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Scope of Data Science

Being a Data Scientist is one of the trending career option of the decade . the demand of the data scientist is huge , The number said to be much higher then the available candidates .so,choosing data science as career option has lot of scope and will remain so in the near future.

What Is Data Science?

It is the blend of various tools, algorithms, and machine learning principles with the goal to discover and hidden patterns from the raw data. Data Science is primarily used to make decisions and predictions making use of predictive and casual analytics, perspective analytics and machine learning.

Data science is one of the famous and trending good options of the year. Choosing data science as a carrier option has a lot of scope and will remain so in the near future. Nowadays Data Science industry is very high and trained people is less. Salaries also still very high and this is the good chance to start the carrier.

The main Advantages of Data Science:

  1. Decreasing risk and fraud
  2. Delivering relevant products
  3. Personalised customer experiences

Different roles in Data Science:

  • The data Architect
  • The Statistician
  • The Business Analyst
  • The Database Administrator
  • The Business Analyst
  • Data and Analytics Manager

About the Data Science

There are 3 components involved in Data Science are OrganisingPackaging and Delivering the data.

Organising Data: Involves the physical storage and format of data and incorporated best practices in data management.

Packaging Data: Involves Logically manipulating and joining the underlying raw data into new representation and package.

Data Delivering: Involves ensuring that the message, the data has been accessed by those who need to hear it.

Important tools required in Data Science

Big Data:

Big data is referred to the study and applications of data sets that are too complex for general data-processing application software to adequately deal with. Big data does include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source. Big data was well known with three key concepts: volume, variety, and velocity.

Machine Learning:

Machine learning (ML) is a category of an algorithm that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. The basic premise of machine learning is to build algorithms that can receive input data and use statistical analysis to predict an output while updating outputs as new data becomes available.

The processes involved in machine learning are similar to that of data mining and predictive modeling. Both require searching through data to look for patterns and adjusting program actions accordingly.

Deep Learning:

Deep Learning is a sub field of machine learning referred to algorithms inspired by the structure and function of the brain known as artificial neural networks.

Artificial Intelligence:

Artificial intelligence (AI) concept of intelligent machines that work and react like humans. Some of the activities computers with artificial intelligence are designed for include:

  • Speech recognition
  • Learning
  • Planning
  • Problem solving etc..