Data Science Training allude to quantitative and qualitative techniques and processes that are used to intensify productivity and business gain. Data is drawn out and categorized to analyze and identify behavioral data, patterns and methods vary according to organizational requirements. It is also known as Data Analysis.
Data Analytics is predominantly conducted in business-to-consumer(B2C) applications. Data is generally classified, stored and analyzed to study patterns and trends. International organizations collect and analyze data correlated with business processes, customers, market economics or practical experience. Data Analytics is Emerging data facilities through decision-making. If we consider an example, a social networking website collects data related to user’s preference, interests of community and it divides according to specified prototype such as demographics, gender or age. Proper data analysis reveals key user and customer trends and expedite the social network’s alignment of content, layout and strategy behind it.
Data Analytics in other way is making sense of Big Data. It is the main domain of Data Analytics. Many tools and techniques are set up in order to collect, transform, cleanse, classify, and convert data into effortlessly understandable data visualization and report formats. Data Analytics follows a series of steps which are 1) Get Data 2) Analyze it 3) Visualize it 4) Publish and 5) Consume.
Data Analytics is one of the most complex term, when it comes to big data applications. The most important attributes of big data include volume, velocity, and variety. The need for Big Data Analytics bounces from all data that is created at extremely fast speeds on the internet. It is predicted that by the end of 2020 the cumulative data that is generated every second will amount to 1.7 MB which is contributed by individuals across the globe. This displays the amount of data being generated and hence need for Big Data Analytics tools to match all that data.
Types of Data Analytics
1)Prescriptive Analytics: It talks about analysis based on the rules and recommendations in order to define a certain analytical way for the organization.
2)Predictive Analytics: It ensures that the path is anticipated for the future course of action.
3)Diagnostic Analytics: It is about looking into the past and mentioning why a certain thing happened. This type of analytics usually whirls around working on a dashboard.
4)Descriptive Analytics: In this type of analytics, we work based on the incoming data and for the mining of it if we expand analytics and come up with an explanation based on the data.
Working with Big Data Analytics
Prescriptive Analytics ensures that it opens light on various aspects of your business and provide you an immense focus on what we need to do. It adds a lot of value to any organization.
Predictive analytics can also make sure that domain of big data can be brought into effective action for predicting the future based on the present data. An example is the deployment of analytical aspects to the sales cycle of company. Machine learning algorithms are coming up with a perfect predictive analysis methodology for any enterprise.
Diagnostic analytics is used for purpose of determining or discovering why a certain course of action happened. For example, we can review a certain social media campaign for coming up with the number of mentions for a post, page views, fans, reviews etc., to analyze why a certain thing happened.
Descriptive analytics is the least popular. It is basically used for coming up with a methodology for discovering patterns that can add value to an organization. credit risk assessment is an example of it.
As no organization today can stay without data analytics, It is clear that data Analytics is an important part of life cycle of data in any organization.
Various tools used in Data Analytics
1)python: It is one of the most versatile programming languages that is briskly being deployed for different types of applications.
2)Apache spark: It is a framework for real-time data analytics which is a part of Hadoop ecosystem.
3)Hadoop: Hadoop is the most popular big data framework that is being deployed by many organizations around the world for making sense of their big data.
4)SAS: It is an advanced analytical tool that is used for working with large amounts of data and deriving valuable insights from it.
5)SQL: It is known as structured query language. It is used for working with RDBMS (relational database management systems).
6)Tableau: it is the most popular Business Intelligence tool that is deployed for aim of data visualization and business analytics.
7)Splunk: It is the tool of choice for parsing machine-generated data and determining valuable data out of it.
8)R Programming: R is the pioneer programming language that is being used by data scientists for the use of statistical computing and graphical applications.
Companies using Data Analytics:
There is a very fast development of various analytic tools and techniques regardless of industry type. The tools are used for parsing of data or visualization tools which are used to make sense of data. The prominent companies using data analytics are Amazon, Microsoft, Facebook, Google. These companies couldn’t survive without the use of data analytics. Amazon uses data analytics in order to recommend you the right product based on the product that you have bought in the past. Data analytics is also used to build customer profiles to serve them better.
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