Data Science Components
Let’s learn about different data science components. These components work together to allow data scientists to extract valuable insights and knowledge from the raw data.
What is Data Science?
Data Science involves extracting insights from vast amounts of data using various scientific methods, algorithms, and processes. It helps to discover hidden patterns from the raw data. Data Science has emerged because of the evolution of Mathematical Statistics, Data Analysis, and Big data.
Data Science Components
The main Data science components are as follows:
- Data
- Big Data
- Machine Learning(ML)
- Deep Learning(DL)
- Statistics
- Visualization
- Programming language
Data/BigData
Data and Big Data are the main components of data science. Data can be structured, unstructured, or semi-structured. Big Data refers to large datasets characterized by volume, variety, velocity, etc.
ML
Machine Learning(ML) is a subset of AI technology that uses algorithms to analyze data, learn from it, and learn to make predictions about future data without explicit programming.
Statistics
Statistics is the science of collecting and analyzing numerical data in large to get valuable insights.
Visualization
Techniques to display data in the form of visuals like graphs and charts. Visual representation is more insightful and digestible forms.
Languages
Programming Languages like Python are the computer languages that enable data scientists to write algorithms and build systems for data science.
Programming languages that are popular for data science are:
- Python
- R
Python
Python is a high-level programming language that provides an extensive standard library. It is the most popular language among data scientists. Python Tutorial on this website can be found at:
R
R is also a popular programming language among data scientists. It can be used with major operating systems, such as Windows, Linux, and Mac OS.