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These datasets contain both quantitative data (number of petals and height) and qualitative data (colour and fragrance
                    intensity) for different types of flowers (Rose, Lily, Sunflower, Tulip).
                     a.  Discuss the differences between quantitative and qualitative data interpretation.
                     b.   Describe the methods and techniques commonly used for interpreting quantitative and qualitative data, highlighting
                       their respective strengths and limitations.
                  2.  You are tasked with analysing the performance of a company's sales across different regions over the past year. How
                    would you utilise data visualisation techniques to present this information effectively to the company's stakeholders
                    during a quarterly review meeting? Describe the types of visualisations you would use and explain how they would help
                    convey the sales trends and patterns to the audience.




                               In Life                                          #Experiential Learning


                 1.  Prepare a questionnaire using Padlet.com, to know how data literacy is helpful in education.
                 2.  Make a presentation to depict the "Data Literacy Process Framework".





                             Deep Thinking                                      #Creativity & Innovativeness


                  1.  In today's digital age, data has become an incredibly valuable resource, much like gold was during the gold rush
                     era. In the context of Artificial Intelligence (AI), data is the raw material that fuels AI systems, How?
                  2.  Who first said that Data is a new gold? Is data more precious than the gold? Justify





                                                                                #Experiential Learning
                              Lab



                       Ask students to collect data of different coloured objects in the Lab and record it in a spreadsheet. Create a
                       basic bar chart to visualise the collected data using spreadsheet software. Later, ask students to present their
                       bar charts, followed by a brief discussion on the importance of data quality and ethical considerations in AI.







              Answers


              Exercise (Section A)
              A.  1. d  2. c     3. b     4. c    5. d    6. a    7. d    8. d    9. a    10. b
              B.  1. Data                 2. Data literate           3. Data literacy
                  4. Data literacy framework   5. Data augmentation   6. Primary data            7. Business intelligence
                  8. Tabular DI           9. Numeric                 10. Longitudinal studies
              C.  1. True                 2. True                    3. False                    4. False
                  5. False                6. True                    7. False                    8. True





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