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