Data Science Interview Questions & Answers | Data Science Interview Questions | Data Science Course

Data Science Interview Questions & Answers | Data Science Interview Questions | Data Science Course
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Unlock the secrets to acing your next data science interview with our comprehensive video, "Data Science Interview Questions & Answers," presented by upGrad. This Top 15 Data Science Interview Questions tutorial is designed to prepare you for the most common and challenging questions you might face in a data science interview, covering a wide range of topics from Basic Data Science , Statistics, Data Analysis, ML, Deep Learning and that's not all - Your soft skill will also play a Major role the interview.

1. What is Data Science?:
Gain a clear definition of data science, including its scope and applications. Learn about the interdisciplinary nature of data science, which combines statistics, computer science, and domain expertise to extract insights from data.
2. Python or R - Which is Better?:
Explore the debate between Python and R, the two most popular programming languages in data science. Understand the strengths and weaknesses of each language and how to choose the right one based on your specific needs and projects.
3. Different Classification Algorithms:
Discover the various classification algorithms used in data science, including logistic regression, decision trees, random forests, and support vector machines.
4. Importance of A/B Testing:
Learn about A/B testing, a crucial method for comparing two versions of a webpage or app to determine which one performs better.
5. Types of Selection Bias:
Identify the three different types of selection bias: sampling bias, observer bias, and survival bias. Learn how to recognize and avoid these biases in your data analysis.
6. Linear Regression :
Gain an understanding of linear regression, a fundamental technique for modeling the relationship between a dependent variable and one or more independent variables.
7. Binomial Probability Formula:
Learn about the binomial probability formula and how it is used to calculate the probability of a specific number of successes in a fixed number of trials.
8. Univariate, Bivariate, and Multivariate Analysis:
Understand the differences between univariate, bivariate, and multivariate analysis, and how to apply these techniques to analyze data sets with one, two, or multiple variables.
9. Artificial Neural Networks:
Explore the basics of artificial neural networks, including their structure and how they are used in machine learning to model complex patterns in data.
10. Recurrent Neural Networks :
Learn about RNNs, a type of neural network designed for sequence data, such as time series or natural language.

Key Moments :
00:00 - What is Data science?
02:34 - Python or R - Which is better?
02:56 - What are the supported data types in python?
03:20 - What are the different classification algorithms?
03:39 - What is Normal Distribution?
03:59 - What's the importance of A/B Testing?
04:55 - There are three different types of selection bias
05:35 - What is Liners Regression?
06:53 - What is the Binomial Probability Formula?
07:54 - What are Artificial Neural Networks?
08:25 - What are RNN?
08:59 - What is Back Propagation?

By the end of this video, you'll be well-prepared to tackle a wide range of questions in your data science interviews. Join us and gain the knowledge and confidence to excel in your data science career. Watch now and get ready to ace your next interview with upGrad!

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