Data Science comparison with AI, ML, BI, and data warehousing, data mining.

Data Science comparison with AI, ML, BI, and data warehousing, data mining.

1. Data Science Comparison with Business Intelligence, Artificial Intelligence, Machine Learning and Data Warehousing/Data Mining

2. Data Science vs Machine Learning Data Science Data Science is a field about processes and systems to extract data from structured and semi-structured data. Data Science as a broader term not only focuses on algorithms statistics but also takes care of the data processing . Data in Data Science maybe or maybe not evolved from a machine or mechanical process. Many operations of data science that is, data gathering, data cleaning, data manipulation, etc. It is used for discovering insights from the data. Example: Netflix uses Data Science technology. Machine Learning Machine Learning is a field of study that gives computers the capability to learn without being explicitly programmed. I t is only focused on algorithm statistics. It uses various techniques like regression and supervised clustering.  It is three types: Unsupervised learning, Reinforcement learning, Supervised learning. It is used for making predictions and classifying the result for new data points. Example: Facebook uses Machine Learning technology.

3. Data Science vs Artificial Intelligence Data Science Data Science is a field about processes and systems to extract data from structured and semi-structured data. Data Science as a broader term not only focuses on algorithms statistics but also takes care of the data processing . Data Science will have a variety of different types of data, including structured, semi-structured, and unstructured type of data. Many operations of data science that is, data gathering, data cleaning, data manipulation, etc. It is used for discovering insights from the data. Its applications are advertising, marketing, Healthcare, etc. Artificial Intelligence Artificial Intelligence is the implementation of a predictive model to forecast future events and trends . Automation of the process and the granting of autonomy to the data model are the main goals of artificial intelligence. AI uses standardized data in the form of vectors and embeddings. It has a lot of high levels of complex processing. It is used for making predictions and classifying the result for new data points. Its application is robotics, automation, etc.

4. Data Science vs Business Intelligence Data Science Data Science is a field about processes and systems to extract data from structured and semi-structured data. It focuses on the future. It deals with both structured as well as unstructured data. Many operations of data science that is, data gathering, data cleaning, data manipulation, etc. It is used for discovering insights from the data. Greater business value is achieved with data science in comparison to business intelligence as it anticipates future events. Business Intelligence It is basically a set of technologies, applications and processes that are used by the enterprises for business data analysis. It focuses on the past and present. It mainly deals only with structured data. It is much simpler when compared to data science. Business Intelligence helps in performing root cause analysis on a failure or to understand the status. Business Intelligence has lesser business value as the extraction process of business value carries out statically.

5. Data Science vs DW-DM Data Science Data Science is a field about processes and systems to extract data from structured and semi-structured data. It is focuses on historical and future data needs. Many operations of data science that is, data gathering, data cleaning, data manipulation, etc. Works with structured and unstructured data. Applicable in virtually every industry with data-driven needs. Data Warehousing/Data Mining Data Warehousing is the technology of storing/retrieving large amounts of data. I t is focuses on historical data for mining. Primarily deals with data storage, retrieval and mining. Focuses on structured data for analysis. Applied in finance, retail and customer relationship management.

6. Artificial Intelligence Machine Learning Deep Learning Data Science Summary

7. Thanks for Watching!