storage facility
• Suite of operators for calculations on arrays, in particular matrices
• Large, coherent, integrated collection of intermediate tools for data analysis
• Graphical facilities for data analysis and display either on-screen or on hardcopy
• Well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities
What is best?
• Open Source – Free Software
• Provides a wide variety of Statistical (linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering) and Graphical Techniques
• Effective data handling and storage facility
What are the benefits?
• Brings analytics to your data
• Runs on a wide variety of platforms- UNIX, Windows, MacOS
• Widely used statistical software
Bottom Line
R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. R is an integrated suite of software facilities for data manipulation, calculation and graphical display.
Scikit-learn
Scikit-learn is an open source machine learning library for the Python programming language.It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. Classification : Identifying to which category an object belongs to Applications: Spam detection, Image recognition. Algorithms: SVM, nearest neighbors, random forest. Regression : Predicting a continuous-valued attribute associated with an object. Applications: Drug response, Stock prices. Algorithms: SVR, ridge regression. Clustering :Automatic grouping of similar objects into sets. Applications: Customer segmentation, Grouping experiment outcomes.…
Bottom Line
Scikit-learn features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.
Weka Data Mining
Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka features include machine learning, data mining, preprocessing, classification, regression, clustering, association rules, attribute selection, experiments, workflow and visualization. Weka is written in Java, developed at the University of Waikato, New Zealand. All of Weka’s techniques are predicated on the assumption that the data is available as a single flat file or relation, where each data point is described by a fixed number of attributes Weka provides access to SQL databases…
Overview
Features
• Data Pre-Processing
• Data Classification
• Data Regression
• Data Clustering
• Data Association rules
• Data Visualization
What is best?
• Data Pre-Processing
• Data Classification
• Data Regression
What are the benefits?
•Portable
•Free to use
•Easy to use
Bottom Line
Weka is a collection of machine learning algorithms for data mining tasks. Weka features include machine learning, data mining, preprocessing, classification, regression, clustering, association rules, attribute selection, experiments, workflow and visualization. Weka is written in Java, developed at the University of Waikato, New Zealand.
datapine
KEY FEATURES:
Visual drag-and-drop interface to build SQL queries automatically, with the option to switch to, advanced (manual) SQL mode
Powerful predictive analytics features, interactive charts and dashboards, and automated reporting
AI-powered alarms that are triggered as soon as an anomaly occurs or a goal is met
datapine is a popular business intelligence software, that is focused on delivering simple, yet powerful analysis features into the hands of beginners and advanced users that need a fast and reliable online data analysis solution for all analysis stages. An intuitive user interface will enable you to simply drag-and-drop your desired values into datapine’s Analyzer and create numerous charts and graphs that can be united into an interactive dashboard. If you’re an experienced analyst, you might want to consider the SQL mode where you can build your own queries or run existing codes or scripts. Another crucial feature is the predictive analytics forecast engine that can analyze data from multiple sources which can be previously integrated with their various data connectors. While there are numerous predictive tools out there, datapine provides simplicity and speed at its finest. By simply defining the input and output of the forecast based on specified data points and desired model quality, a complete chart will unfold together with predictions.
We should also mention robust artificial intelligence that is becoming an invaluable assistant in today’s analysis processes. Neural networks, pattern recognition, and threshold alerts will alarm you as soon as a business anomaly occurs or a previously set goal is met so you don’t have to manually analyze large volumes of data – the data analytics software does it for you. Access your data from any device with an internet connection, and share your findings easily and securely via dashboards or customized reports for anyone that needs quick answers to any type of business question.
Conclusion:
There is a huge demand in the market for data analysis software. Several organizations are using it and making this software one of the most popular in today’s world. Free software data analysis can be used to analyze any kind of data, including qualitative and quantitative information along with social media posts.