Top 8 Most Frequently Asked Questions in Data Science Job Interviews

Introduction

Data science is an interdisciplinary domain engaged with raw data mining, analysis, and discovering trends, patterns, and correlations that could be leveraged to derive actionable insights. The elemental cornerstone of data science involves statistics, machine learning, deep learning, data analysis, data visualisation, and several other technologies.

Prominent corporations are persistently on the lookout for talent and competence in this industry. Data Scientists are among the highest-paid IT professionals due to their high demand and limited availability. This article answers how to prepare for data science interviews with some of the most frequently asked, interview questions.

 

 

Top 8 Most Common Questions to Expect in a Data Science Job Interview

 

1. What precisely does the term "Data Science" mean?

Data Science is an interdisciplinary field that consists of numerous research procedures, tools, machine learning approaches, and algorithms that strive to help uncover similar patterns and extract insightful information from provided raw input data through statistical and mathematical analysis. 

 

2. Is data science and data analytics the same? If not, what is the difference?

No, Data Science and data analytics are two distinct domains that diversely leverage data. Here are some critical points of difference between the two fields.

• Data science transforms data via numerous technical analyses to derive valuable insights that data analysts can implement in their business circumstances.
• Data analytics is concerned with testing current hypotheses and facts and providing answers to make effective, data-driven decisions for a business.
• Data Science stimulates innovation by addressing questions that lead to new correlations and solutions to future challenges.
• Data analytics is concerned with extracting current meaning from existing context, whereas data science emphasises predictive modelling.
• Data Science is a more comprehensive discipline that employs diverse mathematical and statistical techniques, algorithms and tools to solve complicated problems. At the same time, data analytics is a specialised domain that uses limited statistics and visualisation tools to deal with some targeted issues.

 

3. How would you define 'bias' in data science?

'Bias' is a type of inconsistency or anomaly that transpires within a model due to using an inadequately powerful algorithm to depict the underlying correlations and trends in the data.
This scenario happens when the input data is too complicated for the algorithms to analyse, culminating in a model based on simplistic hypotheses. The accuracy degrades as a result of the underfitting.

 

4. What precisely is linear regression? What are the key disadvantages of the linear model?

Linear regression is a statistical technique that predicts the value of a criterion variable Y based on the value of a predictor variable X. The following are some of the disadvantages of Linear Regression-

• The assumption of error linearity is a significant disadvantage.
• It cannot be used to produce binary outcomes. 
• There are overfitting issues that cannot be resolved.

 

5. Why is Python used in data science for data cleaning?

Massive data volumes need to be cleaned and transformed into useful formats by data scientists. It is vital to eradicate irrational anomalies, null values, inconsistent formatting, corrupted entries, and other unnecessary data for better outcomes.

Pandas, Matplotlib, Numpy, SciPy, and Keras are some of Python's data cleaning and analysis packages. These libraries are used for loading and cleaning data and executing effective data analysis. These libraries get utilised effectively to load and clean data and perform efficient data analysis. Considering how to learn Python for data science? You can start by taking programming courses for beginners and then progress on to advanced concepts.

 

 

6. Explain the p-value and its significance in the null hypothesis.

P-value is a number that varies from 0 to 1. The p-value in a hypothesis test in statistics helps us determine how reliable the results are. The claim held for experimentation or trial is known as the Null Hypothesis.

• A low p-value (<= 0.05) reflects the strength of the observations against the Null Hypothesis, which signifies that the Null Hypothesis can be rejected.
• A high p-value (>= 0.05) demonstrates the strength of the outcomes in favour of the Null Hypothesis, implying that the Null Hypothesis can be accepted.

 

7. What are the advantages of dimensionality reduction?

Dimensionality reduction is the process of reducing the dimensions and size of an entire dataset. It removes redundant elements while keeping the underlying data and information undisturbed. Data processing speeds up when dimensions get reduced.

The rationale that data with high dimensions are regarded as problematic is that it requires a significant amount of time to analyse the data and train a model. Reducing dimensions expedites the process, reduces noise, and improves model accuracy.

 

8. When does data resampling take place?

Resampling is a method of sampling data to enhance accuracy and measure the variability of population parameters. It is performed to verify the model's effectiveness to handle variations by training the model on different patterns in a dataset. It is also done when models must be validated using random subsets or labelling data points while running tests.

 

Conclusion

The job of data scientists is not simple and there are many open positions where you can contribute. These data science interview questions can help you to prepare for an interview and you can become more confident. So, get ready for the challenges during the recruitment process and try going deep into Data Science to stand out from the crowd. 

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