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