How connected are DS, DL, ML, AI and BD?
When I first started reading about Data Science, there were times I would stumble or sneak peak at concepts like Deep Learning (DL), Machine Learning (ML), Artificial Intelligence (AI) and Big Data (BD). At times they seemed to be just as different as siblings sharing the same set of parents. And at times they seemed to be as different as ambassadors operating for different countries. The point being they are different yet connected in some way. The skills pertaining to or the tools used within each of them however can be used across each of these concepts with different perspectives.
Data Science — Is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data, and apply the knowledge and actionable insights from data across a broad range of application domains.
Example: Individual portfolio management, Medical recommendations.
Deep Learning — Is an imitation of the workings of a human brain in processing data and creating patterns which can be used in making decisions.
Example: Virtual assistants, chatbots.
Machine Learning — Is based on the idea that systems can automatically learn from historical data and experiences, identify patterns and help in making decisions.
Example: Financial predictions, medical prognosis.
Artificial Intelligence — Is simulating human intelligence into machines that are programmed to think like humans and imitate their actions.
Example: Automated investing, and proactive healthcare management.
Big Data — Is the large volume of data both structured and unstructured, that floods into businesses on a daily basis.
Example: Data related to stock exchanges, and healthcare statistics.
I am hoping that my understanding of these buzzwords around Data Science will definitely evolve and emerge while reading, learning, and experimenting more about it.
