Best Analytics tools,Business analytics, Career in analytics, Big data,

Data analytics is the use of advanced analytic techniques against very large, diverse data sets that include different types such as structured/unstructured and streaming/batch and different sizes from terabytes to zettabytes

Analytics is a fast growing field that promises exciting careers for those with strong quantitative skills. In his bestselling book ‘Competing on analytics’, Thomas Davenport defines analytics as “the extensive use of data, statistical and quantitative analysis, exploratory and predictive models, and fact-based management to drive decisions and actions.” Analytics can range from a simple exploration into how many sales were made last year to a complex neural network model predicting which customers to target for this year’s marketing campaign.

Analytics is used in a variety of industries. Industries like financial services, retail and travel are naturally amenable to analytics as they generate a lot of transactions and hence a lot of data. No wonder these industries were among the leaders in adopting analytics. However, less obvious industries like manufacturing and sports are also making innovative use of analytic applications to drive competitive advantage.

Commercial software:-

MS Excel: Almost every business user has access to MS Office suite and Excel. Excel is an excellent reporting and dash boarding tool. For most business projects, even if you run the heavy statistical analysis on different software but you will still end up using Excel for the reporting and presentation of results.

While most people are aware of its excellent reporting and graphing abilities, excel can be a powerful analytic tool in the hands of an experienced user. Latest versions of Excel can handle tables with up to 1 million rows making it a powerful yet versatile tool.

SAS: SAS is the 5000 pound gorilla of the analytics world and claims to be the largest independent vendor in the business intelligence market. It is the most commonly used software in the Indian analytics market despite its monopolistic pricing. SAS software has wide ranging capabilities from data management to advanced analytics.

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R: R is a programming language and software environment for statistical computing and graphics. The R language is an open source tool and is widely used by the academia. For business users, the programming language does represent a hurdle. However, there are many GUIs available that can sit on R and enhance its user-friendliness.

SPSS Modeler: (Clementine): SPSS Modeler is a data mining software tool by SPSS Inc., an IBM company. It was originally named SPSS Clementine. This tool has an intuitive GUI and its point-and-click modelling capabilities are very comprehensive.

Statistica: is a statistics and analytics software package developed by StatSoft. It provides data analysis, data management, data mining, and data visualization procedures. Statistica supports a wide variety of analytic techniques and is capable of meeting most needs of the business users. The GUI is not the most user-friendly and it may take a little more time to learn than some tools but it is a competitively priced product that is value for money.

Salford systems: provides a host of predictive analytics and data mining tools for businesses. The company specialises in classification and regression tree algorithms. Its MARS algorithm was originally developed by world-renowned Stanford statistician and physicist, Jerome Friedman. The software is easy to use and learn.

KXEN: is one of the few companies that is driving automated analytics. Their products, largely based on algorithms developed by the Russian mathematician Vladimir Vapnik, are easy to use, fast and can work with large amounts of data. Some users may not like the fact that KXEN works like a ‘black box’ and in most cases, it is difficult to understand and explain the results.

Angoss: Like Salford systems, Angoss has developed its products around classification and regression decision tree algorithms. The advantage of this is that the tools are easy to learn and use, and the results easy to understand and explain. The GUI is very user friendly and a lot of features have been added over the years to make this a powerful tool.

MATLAB: is a statistical computing software developed by MathWorks, MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms and creation of user interfaces. There are many add-on toolboxes that extend MATLAB to specific areas of functionality, such as statistics, finance, image processing, bioinformatics, etc. Matlab is not a free software. However, there are clones like Octave and Scilab which are free and have similar functionality.

Weka: Weka (Waikato Environment for Knowledge Analysis) is a popular suite of machine learning software, developed at the University of Waikato, New Zealand. Weka, along with R, is amongst the most popular open source software used by the business community. The software is written in the Java language and contains a GUI for interacting with data files and producing visual results and graphs.

Organizations use a variety of products to extract insights from the data. The complete spectrum of the products ranges from simple tools like excel to number crunching mammoths like SAS or R.

Let us take a look at some other popular data analytics tools today, not mentioned in the lists above.

SQL: It is the primary data storage tool across all organizations. It is still being used to manipulate data and produce reports. Anyone who wishes to be in the analytics industry needs to know SQL.

  Tableau: Tableau as a tool has been adopted by most analytics companies. Its visualization capabilities are well recognized in the industry. It is used mostly to produce visualizations and reports. It also allows users to explore the data before beginning the task of predictive modelling.

Orange: It uses the concept of visual programming and simplifies the task of predictive modelling. All major predictive algorithms are built into the software. One can also add functionalities by writing scripts in python. The fact that it is open source makes it all the more attractive.

KNIME: The Company behind KNIME provides a suite of products catering to different needs. It also like orange makes use of visual programming.

Azure ML: This is one product from Microsoft that is poised to be the next big thing. This product is actually an ecosystem that gives users the ability to create data products by integrating machine learning module (mostly R libraries) with a robust backend and a pretty frontend. The data connections can be made from different sources, SQL servers, Hadoop clusters.

Career in analytics.

http://www.analyticsvidhya.com/blog/2014/05/planning-late-career-shift-analytics-big-data-prepared/