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WorksheetsData Analytics and Artificial Intelligence
Total questions: 10
Worksheet time: 6mins
Why is data cleaning important in data analytics?
Data cleaning is important in data analytics because it helps to ensure accuracy, consistency, and reliability of the data, which is essential for making informed decisions and drawing accurate conclusions.
Data cleaning is important in data analytics because it is a time-consuming process with no real benefits.
Data cleaning is not important in data analytics because the data is already accurate and reliable.
Data cleaning is important in data analytics because it helps to introduce errors and inconsistencies into the data.
What is descriptive analytics?
Descriptive analytics predicts future outcomes based on historical data
Descriptive analytics is a type of data analysis that describes what has happened in the past by using historical data to better understand and explain business performance.
Descriptive analytics analyzes real-time data to make business decisions
Descriptive analytics focuses on identifying trends and patterns in data
Which library is commonly used for machine learning?
pandas
numpy
scikit-learn
matplotlib
>> a = np.random.uniform(low=1., high=3.5, size=4)
>> ...
>> a
output:
array([2.144, 2.348, 3., 2.483])
np.round(a)
np.sort(a)
a = np.round(a,1)
a = np.round(a,3)
What are some common techniques used for data cleaning?
Some common techniques used for data cleaning include removing duplicates, handling missing data, correcting errors, and standardizing data formats.
Ignoring missing data
Introducing more errors to the data
Adding more duplicates to the dataset
Which of the following is not a function in the pandas package
random
Series
sort
read_csv
What are the different types of machine learning algorithms?
The different types of machine learning algorithms include only supervised learning
The different types of machine learning algorithms include supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, and deep learning.
The different types of machine learning algorithms include only reinforcement learning
The different types of machine learning algorithms include only decision tree learning
If there is a question, "How can we cut down on the number of people we employ without reducing the quality of our product?" at the "Determine What You're Going to be Measuring" stage. Which of the following is not relevant to measure?
The number of annual customers
How much the business pays these employees each month
Number of employees who like playing football
The amount of time these employees are currently spending on actually making the product
The second stage carried out in data analysis is...
Setting of goals
Clearly setting priorities for measurement
Data gathering
Data scrubbing
>> np.random.seed(20)
>> a = np.random.uniform(low=1., high=2.5, size=4)
>> a
output:
array([1.88, 2.35, 2.34, 2.5])
>> ...
>> a
output:
array([1.88, 2.35, 2.34, 2.5])
a[0] = 1.882
a[1] = 2.351
a[0, 1] = 2.34
a[3] = 2.50
