{"id":2890,"date":"2025-01-06T23:29:31","date_gmt":"2025-01-06T23:29:31","guid":{"rendered":"https:\/\/dataninjasinc.com\/?p=2890"},"modified":"2025-01-06T23:29:31","modified_gmt":"2025-01-06T23:29:31","slug":"data-science-for-everyone","status":"publish","type":"post","link":"https:\/\/dataninjasinc.com\/2025\/01\/data-science-for-everyone\/","title":{"rendered":"Data Science for Everyone"},"content":{"rendered":"
Data science seems like a brand new term but isn\u2019t so. We have always had data science \u2013 typically defined as principles, processes and techniques to understand the world around us through analysis of data.<\/p>\n
Sometimes, data analysis does not necessarily result into decision making. So what do we need to do to get become a data driven decision making organization? First step is to understand what is generally involved in data science and data driven decision making.<\/p>\n
I would have to say that there are two types of data based decisions groups generally identified \u2013<\/p>\n
During the past few years, we have seen tremendous improvements in technology and the natural rise of \u201cBig Data\u201d. So how can we make use of these advances, think analytically at a massive scale and process giant volumes of data on a daily basis?<\/p>\n
The answer is mostly related to data processing. It is important to understand that data processing and data science are two separate yet related entities. Data processing is almost critical to maturation of data science.<\/p>\n
We previously identified two separate classes of data based decisions.<\/p>\n
With this basic difference in data processing and data science in mind, it will be interesting to figure out data science approaches and what can be done to fulfill the promise of pure data based decision making.<\/p>\n
Now that we have reviewed the basics of data driven decision making categories and have discussed a few differences about how data science will require data processing, we are ready to jump into smaller subset of data mining techniques that are foundational to the data science process.<\/p>\n
Following are brief descriptions of data mining techniques:<\/p>\n
Data science, while often seen as a new concept, has always been about analyzing data to better understand the world. However, merely analyzing data doesn\u2019t always lead to actionable decisions. To become a data-driven decision-making organization, it\u2019s essential to grasp the core of data science and its role in decision-making.<\/p>\n","protected":false},"author":1,"featured_media":2655,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","footnotes":""},"categories":[23],"tags":[19,35,51,54],"class_list":["post-2890","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blogs","tag-best-practices","tag-data-compliance","tag-data-management","tag-data-science"],"acf":[],"yoast_head":"\n