Page 254 - AI Ver 3.0 class 10_Flipbook
P. 254

UNIT 4



                                                                  STATISTICAL DATA


                                                                        (PRACTICAL)












                               Learning Outcomes




                  •  What is Data Science?            •  Introduction to Low/No-Code AI Approach for Statistical Data
                  •  Important Concepts in Statistics      •  Orange Data Mining
                  •  Applying the AI Project Cycle in Orange Data Mining


              Artificial Intelligence (AI) is a powerful technology that drives many of today’s innovations, and it depends heavily
              on data. AI systems use large amounts of data to “learn” through algorithms, allowing machines to do tasks that
              usually need human intelligence. The success of AI depends on the type, quality, and amount of data it receives.
              Depending on the data and the tasks the AI is made to do, AI can be divided into three main domains: Data
              Science, Computer Vision, and Natural Language Processing (NLP).


                       What is Data Science?


              Data Science is a continuous process of exploring and discovering new things by analysing data. It helps us find
              patterns, trends, and insights that allow us to better understand the world. At its core, Data Science takes raw data
              and turns it into valuable knowledge using a mix of statistics, computer techniques, and specialised knowledge
              from different fields.
              Data Science itself is an interdisciplinary field that integrates various aspects of statistics, data analysis, machine
              learning, and other related methodologies. Its purpose is to extract meaningful insights from data and apply them
              to solve real-world problems. To achieve this, data scientists use a diverse range of techniques drawn from fields
              like Mathematics, Statistics, Computer Science, and Information Science, all of which contribute to the development
              of models, algorithms, and tools that enhance data-driven decision-making.
              In  essence,  Data  Science  serves  as  the  backbone  of  AI,  bridging  the  gap  between  raw  data  and  actionable
              intelligence, while providing the foundation for AI systems to learn, adapt, and improve over time.
              For example, imagine a company that sells online clothing. The company collects data about customers’ purchasing
              habits, such as what items they buy, when they buy them, and what discounts they use. A data scientist can analyse
              this data to find patterns, such as which items are most popular during certain seasons or how certain discounts
              affect sales. Using these insights, the company can make data-driven decisions like adjusting their marketing
              strategy, predicting demand, or creating personalised offers for customers.






                    252     Touchpad Artificial Intelligence (Ver. 3.0)-X
   249   250   251   252   253   254   255   256   257   258   259