Data Science- It deals with unstructured and structured data. It is the combination of mathematics, programming, statistics, science, and machine learning algorithms. This field involves everything related to preparation, analysis and data cleansing.
Data is everywhere in the world and is growing faster than before. In 2020, 1.7 megabytes of information have been created for every human. In this blog, we described the difference between Big Data and Data Science. Join Data Science Courses in Bangalore and gain more knowledge in data science and learn how to become a Data Scientist.
The Definition of Big Data is, High-Variety, High-Volume, and High-Velocity, and Cost-effective, new methods of data processing that support process automation, enhanced insight, decision making. In simple terms, it refers to the immense amount of data it consists of and that it can not be processed efficiently with conventional methods. Where new methods or tools are applied to process the data. Join Big Data Training in Bangalore and learns more about Big Data and the tools used in it.
Applications of Data Science
From Digital billboards to Digital banners, the whole digital marketing field is using data science algorithms. Because of the data science algorithm, digital ads are achieving more CTR than traditional advertisements.
Data Science algorithm helps the search engines to give the best results for search queries immediately.
Many companies are using this Recommender Systems to improve the product’s brand and ideas by the user’s demand and suggestions. The recommendations or suggestions are based on the user’s past research results.
Applications of Big Data
Big Data in Communications
The top priorities of telecommunication services are, making new customers, subscribers, and developing the business within the current subscriber. For these challenges, the solutions lie in the capacity to examine and connect the masses of machine-generated data and customer-generated data, which is making every day. Join Big Data Training in Chennai and learn more about the advantages and applications of Big Data
Many of the institutional investment banks are using big data for their financial services like Retails banks, Private wealth management advisories, Credit Card Companies. Big Data has the capability to resolve the problem that commonly occurs due to the storage of large amounts of information in the multiple disparate systems. The big data is used in many ways like:
- Operational Analytics
- Compliance Analytics
- Fraud Analytics
- Customer Analytics
Skills required for Data Scientist
- Qualification for Data Scientist: A Data Scientist should have 88% of aggregate in the master’s degree.
- Must have good knowledge of R and SAS. R programming language is mostly preferred for Data Science.
- Python Coding: Python is one of the popular coding languages, which can be used in data science along with other programming languages such as C, C + +, Java and Perl.
- Hadoop platform: The Hadoop platform is still favored for the Data Science field.
Skills required for Big Data Specialist
Analytical skills: With Analytical skills, you are able to learn what type of data is relevant to the solution, like problem-solving.
Creativity: You should have the attribute of creating new methods for evaluating, analyzing, and collecting data for different strategies.
Computer Science: Must have good knowledge in Computer programs, for developing different processes and algorithms for data insights.
Business Skills: Big Data professionals should have a good understanding of the business, which may help to drive the growth of the business and also its profit.
Data Scientist Salary
The average salary of a Data Scientist is $ 108224 / year.
Big-Data Specialist Salary
The average salary of the Big Data Specialist is $ 106,784 per year.
In this blog, we have described two of the emerging fields at present and that is the Data Science and Big Data field. According to the current data growth trends, Big data will survive in the coming years. Data science is growing quickly with new methods promoted continuously which can help data science experts in the future.