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Data Analytics

Data analytics (DA) comprises the process of examining data sets in order to convert any data into a meaningful information. Data analytics technologies and techniques are widely used in different sectors in order to analyse all the possibilities required for their growth. Scientists and researchers use different analytics tools to verify or disprove scientific models, theories and hypotheses.

As a term, data analytics mostly refers to collection of applications, from basic BUSINESS INTELLIGENCE (BI), reporting and ONLINE ANALYTICAL PROCESSING (OLAP) to various forms of advanced analytics. Data analytics initiatives can help businesses increase revenue, improve operational efficiency, optimize marketing campaigns and strengthen customer service efforts. Analytics also enable organizations to respond quickly to emerging market trends and gain a competitive edge over business competitors. The ultimate goal of data analytics is to boost business performance. Depending on the particular application, the data that is analysed consists of either historical records or new information that has been processed for real-time analytics. In addition, it can come from a mix of internal systems and external data sources.

Data analytics can also be divided into quantitative analysis and qualitative analysis. The former includes the analysis of numerical data with quantifiable variables. These variables can be optimized statistically. The qualitative approach is more translative as it focuses on understanding the content of statistical data like text, images, audio and video, as well as common phrases, themes and points of view.

At the application level, BI and reporting provide business executives and corporate workers with actionable information about key performance indicators, business operations, customers and more. Now, more organizations use self-service BI tools that let executives, business analysts and operational workers run their own temporary queries and build reports themselves.

Advanced types of data analytics include data mining, which involves sorting through large data sets to identify trends, patterns and relationships. Predictive Analysis seeks to predict customer behaviour, equipment failures and other future business scenarios and events. Machine learning can also be used for data analytics, by running automated algorithms to churn through data sets more quickly than data scientists can do via conventional analytical modelling. Big data analytics applies data mining, predictive analytics and machine learning tools to data sets that can include a mix of structured, unstructured and semi-structured data. Text mining provides a means of analysing documents, emails and other text-based content.

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 Data analytics supports business widely like:- banks and credit card companies analyse withdrawal and spending patterns to prevent fraud and identity theft ; E-commerce companies and marketing services providers use clickstream analysis to identify website visitors who are likely to buy a particular product or service ; Healthcare organizations mine patient data to evaluate the effectiveness of treatments for cancer and other diseases ; Mobile network operators examine customer data to forecast churn that enables them to take steps to prevent customers from defecting to rival vendors.

To boost customer relationship management efforts, companies engage in CRM analytics to segment customers for marketing campaigns and equip call centre workers with up-to-date information about callers.

Tools Used in data analytics are Microsoft Excel, Microsoft BI, Tableau, Python, SAS, Apache Spark, Rapid Miner, Knime, etc.

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Full Stack Web Development

Full Stack is a layer of software and web development consisting of the front-end and the back-end portions of an application. Front-end is what the users see or interact with on the application. Back-end is the part which users do not see, such as application's logic, database, server, etc. A full-stack web developer should be comfortable in working with both back-end and front-end technologies which would make a website or application attractive and function according to the needs. The developers must have an experience on creating user interface and user experience for front-end, and should also have strong knowledge in a programming language that is used for handling the logic of the application.

In the world full of software programming, it helps to have someone on the team who is a jack of all trades. They help with various stages of development and have the versatility and time management to assist all levels of the development team.

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What Does a Full Stack Developer Do?

As we all know, a Full Stack Developer is someone who works with the Back End — or server side — of the application as well as the Front End, or client side. Full Stack Developers must have some skills in a wide variety of coding niches, from databases to graphic design and UI/UX management in order to do their job well.

Some of the key responsibilities of a Full Stack Developer consists of:

·       design and development of the software

·       Optimization by testing and debugging software

·       Coding for the front and back end of the software

·       Designing UIs on the web application

·       Creating servers and databases

·       Ensuring cross-platform compatibility and optimization

·       Testing and maintaining the responsive design of the applications

·       Working with graphic designers for designing new features

·       Develop APIs and RESTful services

·       Updating up with technological advances to optimize their software

·       Communicating effectiveness of emerging technologies

·       Consider security, maintenance, scalability, and much more

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Data Science Technology & Its Usage

Data Science has emerged out as one of the popular fields of 21st Century. Companies hire Data Scientists and Data Analyst to help them gain insights about the market and to improve their products and services in better way.

Data Scientists work as decision-makers and are responsible for analysing and for handling a large amount of oragnized or predefined data as well as raw data which have to be first oragnized to analyze.

Data science is the field of study that consists of domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data scientists apply machine learning with python algorithms to numbers, text, images, video, audio, and more to produce artificial intelligence (AI) and deep learning to perform tasks that work on the human intelligence to make every field more precise. In turn, these systems generate insights which analysts and business users can translate into tangible business value.

The field includes preparing data for analysis, formulating data science problems, analysing data, developing data-driven solutions, and presenting findings to inform high-level decisions in a broad range of application domains. As such, it incorporates skills from computer science, statistics, information science, mathematics, information visualization, data sonification, data integration, graphic design, complex systems, communication and business.

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 Why Data Science training course is Important?

 In 80s, "data analysis", acknowledged the emergence of a new discipline focused on data of various origins and forms, combining established concepts and principles of statistics and data analysis with computing.

With every increasing day, most companies are coming realizing the importance of data science, Artificial Intelligence, and machine learning. Regardless of industry or the size, organizations those wish to remain competitive in the age of need of data to efficiently develop and implement data science capabilities or risk being left behind.

 Uses of data science:

Internet Search

Digital Advertisements (Targeted Advertising and re-targeting)

Recommender Systems

Advanced Image Recognition

Speech Recognition

Gaming

Price Comparison Websites

Airline Route Planning

Fraud and Risk Detection

Delivery logistics

Healthcare

Augmented Reality

 A best data scientist is the one who creates programming code, and combines it with statistical knowledge to create insights from data. He is the one responsible for extracting, manipulating, pre-processing and generating predictions from the information provided.

  Tools Used In Data Science:

Sas, Apache Spark, MATLAB, Excel, Tableau, Jupyter etc.