Cisco Data Scientist Interview Questions, Process, and Tips

Q1. Python, alogrithms, data science
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Python 45 mins, OOPS Concepts, SQL

Q1. What is p value
Ans. 
P value is a statistical measure that helps determine the significance of results in hypothesis testing.
• P value is the probability of obtaining results as extreme as the observed results, assuming the null hypothesis is true.

• A small p value (typically ≤ 0.05) indicates strong evidence against the null hypothesis, leading to its rejection.

• Conversely, a large p value (> 0.05) suggests weak evidence against the null hypot...read more
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Q2. When will you use t test
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T test is used to determine if there is a significant difference between the means of two groups.
• Use t test when comparing means of two groups to determine if there is a significant difference.

• Commonly used in hypothesis testing to assess if there is a statistically significant difference between groups.

• Example: Comparing the average test scores of students who received tutoring versus those who did not.

• Example: Testin...read more
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How will you do customer segmentation

Q1. Tools you worked on previously
Ans. 
I have worked with tools such as Python, R, SQL, Tableau, and Excel for data analysis and visualization.
• Python

• R

• SQL

• Tableau

• Excel
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Q2. Why did you choose cisco
Ans. 
I chose Cisco for its reputation as a leader in networking technology and its commitment to innovation.
• Cisco is a global leader in networking technology

• Cisco has a strong reputation for innovation and cutting-edge solutions

• I admire Cisco's commitment to diversity and inclusion in the workplace
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Q1. Explain K-Means algorithm
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K-Means is a clustering algorithm that partitions data into K clusters based on similarity.
• Divides data points into K clusters based on distance from centroid

• Iteratively updates centroids to minimize within-cluster variance

• Requires specifying number of clusters K beforehand

• Example: Grouping customers based on purchasing behavior
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Q1. How do you select the appropriate learning algorithm for a problem?
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Selecting the appropriate learning algorithm involves considering the problem's characteristics and requirements.
• Understand the problem's nature, such as classification, regression, clustering, etc.

• Consider the size of the dataset and the computational resources available.

• Evaluate the complexity of the relationships within the data.

• Experiment with different algorithms and compare their performance using metrics like ac...read more
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Q1. Questions to assess compatibility with team culture
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Q1. Your past experience related questions
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Q1. Role related questions
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