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Tag: Data Analytics Tools

The Next Big Thing in Data Analytics

Posted on by Imarticus Learning

 

Data analytics is fast evolving, and with the increasing use of streaming data, machine data and big data only adds to the continuous challenges encountered during analyzing log data, enterprise application data, web information, historical data stored in documents and reports etc.

In the present day, data analyst struggle to provide a solution for business and client request. As it is, there is a substantial deficient of talent in the field of business data analysts and data scientist, with businesses continue to struggle with data reconciliation, data blending, data access, development of data analytics tools and data mining techniques.

Data analyst and data scientist are frequently unable to discover data and information required and are often unaware of the latest data analytics tools such as the self-service data prep tools assist in the improvement of productivity. Furthermore, the continuous development of advanced social technologies and with the incorporation of various social features have caused an increased expectation regarding timeliness and information availability. Similarly, users have similar enhanced expectations towards business information irrespective of where the data originates or how is it formatted. There is an increasing demand for instant access for data and the ease of sharing it with essential stakeholders.

 

Data socialization is the metamorphosis of data mining techniques to enhance data accessibility across companies, teams, and individuals. Data socialization is changing how business think about business data and how employees interface with business data.

Data socialization comprise of management of data platform which enables the linkage between self-service visual data preparation, automation, cataloging, data discovery and governance features with essential features common to a various social media platform. Hereby, it provides businesses with the ability to leverage social media metrics such as user ratings, discussions, recommendations, comments etc. to enable usage of data for improved decision making.

What is Data Socialisation?

It is a data analytic tool which enables business analyst, data scientist and various relevant users throughout an organization to search, reuse, and share managed data. It aids in the achievement of agility and enterprise collaboration. Data socialization allows employees to find and utilize data which is accessible to them within a specified data ecosystem and assist in the creation of a social network of raw data sets which are curated and certified. These data ecosystems have various levels of controls, restrictions, and limitations which can be well defined for each individual person in an organization. These data mining techniques aid the strengthening an environment of data access, wherein analyst and users are allowed to learn from one another, enhance productivity and be well-connected as its sources, cleans and prepares of data analytics.

Some Characteristics of Data Socialisation

Some of the critical characteristics of data socialization include:

  • The ability of understanding data with regards to its relevance about how a particular data is deemed to be used by various users within an enterprise.
  • Involvement of collaboration of essential users with the data set to harness knowledge which often remains unshared.
  • It enables enterprise users to search for data which has been cataloged, prepare data models, and index metadata by users, type, application, and various unique parameters.
  • Data Socialisation enables to perform a data quality score, suggest for relevant data sources, automatically recommend actions for preparing actions designed according to user persona.

With various business applications incorporating features of social media functions towards improvement in business collaboration, at this moment making individuals and companies well informed, productive and agile.

Data socialization aids in delivering various benefits to various data analytics tools and removal of obstacles towards accessing and sharing data, at this moment allowing data scientist, business users and business information analyst in improving their productivity and decision-making. It further empowers analyst, data scientist and other business users across various departments to collaborate using the available data. By providing the right person with the correct data required to make informed, educated and timely decisions, the implementation of Data socialization is deemed to be the next big thing in data analytics.

Join Big Data Analytics Course from Imarticus Learning to start your career in data analytics. 

5 Simple Facts About Big Data Analytics Courses – Explained

Posted on by Imarticus Learning

Data Science, Machine Learning or the Big Data Analytics Courses whatever one might refer it as, the subject matter has witnessed colossal growth over the last two decades due to the increase in collection of data, improvement in data collection techniques and methods, and a substantial enhancement in the power of computing data. Various data analyst jobs are pooling talent from multiple branches of engineering, computer scientist, statisticians and mathematicians and is increasingly demanding an all-around solution for numerous problems faced by the businesses in managing their data.
As a matter of fact, not a single stream of business, engineering, science etc. has remained far from the reach of data analytics and are employing various data analysis tools on an on-going basis within their respective industries. Perhaps it can be one of the best times for students to enroll in the big data analytics courses and be future ready as the future is in data analytics.
But, as data analytic jobs are deemed to be in an upward trend shortly, here are some simple facts one needs to know about data analytics before embarking a big data analytics course or a career in data analytics. 

  1. No Data is Ever Clean

Theoretically, as taught during a  data analytics course,  analytics in the absence of data is just a group of theories and hypothesis, whereas data aids to test these theories and hypothesis towards finding a suitable context. But, when it comes to the real world, data is never clean and is always in a pile of mess. Organisations with established data science centres to say that their data is not clean. One of the major issues organisations face apart from missing data entries, or incorrect entries is combining multiple datasets into a single logical unit.
The various datasets might face many problems which prevent its integration. Most data storage businesses are designed to be well integrated with the front-end software and the user who generates the data. However, many-a-times, data is created independently, and the data scientist arrives at the scene at a later stage and often ends up being merely a “taker” of data which is not a part of the data design.

  1. Data Science is not entirely The user will need to clean some data manually

A vast majority of people do not wholly understand what data analytics is? One of the most common misconceptions about data analytics is that the various data analysis tools thoroughly clean the data. Whereas, in reality, as the data is not always clean, it requires a certain degree of manual processing to make it usable, which requires intense amount of data processing, which can be very labour intensive and time-consuming, and the fact remains that no data analysis tools can completely clean the data at the push of a button.
Each type of data poses its own unique problem, and data analyst jobs involve getting their hands dirty and manually processing data to test models, validate it against domain experts and business sense etc.

  1. Big Data is merely a tool

There is quite a lot of hype around the Big Data, but many people do not realize that it is only a collection of data analysis tools which aids working with a massive volume of data promptly. Even while using Big Data, one requires the utilise best data modelling practices and requires a trained eye of an expert analyst.

  1. Nobody cares how you did something

Executives and decision making are often the consumers of various models of data science and continuously require a useful and a workable model. While a person performing one of many data analyst jobs might be tempted to provide an explanation to how data was derived, in reality, these executives and decision makers care less how the data was acquired, and are more interested in its authenticity and how can it be used to improve any of their business functions.

  1. Presentation is Everything

As most of the consumers of analytic solutions are not mathematicians and are experts in their respective fields, presentation plays a vital role in explaining your findings, in a non-technical manner, which is understandable to the end user. A PowerPoint presentation loaded with infographics can aid a data scientist in conveying the end-user their message in a language and mode of communication with is easy of them to understand.

Basics About Topic Modelling As A Data Analytics Technique

Posted on by Imarticus Learning

The Data Science industry has brought about various new avenues into the world of business and internet of things. Here, data analytics as a field, basically deals with extracting ‘information’ from all the obtained data. With rapid digitalization and increasing of the boundaries of the virtual world, the generation and availability of data is on an all-time high. While some of this data might be pre-processed and structured, most of it is just not structured at all. This causes a lot of difficulties when it comes to the part, where relevant and important information is required. That’s where the tools and technologies of the data analytics industry come into play. These are powerful methods, developed by technology and can be used for sifting through the volumes of data and sniffing out, exactly what a professional is looking for. One of the subsets of these technology is the field of text mining, which basically deals with the technique known as Topic Modelling.
This process mainly deals with, identifying topics present in a text object and deriving hidden patterns automatically, thus aiding in the betterment of decision making. This process differs from other run of the mill text mining approaches, which basically deal with regular search techniques or keywords searching techniques based on any random dictionary. A specific bunch of words that is supposed to be found and observed by a professional, is known as “topics”, which usually are present in large clusters of texts. Topic modelling is the unsupervised approach to performing the above mentioned action, with only the machine and no manual help.
Data Science CourseTopics in other words are, “a pattern of co-occuring terms in a corpus, which keeps repeating itself”. For instance

while building a topic model for healthcare, it should be devised in such a way that it results in words like, health, doctor, patient, hospital and other related words. These topic models are very useful when it comes to processes such as, document clustering, organizing large blocks of textual data, feature selection and retrieval of information from unstructured text and so on. What makes this technique so very important is that it can be used in almost any field from print media to marketing and still be relevant and product centric. For example, there are top gun newspaper publishing houses like, The New York Times, who have a team working on perfecting topic models so as to boost their article recommendations for users. There are a lot of advanced HR teams dabbling in this sector by trying to use it to match perfect candidates, with perfect job profiles
These text models are also used in various other applications such as organization of large datasets of emails, customer reviews and user social media profiles. These are some of the reasons why professionals specializing in this technique are gradually becoming sought after. As the demand of companies rises, the amount of people opting to get trained in these techniques also goes up. Imarticus Learning has various industry intensive course offerings for various data analytics tools like Python, which uses this topic modeling technique most extensively.