Mission Statement Research Metrics and Analytics (RMA) aims to provide an open and stimulating forum for the study of the advancement, dissemination, and assessment of scholarly knowledge in science, technology, medicine, humanities and social sciences. The specialty journal publishes high-quality qualitative and quantitative studies of academic work at various levels of granularity. The scope of the specialty journal covers the development, applications, and evaluation of scholarly metrics, including but not limited to, bibliometric, scientometric, informetric, and altmetric studies. In addition, the specialty journal publishes fundamental research of academic knowledge and relevant analytic and evaluative techniques. Topics of interest: - Altmetrics - Bibliometrics - Case studies - Citation analysis - Informetrics - Machine learning - Methodologies - Patent analysis - Qualitative studies of scholarly knowledge - Research assessment and evaluation - Research policy - Reviews and surveys of the state of the art - Scholarly communication - Scholarly discourse and metadiscourse - Science and technology indicators - Science mapping - Science studies - Scientometrics - Technometrics - Text Mining - Visual Analytics - Webometrics Specialty Chief Editor/s: Chaomei Chen , Drexel University, USA Submission Frontiers in Research Metrics and Analytics welcomes submissions of the following article types : Book Review, Code, Conceptual Analysis, Correction, Data Report, Editorial, Empirical Study, General Commentary, Hypothesis Theory, Methods, Mini Review, Opinion, Original Research, Perspective, Protocols, Review, Specialty Grand Challenge and Technology Report. All manuscripts must be submitted directly to Frontiers in Research Metrics and Analytics, where they are peer-reviewed by the Associate and Review Editors of the specialty journal. Articles published in Frontiers in Research Metrics and Analytics will benefit from the Frontiers impact and tiering system after online publication. Authors of published original research with the highest impact, as judged democratically by the readers, will be invited by the Chief Editor to write a Frontiers Focused Review - a tier-climbing article. This is referred to as democratic tiering . The author selection is based on article impact analytics of original research published in the Frontiers specialty journals and sections. Focused Reviews are centered on the original discovery, place it into a broader context, and aim to address the wider community of the field. From: http://journal.frontiersin.org/journal/research-metrics-and-analytics#about
Top 10 Data Analytics Tools Sakshi Behl - Dec 27th, 2016 - Data Analysis - 0 Comments - big data analytics tools , data analysis tools in excel , data analytics tools , statistical tools for data analysis Share the post Top 10 Data Analytics Tools Total: 11 The organizations today, are treating data as an asset and therefore, the data analytics tools are going to be the next big thing. Soon! Therefore, it is important to know what is data analytics and which tool will fit you the best. The revenue from the sales of data and business analytics services will increase more than 50% to $187 billion by 2019. Why Data Analysis? Companies that are not leveraging data analytic tools and techniques are falling apart. Since data analytics tools capture in products that automatically collect, clean, and analyze data, delivering information and predictions, you can improve prediction accuracy and refine the models. Goals of performing Data Analysis You can analyze data. Extract actionable and commercially relevant information to boost performance. There are a number of extraordinary analytical tools that are free and open source so that you can leverage it to enhance your business and develop skills. Top Data Analytics Tools Here is the list of top Data Analytics tools that are available for free (for personal use), easy to use (no coding and precisely designed), well-documented (you can Google your way through if you get stuck), and have powerful capabilities (more than excel). These tools used for data analysis will help you manage and interpret data in a better and more effective way: #1 Tableau Public What is Tableau Public It is one of the intuitive and simple to use tool which democratizes visualization. This data analytics tool communicates insights through data visualization. Although there are great alternatives to data visualization, Tableau Public’s million row limit acts as a great playground for personal use. With Tableau’s visuals, you can quickly investigate a hypothesis, explore the data, and check your intuitions. Uses of Tableau Public This free service lets you publish interactive data visualizations to the web. There is no programming skills required. Visualizations published to Tableau Public can be embedded into blogs and web pages. They can also be shared through email or social media. In fact, they can be made available to users for via download options. Limitations of Tableau Public All your data is public. There is a limitation on data size on an id but luckily, the limit is quite high so there’s not much to worry about. You cannot connect with R. The only way to read is via OData sources, Excel or txt. #2 OpenRefine What is OpenRefine Formerly known as GoogleRefine, this is a data cleaning software that helps you get everything ready for analysis. It operates on a row of data which have cells under columns, which is very similar to relational database tables. This is one of the Data Analytics tools for business. Uses of OpenRefine Cleaning messy data Transformation of data Parsing data from websites Adding data to data set by fetching it from web services. For instance, OpenRefine could be used for geocoding addresses to geographic coordinates. Limitations of OpenRefine Open Refine is not suitable for large datasets. Refine does not work very well with big data. #3 KNIME What is KNIME? One of the best data analytics tools that allow you to manipulate, analyze, and modeling data in an intuitive way via visual programming. KNIME is used to integrate various components for data mining and machine learning via its modular data pipelining concept. Uses of KNIME Rather than writing blocks of code, you just have to drop and drag connection points between activities. This data analysis tool supports programming languages. In fact, analysis tools like these can be extended to run chemistry data, text mining, python, and R. Limitation of KNIME One of the areas where this tool lacks is the poor data visualization. #4 RapidMiner What is RapidMiner? One of the top data analytics tools is RapidMiner provides machine learning procedures and data mining including data visualization, processing, statistical modeling, deployment, evaluation, and predictive analytics. RapidMiner is also considered top in the list of Big data analytics tools. This software is written in the Java programming language. Uses of RapidMiner It provides an integrated environment for business analytics, predictive analysis, text mining, data mining, and machine learning. Along with commercial and business applications, it is also used for application development, rapid prototyping, training, education, and research. Limitations of RapidMiner With RapidMiner, there are size constraints with respect to the number of rows. For using RapidMiner, you require much more hardware resources than ODM and SAS for the same task and data. #5 Google Fusion Tables What is Google Fusion Tables? When talking about Data Analytics tools for free, here comes a much cooler, larger, and nerdier version of Google Spreadsheets. An incredible tool for data analysis, mapping, and large dataset visualization, Google Fusion Tables which could be added to business analytics tools list. Uses of Google Fusion Tables Visualize bigger table data online: You can filter and summarize across hundreds of thousands of rows. Combine tables with other data on web: You can merge two or three tables to generate a single visualization that includes sets of data. With the help of Google Fusion Tables, you can find public data to combine with your own for a better visualization. You can make a map in minutes! Limitations of Google Fusion Tables Only the first 100,000 rows of data in a table are included in query results or mapped. The total size of the data sent in one API call cannot be more than 1MB. #6 NodeXL What is NodeXL? It is a visualization and analysis software of relationships and networks. NodeXL provides exact calculations. It is a free (not the pro one) and open-source network analysis and visualization software. NodeXL is one of the best statistical tools for data analysis which includes advanced network metrics, access to social media network data importers, and automation. Uses of NodeXL This is one of the data analysis tools in excel that helps in following areas: Data Import Graph visualization Graph Analysis Data Representation This software integrates into Microsoft Excel 2007, 2010, 2013, and 2016. It opens as a workbook with a variety of worksheets containing the elements of a graph structure like nodes and edges. This software can import various graph formats like adjacency matrices, Pajek .net, UCINet .dl, GraphML, and edge lists. Limitations of NodeXL You need to use multiple seeding terms for a particular problem. Running the data extractions at slightly different times. #7 Wolfram Alpha What is Wolfram Alpha? It is a computational knowledge engine or answering engine founded by Stephen Wolfram. With this online data analysis tool, you can get answers to factual queries directly by computing the answer from externally sourced ‘curated data’ instead of providing a list of documents or web pages. Uses of Wolfram Alpha It helps to power Apple’s Siri. It provides detailed responses to technical searches and makes quick work of calculus homework. It helps business users in a way by providing information charts and graphs, and is excellent for topic overviews, commodity information, and high-level pricing history. Limitations of Wolfram Alpha Wolfram Alpha can only know things are somehow public and known. That is, it can only deal with number and facts, not with viewpoints. It limits the computation time for each query. #8 Google Search Operators What is Google Search Operators? It is an undeniably powerful resource which helps you instantly filter Google results to get most relevant and useful information. You can use other punctuations to get more specific search results except a few like +, $, #, etc. Uses of Google Search Operators It allows you to quickly filter Google results. Google’s powerful data analysis tool can help discover new information or market research. #9 Solver What is Excel Solver? The Solver Add-in is a Microsoft Office Excel add-in program that is available when you install Microsoft Excel or Office. It is a linear programming and optimization tool in excel. This is one of the best data analysis tools in excel that allows you to set constraints. It is an advanced optimization tool that helps in quick work of a wide range of problems. Uses of Solver The final values found by Solver are a solution to interrelation and decision. It uses a variety of methods, from nonlinear optimization and linear programming to evolutionary and genetic algorithms, to find solutions. Limitations of Solver Poor scaling is one of the areas where Excel Solver lacks. It can affect solution time and quality. Solver affects the intrinsic solvability of your model. #10 Dataiku DSS What is Dataiku DSS? This is a collaborative data science software platform that helps team build, prototype, explore, and deliver their own data products more efficiently. Uses of Dataiku DSS It provides an interactive visual interface where they can build, click, and point or use languages like SQL. This data analytics tool lets you draft data preparation and modelisation in seconds. Helps you coordinate development and operations by handling workflow automation, creating predictive web services, model health on a daily basis, and monitoring data. Limitation of Dataiku DSS Limited visualization capabilities UI hurdles: Reloading of code/datasets Inability to easily compile entire code into a single document/notebook Still need to integrate with SPARK 5 Data Analytics Tools and Techniques You Must Know Here are some of the useful analytics techniques and business analysis tools that can be used for performing better: 1. Visual Analytics There are different ways to analyze the data. One of the simplest ways to do is to create a graph or visual and look at it to spot patterns. This is an integrated method that combines data analysis with human interaction and data visualization. 2. Business Experiments Experimental design, AB testing, and business experiments are all techniques for testing the validity of something. It is trying out something in one part of the organization and comparing it with another. 3. Regression Analysis It is a statistical tool for investigating the relationship between variables. For instance, the cause and effect relationship between product demand and price. 4. Correlation Analysis A statistical technique that allows you to determine whether there is a relationship between two separate variables and how strong that relationship may be. It is best to use when you know or suspect that there is a relationship between two variables and wish to test the assumption. 5. Time Series Analysis It is the data that is collected at uniformly spaced time intervals. You can use it when you want to assess changes over time or predict future events on the basis of what happened in the past. 摘自：http://www.digitalvidya.com/blog/data-analytics-tools/
序言部分 Social networks have been studied fairly extensively over two decades in the general context of analyzing interactions between people, and determining the important structural patterns in such interactions. The trends in recent years have focussed on online social networks, in which the social network is enabled as an internet application. Some examples of such networks areFacebook, LinkedIn and MySpace. Such social networks have rapidly grown in popularity, because they are no longer constrained by the geographical limitations of a conventional social network in which interactions are de ned in more conventional way such as face-to-face meetings, or personal friendships. The infrastructure which is built around social networks can support a rich variety of data analytic applications such as search, text analysis, image analysis, and sensor applications. Furthermore, the analysis and evolution of the structure of the social network is also an interesting problem in of itself. While some of these problems are also encountered in the more conventional notion of social networks, many of the problems which relate to the data-analytic aspects of social networks are relevant only in the context of online social networks. Furthermore, online social networks allow for more efficient data collection on a large scale, and therefore, the computational challenges are far more significant. A number of books have been written in recent years on the topic of social networks, though most of these books focus on the non-technological aspect, and consider social networks more generally in the context of relationships between individuals. Therefore, these books mostly focus on the social, structural, and cognitive aspects of the social network, and do not focus on theunique issues which arise in the context of the interplay between the structural and data-centric aspects of the network. For example, an online social network may contain various kinds of contents or media such as text, images, blogs or web pages. The ability to mine these rich sources of information in the context of a social network provides an unprecedented challenge and also anopportunity to determine useful and actionable information in a wide variety of fields such as marketing, social sciences, and defense. The volume of the data available is also a challenge in many cases because of storage and ef ciency constraints. This book provides a rst comprehensive compendium on recent research on the data-centric aspect of social networks. Research in the field of online social networks has seen a revival in the last ten years. The research in the field is now reaching a level of maturity where it is useful to create an organized set of chapters which describe the recent advancements in this field. This book contains a set of survey chapters on the different data analytic issues in online social networks. The chaptersdescribe the different facets of the field in a comprehensive way. This creates an organized description of the significant body of research in the important and emerging field of online social networks.
标签: Analytics
