Page 318 - AI Ver 3.0 Class 11
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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()
316 Touchpad Artificial Intelligence (Ver. 3.0)-XI

