data science vs. big data
Data science is statistics, arithmetic, programming, problem-
Solve the problem, capture the data in a clever way, in this way, the power to look at other things, and the activity to clean up, prepare and adjust the information.
Simply put, this is the umbrella of the technology used when trying to extract insights from data.
More details: Big data refers to the large amount of data that exists that standard applications cannot handle effectively.
The processing of huge data begins with data that is not aggregated, and the most common case is that it cannot be stored in the memory of a laptop.
Accustomed to describing the nonsensical nature of a large amount of data, every unstructured and structured huge amount of data will flood the business on a regular basis.
Huge data is something that is used to analyzing insights that can lead to higher choices and strategic business actions.
The big data definition given by Gartner is: \"Big Data
Speed and/or high
Various information assets that require cost
Efficient and innovative data science has changed the automation of insight, decision-making ability and methods.
\"Applications for every field of data science: Internet search: search engines use data science algorithms to provide the simplest results for search queries in just a few seconds.
Digital advertising: complete digital promotion information science algorithm for spectrum use
From display banners to digital billboards.
This is usually the average reason why digital advertising gets a higher CTR than ancient advertising.
Recommendation System: recommendation system not only makes it easy to find related products from billions of products, but also adds a lot to users togetherexperience.
Many companies use this method to promote their products and suggestions based on users\' needs and data connections.
The user\'s previous search results support these suggestions.
Big data: big data for financial services: MasterCard, retail bank, non-
Public Wealth Management Consultants, insurance companies, venture funds, and institutional investment banks all use large amounts of data for his or her money services.
The common drawback of all these methods is that a large number
Structured data that lives in multiple different systems can be solved by huge amounts of data.
Therefore, a large amount of data is used in many ways, such as: customer analysis compliance analysis fraud analysis big data in operational analysis Communications: getting new users, retaining customers, and increasing the existing user base every once in a while is the top priority for telecom service providers.
Solutions to these challenges lay the capacity to mix and analyze a large number of customers
Generated Data and machines
Generated Data created every day.
Retail Big Data: physical or Internet e-commerce
Tailer, a solution that keeps moving and competitive, knows customers better and serves them.
This requires investigating all the different data sources that companies consume on a daily basis, as well as blogs, customer transaction data, social media, stores --
MasterCard brand data and loyalty program data.
More details: skills needed to be a data scientist: Education: Master\'s degree in one mile and a half, PhD in 46
Deep data for SAS or R: R is usually the best for Data Scienceliked.
Python writing: Python is the most common coding language used in data science with Java, Perl, C/C.
Hadoop platform: Although it is not an ongoing requirement, it is still the best to understand the Hadoop platform --
Like this field.
In addition, having a small amount of expertise in a hive or pig is also a huge problem.
SQL database/coding: NoSQL and Hadoop are an important part of the information science background, but it is still the best --
I like it if you write and execute complex queries in SQL.
Processing unstructured data: Data people must process unstructured data whether on social media, video sources, or audio.
Website: be a huge data professional: analytical skills: the power to be the massive amount of data you simply get.
With analytical skills, you can confirm that the data is relevant to your answer and that there are similar questions --solving.
Creativity: you want to have the ability to form new strategies for collecting, interpreting, and analyzing data strategies.
This is usually a particularly appropriate ability.
Math and applied math skills: smart, \"digital computing\" in old schools \".
\"This is often necessary in the fields of data science, data analytics, and big data.
Computer Science: computers are the main force behind every data strategy.
Programmers can continue to need to return algorithms to put data methods into insights.
Business skills: a huge data professional can understand the business objectives on site, and because the underlying process that drives business expansion is also its profit.