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In Life                                                 #Interdisciplinary



                   Statistics is the heart of Data Science, helping to evaluate, transform, and predict data. So, if you want to excel in
                   this amazing field, you must first become acquainted with the relevant Statistics topics for Data Science. Few are
                   mean, median, mode, correlation and standard deviation. Find out 5 more Statistics concepts you should know for
                   a career in Data Science.




                              Deep Thinking                                           #Creativity and Innovativeness



                   Misleading interpretations resulting from inaccurate or "unclean" data can influence flawed business strategies and
                   decision-making processes. Such misinterpretations may lead to embarrassing situations during reporting meetings
                   when the inadequacy of the data becomes apparent. It is crucial to establish a culture of high-quality data within
                   your organisation to avoid such scenarios. To achieve this, it is necessary to document the tools employed to foster
                   this culture and define the standards and criteria for data quality that are meaningful to your organisation. Find out
                   any five characteristics of quality data.




                                                                                   #Coding & Computational Thinking
                              Lab



                       1.   Construct a simple line graph to represent the rainfall data of Delhi as shown in the table below
                            using Python:
                              Month    Jan   Feb   Mar   Apr   May    Jun    Jul  Aug    Sep   Oct   Nov    Dec
                             Rainfall   2.7   5    10.4    5    9.3   20.3   40    45     50   20.5   10    5.5
                               (cm)
                       Ans.
                            import matplotlib.pyplot as plt
                            # Data
                              months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep',
                            'Oct', 'Nov', 'Dec']
                            rainfall = [2.7, 5, 10.4, 5, 9.3, 20.3, 40, 45, 50, 20.5, 10, 5.5]
                            # Create line graph
                            plt.plot(months, rainfall, marker='o', linestyle='-')
                            # Add title and labels
                            plt.title('Rainfall Data of Delhi')
                            plt.xlabel('Month')
                            plt.ylabel('Rainfall (cm)')
                            # Rotate x-axis labels for better readability
                            plt.xticks(rotation=45)
                            # Show plot
                            plt.show()



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