Data is a set of factual information made up of numbers, words, observations, and measurements that may be used for calculations, discussions, and reasoning.
The raw dataset is the fundamental building block of data science, and it can be structured (tabular structure), unstructured (photos, recordings, communications, PDF documents, and so on. ), or semi structured.
The structured data is well-organized, presented, and easy to find. The structured data is easily understood by the machine language. Name, address, date, and so on are some examples.
For structured data, RDBMS, CRM, and ERP are appropriate.
Unstructured data is unformatted, disorganised, and cannot be processed or analysed using traditional methods and devices, such as text, audio, video, social media activity, and so on.
Unstructured data is best stored in non-relational and NoSQL databases.
Big Data refers to extremely large data collections. Volume, diversity, velocity, vision, value, variability, and imagery, for example, are all part of it.
Data is compared with raw petroleum, which is a valuable crude resource, and scientists may extract various forms of data from crude information by using data science to separate the refined oil from the unrefined petroleum.
Hadoop, Spark, R, Java, Pig, and other devices are among the numerous tools used by data scientists to process large amounts of data.
Machine Learning is a subset of Data Science that allows a system to process datasets autonomously, without the need for human intervention, by employing a variety of algorithms to operate on large amounts of data created and retrieved from a variety of sources.
It generates forecasts, analyses trends, and offers advice. Fraud detection and client retention are two areas where machine learning is commonly utilised.
Data is managed in order to extract data from it. The numerical foundation of data science is insights and likelihood, since without a decent learning of measures and likelihood, there's a great risk of confusing the data and arriving at an incorrect conclusion. This is why statistics and probability play such an important role in data research.
This is the section that deals with upholding high ethical standards as a working data scientist. As a data scientist, you must be aware of the implications of your project's outcomes and conclusions. Be honest with yourself. Avoid altering data or employing a strategy that may cause results to be skewed. From data collection to analysis, model development, testing, and application, act ethically at all times. If you want to deceive or manipulate your audience or supervisor, don't fabricate results. Be ethical while interpreting the results of your data science research.
