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Data Cleaning and Jupyter Notebook Essentials
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Data Cleaning and Jupyter Notebook Essentials
Data Cleaning and Jupyter Notebook Essentials
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1
Question
Q1: Match the types of missingness with their definitions. — MCAR (Missing Completely At Random)
Page 1
Answer
Missingness is completely unrelated to any variable.
2
Question
Q1: Match the types of missingness with their definitions. — MAR (Missing At Random)
Page 1
Answer
Missingness is related to other observed variables.
3
Question
Q1: Match the types of missingness with their definitions. — MNAR (Missing Not At Random)
Page 1
Answer
Missingness is related to the unobserved value itself.
4
Question
Q1: Match the types of missingness with their definitions. — Absolute Missingness
Page 1
Answer
Missingness is unrelated to any variable.
5
Question
Q2: Empty cells in a dataset cannot cause any issues during analysis.
Page 1
Answer
False. Empty cells can cause issues during analysis.
6
Question
Q3: Duplication can inflate counts, leading to _____ of datasets.
Page 1
Answer
misleading interpretations
7
Question
Q4: Removing duplicates is important to maintain data integrity and accurate analysis.
Page 1
Answer
True. Removing duplicates supports data integrity and accurate analysis.
8
Question
Q5: Imputation methods can _____ rather than dropping entire rows.
Page 1
Answer
fill in missing values
9
Question
Q6: Data cleaning ensures that all datatypes are _____ for analysis.
Page 1
Answer
consistent and validated
10
Question
Q7: Which method is suitable for normalizing numerical data?
Page 1
Answer
Min-Max Scaling
11
Question
Q8: Which function in pandas is used to detect duplicates in a DataFrame?
Page 2
Answer
duplicated()
12
Question
Q9: The process of data cleaning involves correcting, removing, or imputing _____ to ensure reliability.
Page 2
Answer
inaccurate or missing data
13
Question
Q10: Data preprocessing is necessary before applying machine learning algorithms.
Page 2
Answer
True. Preprocessing is necessary before applying machine learning algorithms.
14
Question
Q11: Which of the following is NOT a typical reason for data cleaning?
Page 2
Answer
To add additional records manually.
15
Question
Q12: Match the data cleaning techniques with their descriptions. — Removing Rows
Page 2
Answer
Dropping whole entries when they contain errors or null values.
16
Question
Q12: Match the data cleaning techniques with their descriptions. — Replacing Values
Page 2
Answer
Substituting erroneous values with appropriate ones.
17
Question
Q12: Match the data cleaning techniques with their descriptions. — Data Imputation
Page 2
Answer
Filling in missing values using statistical methods.
18
Question
Q12: Match the data cleaning techniques with their descriptions. — Standardization
Page 2
Answer
Adjusting values to a common scale.
19
Question
Q13: Data cleaning can improve the accuracy of analysis results.
Page 2
Answer
True. Data cleaning can improve analysis accuracy.
20
Question
Q14: What is one common method to fill missing values in a numerical dataset?
Page 2
Answer
Using the median value of the column.
21
Question
Q15: It is preferable to fill missing values with the mean in all circumstances.
Page 3
Answer
False. Mean imputation creates bias when data has outliers or skewness.
22
Question
Q16: Data cleaning is the process of correcting, removing, or imputing inaccurate or missing data so that the dataset becomes _____ for analysis.
Page 3
Answer
valid
23
Question
Q17: One of the main uses of Jupyter Notebooks in data science is for conducting _____ (EDA).
Page 3
Answer
Exploratory Data Analysis
24
Question
Q18: Anaconda is a distribution that simplifies package management and deployment for _____ and _____ programming languages.
Page 3
Answer
Python, R
25
Question
Q19: Visual outputs in Jupyter Notebooks can include tables, charts, and _____.
Page 3
Answer
graphs
26
Question
Q20: Markdown in Jupyter Notebook is used for creating well-structured _____ and notes.
Page 3
Answer
documentation / tables
27
Question
Q21: Sessions in Google Colab can hold temporary files and _____ that can be used during a notebook run.
Page 3
Answer
folders
28
Question
Q22: The file format used for Jupyter Notebooks is _____ which stands for interactive notebook.
Page 4
Answer
ipynb
29
Question
Q23: To import datasets in Google Colab, one can access files from _____ or session storage.
Page 4
Answer
Google Drive
30
Question
Q24: Which Jupyter Notebook feature allows you to create plots directly within the notebook?
Page 4
Answer
Inline plotting with commands like plt.show()