2. Data: raw facts
Information: meaning of the data
Knowledge: insights gained from information
Wisdom: apply knowledge to action
Data :Red, traffic light
Information: South facing traffic light on AABC
street has turned red
Knowledge: Traffic light in my direction has turned
red
Wisdom: I need to stop the car
4. • Data literacy is the ability to understand ,
interpret and communicate with data
5. • Data security: protecting data from
unauthorized access, corruption or theft.
• Data privacy: determines who gets to see your
personal information.
6. • Data privacy and data security are often used
interchangeably but they are different from
each other.(t/f)
• ______ is the practice of protecting digital
information from unauthorized access,
corruption or theft throughout its entire
lifecycle.
• A) data security b) data literacy
• C) data privacy d) data acquisition
7. Q. Classify the following into Qualitative and Quantitative data.
which is a good park nearby?
Cricket score
Restaurant bill
Temperature
Gender
Shoe size
Favorite color
Weight of a person
8. Q. Classify the following into discrete and continuous data.
Number of students in a class
Height
Weight
temperature
only
Decimal points are allowed
Decimal points are not allowed
9. Song name year Song length artist
Blinding
lights
2019 3.2 harry
happy 2020 2.49 stephen
7 rings 2018 3 david
Is song length discrete or continuous?
Is song name qualitative or quantitative?
Is year qualitative or quantitative?
Is song length qualitative or quantitative?
10. • Data also is divided based on the domains of
AI.
• For Computer vision applications, data
required is images or videos.
• For Data Science applications, data required is
numbers.
• For Natural Language Processing applications,
data required is text or voice.
11. Data acquisition
• Data acquisition :collecting data
• Steps
1. Data discovery
2. Data augmentation
3. Data generation
12. • Data discovery: searching for data.
– Collecting data
• Data augmentation: adding more data to the
existing data.
13. • Data generation: generating data if data is not
available or recording data using sensors.
• creating new data.
14. • _____ refers to the data collection process
that involves gathering data from multiple
databases and data sources, cataloging said
data, and classifying the data for evaluation
and analysis.
• _____ is the process of artificially generating
new data from existing data, primarily to train
new machine learning (ML) models.
• _____ refers to creating or producing new
data.
16. • Classify into good data or bad data
• Information is well structured
• Information is scattered
• Accurate
• Incorrect
• clearly presented
• Contains relevant information
• Poorly presented
• Not relevant to the requirement
21. Features of data
• Characteristics or properties of the data.
• Student record
– Student name
– Age
– Class
These are the features of student record.
23. Features of data
– Independent: input to the model or information
we provided to make prediction.
– Dependent: output of the model or prediction
24. • Independent features -> model->dependent
features
• Size of house
• No. of rooms house price
• location
25. • Previous mark
• Study time mark prediction
• Have extra tuition
• Sleep time
26. • Employed
• Monthly salary how much loan money?
• Have extra income?
• Have own land?
• gold
28. Data processing and data interpretation
• Niki has 7 candies and ruchi has 4 candies.
How many candies do niki and ruchi have in
total?
• Data processing means operating on data to
produce meaningful information.
29. • Niki has 7 candies and ruchi has 4 candies.
• Who should get more candies so that both
Niki and Ruchi have an equal number of
candies?
• How many candies should they get?
• Data interpretation means analyzing data to
arrive at meaningful decisions.
30. Q. ____ relates to the manipulation of data to
produce meaningful insights.
a. Data processing
b. Data interpretation
c. Data analysis
d. Data presentation
32. Qualitative Data Interpretation
• Qualitative Data Interpretation analyses non-
numeric data.
• It analyses the emotions and feelings of
people.
34. Examples of Qualitative Data Interprepation
• Trending sports
• Trending movies
• Trending athletes
35. Quantitative Data Interpretation
• Quantitative Data Interpretation is made on
numerical data.
• It helps us answer questions like “when”, “how
many” and “how often”?
• Examples
– No. of website visit.
– Cumulative grade point
– Height of students in a class
37. Q. Classify the following into Quantitative data
interpretation and Qualitative data interpretation.
• Group activities is the best to learn things.
• 75% of students scored above 80% in maths. Higher
percentage indicates that teching method is effective.
• Library environment needs to be modernized.
• Interactive activities are better learning methods than
traditional lectures.
• The school might need to evaluate the homework policy,
as exceeding homework could be contributing to student
stress.
• Sales of sandwiches increased by 20% and that of sugary
drinks decreased by 15%. Students are opting for
healthier food options.
44. Q. Which among these is not a type of data
interpretation?
a. Textual
b. Tabular
c. Graphical
d. Raw data
Q. A bar graph is an example of ?
e. Textual
f. Tabular
g. Graphical
