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​WHAT IS DATA SCIENCE?

Data Science involves using automated methods to analyze massive amounts of data and to extract knowledge from them.
There are 3 important sciences which are form Data Science. These are:
  1. Computer Science
  2. Mathematical Statistics
  3. Applications
​It is the combination of all the 3 sciences and every Data Science project involves using them to attain the results required. By combining aspects of statistics, computer science, applied mathematics and visualization, data science can turn the vast amounts of data the digital age generates into new insights and new knowledge.
Go through our blog on What is Data Science to completely understand the important concepts involved. I would like to recommend Edureka’s blogs on Data Science. Check out these blogs which have all the basic content required to clear a Data Science Interview.
  1. Top 35 Data Science Interview Questions
  2. Data Science Tutorial
  3. Edureka’s Data Science Blogs

COMPONENTS OF DATA SCIENCE
Data Science has the following components:
1 . STATISTICS
  1. Statistics is a branch of mathematics dealing with the collection, analysis, interpretation, presentation and organization of data.
  2. Statistics began in the ancient civilization, going back at least to the 5th century BC, but it was not until the 18th century that it started to draw more heavily from calculus and probability theory.
2 . VISUALIZATION
Visualization is when we display the results of Data Science analysis in a simpler way using diagrams, charts and graphs.
It improves decision making, sense of work, customer relationship and financial performance.
3. MACHINE LEARNING
  1. Machine Learning explores the study and construction of algorithms that can learn from and make predictions on data.
  2. Closely related to computational statistics.
  3. Used to devise complex models and algorithms that lend themselves to a prediction which in commercial use is known as predictive analytics.
4. DEEP LEARNING
Deep learning is one of the only methods by which we can circumvent the challenges of feature extraction in machine learning. This is because deep learning models are capable of learning to focus on the right features by themselves, requiring little guidance from the programmer.
Therefore, we can say that Deep Learning is:
 1. A collection of statistical machine learning techniques
 2. Used to learn feature hierarchies
 3. Often based on artificial neural networks

Source: www.quora.com

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