data_literacy_ch2_scientificst.pptx (data literacy)

Ai data literacy

1. Data literacy Chapter 2

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

3. Data Pyramid

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.

15. Data is collected directly from the source

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

17. Data preprocessing Structure Good structure Poor structure Data cleaning Accuracy: closeness to the actual value.

18. Good structure, Poor structure Text Spreadsheet Table video

19. Data cleaning Clean data from duplicates, missing value and errors

20. accuracy How well data matches real- world values.

21. Features of data Characteristics or properties of the data. Student record Student name Age Class These are the features of student record.

22. Data features Independent features Dependent features

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

27. 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.

28. 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.

29. Q. ____ relates to the manipulation of data to produce meaningful insights. Data processing Data interpretation Data analysis Data presentation

30. Data Interpretation Quantitative Data Interpretation Qualitative Data Interpretation

31. Qualitative Data Interpretation Qualitative Data Interpretation analyses non- numeric data. It analyses the emotions and feelings of people .

32. Examples of Qualitative Data Interprepation Trending sports Trending movies Trending athletes

33. Quantitative D ata 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

34. 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.

35. Q. Quantitative data is numerical in nature.(T/F)

36. Data Interpretation Textual DI Tabular DI Graphical DI

37. Textual DI Data is mentioned in the text form.

38. Tabular DI Data is represented systematically in the form of rows and columns.

39. Graphical DI

40. Q. Which among these is not a type of data interpretation? Textual Tabular Graphical Raw data Q. A bar graph is an example of ? Textual Tabular Graphical