It didn’t take much time for the world to understand the most important source of power is data. Since then, almost all the major companies have invested heavily in the issues related to data management. With the emergence of Hadoop and Big Data, the focus shifted towards identifying useful data to extract insights which could help people or companies. Here is where data science comes into the picture. Livewire Thrissur, through this article, helps you to understand the basic concepts present in every data science course.
Components of Data Science
Data Science in itself is not a separate entity. It is a collection of different subjects like Mathematics, Statistics, Machine Learning, Big Data etc. Collectively, we make use of the principles of these and succeed in gaining new insights. In a normal scenario, the lifecycle of a data science project consists of the following processes:
- Data Discovery
- Data Preparation
- Model Planning
- Model Building
- Operationalize
- Communication
The first step involves the discovery of data from various sources. It might include both structured and unstructured data. The next step in the cycle is the preparation of data such that it can be useful. In the model planning stage, we should find decide on a method or technique through which we are going to differentiate between variables. Preparation of datasets for testing comes under the model building. After this, then we are in with a lot of documents and reports regarding the study conducted. The final step is to communicate the results of the study to the necessary people and to make certain the initial goal has been met.
Now let us delve deeper into the subject and find the major components.
DATA
The most basic unit of data science is the data. It is through the intake of data, we are able to gain information. In most projects, data includes both structured and unstructured data. For those who are unaware, structured data means the organized data in which one can easily search for a particular piece of information if needed. In the case of unstructured data, there is no organization or pre-defined format. So it is difficult to analyze and process the data.
PROGRAMMING
As it with most modern processes, programming plays a crucial role in data science too. The data received is analyzed and managed through different algorithms which contain the logic of the entire process. Mainly, Python and R programming are used in Data science. Whereas Python is widely used by programmers everywhere, R programming is mainly used by statisticians and data miners.
STATISTICS & PROBABILITY
To extract the needed information from the data obtained, one has to be an expert in statistics and probability theorems. Of course, you can proceed without a mathematical expert, but there is a high chance that the results may not be what is expected out of the process and the conclusion would be wrong.
MACHINE LEARNING
Machine learning algorithms like regression and classification play a major role in extracting useful insights from the processed data and to possibly predict future outcomes in the given field. It is an integral part of data science.
BIG DATA
Big Data refers to a huge volume of data which cannot be processed by normal means and which contains both structured and unstructured data. In data science, big data is an
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