business analytics & Data Science

business analytics & Data Science

Analytics is the discovery, interpretation, and communication of meaningful patterns in order to draw conclusions especially valuable in areas rich with recorded information, analytics relies on the simultaneous increasingly with the aid of specialized systems and software. Data analytics technologies and techniques are widely used in commercial industries to enable organizations to make more-informed business decisions.

Trend Analysis:  

Trend analysis is a technique used in technical analysis that attempts to predict the movements based on recently observed data.  Trend analysis is based on the idea that what has happened in the past gives an idea of what will happen in the future.




Data Quality and Data Management:

The quality of data is highly important for organization to use it for business decision.  Data quality management is a set of practices that aim at maintaining a high quality of information.  DQM goes all the way from the acquisition of data and the implementation of advanced data processes, to an effective distribution of data. It also requires a managerial oversight of the information you have. Effective DQM is recognized as essential to any consistent data analysis, as the quality of data is crucial to derive actionable and   more importantly accurate insights from your information.

Data mining:  Data mining is process of extractionwarehousinganalysis, and statistics. Data mining technology helps you examine large amounts of data to discover patterns in the data – and this information can be used for further analysis to help answer complex business questions. With data mining software, you can sift through all the chaotic and repetitive noise in data, pinpoint what's relevant, use that information to assess likely outcomes, and then accelerate the pace of making informed decisions.



Predictive analytics:  Predictive analytics technology uses data, statistical algorithms and machine-learning techniques to identify the likelihood of future outcomes based on historical data. It's all about providing a best assessment on what will happen in the future, so organizations can feel more confident that they're making the best possible business decision. Some of the most common applications of predictive analytics include fraud detection, risk, operations and marketing.

Text mining:  With text mining technology, you can analyze text data from the web, comment fields, books and other text-based sources to uncover insights you hadn't noticed before. Text mining uses machine learning or natural language processing technology to comb through documents – emails, blogs, Twitter feeds, surveys, competitive intelligence and more – to help you analyze large amounts of information and discover new topics and term relationships.

BigData  and  Hadoop:   To Analysis large data sets now days Hadoop open source software framework can store large amounts of data and run applications on clusters of commodity hardware.