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10.  What elements of data storytelling, when merged together can engage the audience?   [CBSE Sample Paper, 2022]
              Ans.    Narrative and Visual

            B.   Long answer type questions.
                1.   What is the purpose of visualisation in data storytelling? Name a few visualisation tools.
              Ans.    Visualising your data story improves comprehension at all levels. Stories with data visualisation present information
                     in a simple manner, highlight the most relevant data and express crucial ideas fast.
                     There are numerous ways to visualise  your data, including flowcharts, bar charts, infographics, pie charts, and
                     scatterplots. Only those visualisations must be chosen that will make it easier for your audience to grasp and
                     interact with the data.

                2.   Explain how the three elements of a data story can influence change.
              Ans.    Data storytelling uses a structured approach to delivering data insights that always includes a combination of
                     three main elements: data, graphics, and narrative. When a narrative is backed by data, it helps to explain to the
                     audience what is happening in the data and why a specific insight was developed. When visuals are applied to data,
                     they can enlighten the audience to insights that they would not have noticed otherwise, such as charts or graphs.
                     Finally, when narrative and images are combined, the audience can be engaged or even entertained. When proper
                     graphics and narrative are combined with correct data, you have a data story that has the potential to impact and
                     drive change.
                3.   Is data analysis enough to gain insights? Why is there a need for data storytelling?
              Ans.    Even after data analysis, there is no assurance that the analysis alone can bring change in the people unless it is
                     narrated properly. The analysis that is having both data and analytics with a good narrative is much more effective
                     than anecdotes or personal experiences.
                4.   A group of students were taught machine learning. A survey was conducted to map their interest before and at
                     the end of the course. The graph showing their interest is as follows: Create a data story from this information.


                                                         ML PROGRAM

                                                       Before (%)  Course End (%)
                                                             42
                                                                                         39



                                                                 22
                                                 18                      20   19
                                     15
                                                     11
                                         9
                                                                                      5


                                      Bored    Not interested   OK      Little interested  Excited


              Ans.    Before the course                       After the course
                       42%    of students were OK to learn about ML.     58%   of students are interested and excited to learn ML.
                                                                  13%   Drop in the percentage of students who were bored
                                                                      or not interested.
                                                                    Hence, the course was clearly liked by students!







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