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If you want to become a data scientist, you must be prepared
to impress potential employers with your knowledge. And in order to do so, you
must be able to ace your next data science interview in one sitting! We've
compiled a list of the most common data scientist interview questions for your
next interview. We have included the most frequently asked **Data
Scientist interview questions** for experienced candidates in this blog.

**Data
Scientist Interview Questions**

**Q1. What is
the difference between time series problems and other regression problems?**

Time series data can be considered as an extension to linear regression that employs terms such as autocorrelation and average movement to summarize historical data of y-axis variables in order to forecast a better future.

**Q2.What is
RMSE?**

RMSE is an abbreviation for Root Mean Square Error. RMSE is used in a linear regression model to evaluate the performance of the machine learning model. It is used to assess the data distribution around the line of best fit.

**Q3. What is
MSE?**

Mean Squared Error is used to calculate how close the line is to the actual data. So we take the difference in the distances of the data points from the line and square it. This is repeated for all data points, and the squared difference divided by the overall number of data points provides the Mean Squared Error (MSE).

**Q4. Explain
the fundamentals of neural networks.**

Different neurons can be found in the human brain. These neurons collaborate and carry out various tasks. Deep learning neural networks attempt to look like human brain neurons. The neural network learns patterns from data and uses the knowledge it gains from different patterns to predict the output for new data.

**Q5. What are the 3 layers of Neural
Networks?**

·
**Input
Layer:** The input layer of the neural network is where the input is
received.

·
**Hidden
Layer:** In between the input layer
and the output layer, there may be several hidden layers. The first hidden
layers are responsible for detecting low-level patterns, while the following
ones are in charge of combining output from previous ones to find more
patterns.

·
**Output
Layer:** This one outputs the prediction.

**Q6. What
exactly is a Generative Adversarial Network?**

A generative adversarial network (GAN) is a Machine Learning (ML) model in which two neural networks compete to become more accurate in their predictions by using deep learning methods.

**Q7. What is
a computational graph?**

The computational graph is referred to as "Dataflow Graph". TensorFlow, the well-known deep learning library, is built entirely on the computational graph. Tensorflow's computation graph is a network of nodes, with each node performing a specific function. This graph's nodes represent operations, and its edges represent tensors.

**Q8. Define
auto-encoders?**

Learning networks are what auto-encoders are. They convert inputs into outputs with as few errors as possible. In simple terms, this means that the desired output should be nearly equal to or as close to the input as possible.

**Q9. What
are exploding gradients?**

Let's consider you are training an RNN and you observed exponentially growing error gradients that accumulate, resulting in very large updates to the neural network model weights. Exploding Gradients are error gradients that grow exponentially and significantly update neural network weights.

**Q10. What
are vanishing Gradients?**

Assume you are training an RNN once more. Assume the slope has become too small. Vanishing Gradient refers to the problem of the slope becoming too small. It significantly increases training time and results in poor performance and extremely low accuracy.

**Q11. What
is the p-value in the Null Hypothesis and what does it mean?**

P-value is a number between 0 and 1. The p-value in a hypothesis test in statistics tells us how strong the results are. The claim that is kept for experimentation or trial is referred to as the Null Hypothesis.

**Q12. What
is a high and low p-value?**

A low p-value, that is, a p-value less than or equal to 0.05, indicates the strength of the results against the Null Hypothesis, indicating that the Null Hypothesis can be rejected.

A high p-value, that is, a p-value greater than 0.05, indicates the strength of the results in favor of the Null Hypothesis, which means that the Null Hypothesis can be accepted.

**Q13. Is
TensorFlow the most popular deep learning library?**

Tensorflow is a well-known deep learning library as it includes C++ and Python APIs, making it much easier to work with. TensorFlow also has a faster compilation speed than Keras and Torch (two other well-known deep learning libraries). Tensorflow also supports both GPU and CPU computing devices. As a result, it is a huge success and a very popular deep learning library.

The work of data scientists is not easy, but it is
rewarding, and there are many open positions. These **data
scientist interview questions** will get you one step closer to landing
your dream job. Prepare for the interviews by staying up-to-date on the nuts
and bolts of data science.

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