Data Analytics

Big Data: Big Data refers to humongous volumes of data that cannot be processed effectively with the traditional applications that exist. The processing of Big Data begins with the raw data that isn't aggregated and is most often impossible to store in the memory of a single computer.
A buzzword that is used to describe immense volumes of data, both unstructured and structured, Big Data inundates a business on a day-to-day basis. Big Data is something that can be used to analyze insights which can lead to better decisions and strategic business moves.
The definition of Big Data, given by Gartner is, Big data is high-volume, and high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.
Data Analytics: Data Analytics the science of examining raw data with the purpose of drawing conclusions about that information.
Data Analytics involves applying an algorithmic or mechanical process to derive insights. For example, running through a number of data sets to look for meaningful correlations between each other.
It is used in a number of industries to allow the organizations and companies to make better decisions as well as verify and disprove existing theories or models.
The focus of Data Analytics lies in inference, which is the process of deriving conclusions that are solely based on what the researcher already knows.

Well, you must have heard that "Data Scientist is the sexiest job in the 21st Century". That alone is a statement enough to give you the idea of why it is a good career option. Although you should choose this career option if you are genuinely interested in this field. You should have some background in programming and keen towards learning & paving your path in Data Science. Do not just consider this field just because it's one of the best options with a very good salary package. Data Scientists play a very important role in the Industry right now with its demand increasing day by day in various fields. Let's look at the use of Data Science in various fields:
Data Mining: Mostly associated with Data Mining, Data Science involves several work-stages, including Data Wrangling, Data Mining and Data Acquisition. Mostly incorporated with Feature extraction and Feature selection, Data Mining is the process of establishing various inferences and relationships between variables in a large data-set. Data Analytics and Data Visualization: Data Analytics and Visualizations is a huge commercial sector under the domain of Data Science. Analyzing Data-points and visualizing it with effective tools like Tableau, D3. Js, etc., is the process incorporated in the same. Machine Learning: The new sensation in the field of AI and intelligent systems, Machine Learning is surely taking technology by storm. Machine Learning is the process of building predictive models or intelligent systems by training it against previous structured data. Business Intelligence: Data Science also has a huge hand in the commercial sectors for financial modelling, risk analysis, credit-generated bank reports, loan allowance predictions, etc. The reasons why Data Science is a great career option for you are:
Demand for Analytic Professionals
Huge job opportunities in the field according to the skills possessed
Great salary
Big Data has huge priority in a lot of companies and organizations
Analytics playing a major role in decision making
Data Science has applications in the very field which is data-driven including Bio-informatics, Medicine Sciences, Mass-Production Systems, BigData Technologies, Cloud Computing, Agriculture, Stock Market, Research in fields of drugs discovery, machine learning and AI. It's a huge market with very few people who are proficient in the same. You can read this article on Forbes which lists out the reasons to why Data Science is a great career option.

It is a type of computing, which involves sharing of computer resources rather than using local servers or dedicated devices for processing. In simple words, it is the process of storing frequently used data on multiple servers which could be accessed by using the Internet. Various services like servers, storage and applications are provided to the users computing devices via Internet. Some good examples of using Cloud are:
Drop box: An online storage providing free storage to the users. Google: Letting the users to create documents and calendars for free. I Cloud: Free Storage for Apple & Mac Users. Need of Cloud Computing:
Cloud computing is now becoming a business standard. It simplifies the user's accessibility. It provides a virtual storage space to the user which could be used without bothering about the details of the entire mechanism. Here are some other reasons why every enterprise might need cloud computing for their business:
Cost savings - Cloud computing removes the requirement of a company to invest in storage hardware and servers. Focusing on the business -Since all the services will execute over the internet, a company does not have to bother about technical issues and other problems associated with physical storage and backup. A company can thus focus more on their core business. Performance - It delivers reliable performance irrespective to the geographical location of the user. Another key feature could be the automatic updating of services and applications. Security - Cloud Computing offers optimum security which protects you against any unauthorized access, modification and loss of data. Flexibility - Even if part of the cloud environment fails or stops working, the other resources continue to work until the problem is fixed. https://www.shiksha.com/it-software/articles/top-specialisations-you-can-opt-for-after-bca-blogId-12966.