Data science is an interdisciplinary field that mines raw data, analyses it, and creates patterns from which useful insights can be extracted. Data science is built on a foundation of statistics, computer science, machine learning, deep learning, data analysis, data visualization, and a variety of other technologies.
Here is atop data science interview questions for freshers
1. What exactly does the term “Data Science” imply?
Data Science is an interdisciplinary field that includes a wide range of scientific procedures, algorithms, tools, and machine learning methods. Using statistical and mathematical analysis, they collaborate to find common patterns and extract relevant insights from raw input data.
If you want to learn data analysis, there are plenty of resources available online. You can find paid courses, data analysis scholarships and even boot camps that will teach you everything you need to know.
2. What are some of the sampling procedures used? What is the primary benefit of sampling?
When dealing with enormous datasets, data analysis cannot be done on the entire volume of data at once. It’s critical to collect some data samples that may be used to represent the entire population and then analyze them. While doing so, it’s critical to carefully select sample data from the massive data collection that accurately represents the complete dataset.
Based on the use of statistics, there are primarily two types of sampling techniques:
- Probability Sampling techniques: Clustered sampling, simple random sampling, and stratified sampling.
- Non-Probability Sampling techniques: Convenience sampling, Quota sampling, snowball sampling, and so on are all examples.
3. Make a list of the overfitting and underfitting conditions.
Overfitting: Only for the sample training data does the model perform well. If the model is given any fresh data as input, it fails to produce any results. These conditions arise as a result of the model’s low bias and high variance.
Underfitting: The model is so simplistic in this case that it is unable to recognize the correct relationship in the data, and as a result, it fails to perform well even on test data. This can happen when there is a lot of bias and little variance. Underfitting is more common in linear regression.
4. When is resampling done?
Resampling is a data sampling technique that improves accuracy and quantifies the uncertainty of population parameters. It is done to ensure that variances are managed by checking if the model is adequate by training it on diverse patterns in a dataset. It’s also done when models need to be validated with random subsets or when doing tests with labels substituted on data points.
5. What do you understand by Imbalanced Data?
When data is spread unequally across several categories, it is said to be highly unbalanced. These datasets lead to model mistakes as well as performance concerns.
6. Is there a distinction between the expected and mean values?
Although there aren’t many variations between these two, it’s worth noting that they’re employed in different situations. In general, the mean value refers to the probability distribution, whereas the anticipated value is used when dealing with random variables.
7. What do you understand by Survivorship Bias?
This prejudice refers to the logical fallacy of focusing on components that survived a process while ignoring those that did not due to a lack of prominence. This bias can lead to incorrect conclusions being drawn.
8. Define the terms key performance indicators (KPIs), Lift, Robustness, Model fitting, and DOE.
- KPI: KPI is an acronym for Key Performance Indicator, which is a metric that assesses how well a corporation achieves its objectives.
- Lift: Lift is a metric that compares the performance of the target model to that of a random choice model. The lift indicates how well the model predicts when compared to no model.
- Model fitting: is a metric for determining how well a model matches the data.
- Robustness: This refers to the system’s ability to deal with changes and variances effectively.
- DOE: stands for the design of experiments, and it refers to the task of describing and explaining information variance under postulated settings to reflect factors.
9. Define confounding variables.
Confounders are another term for confounding variables. These variables are a type of auxiliary variable that affects both the independent and dependent variables. Making erroneous linkages and mathematical correlations between variables that are not related by chance.
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10. What is Linear Regression? What are some of the linear model’s key drawbacks?
Linear regression is a technique in which the value of a predictor variable X is used to predict the value of a variable Y. The criteria variable is referred to as Y. The following are some of Linear Regression’s disadvantages:
- A key flaw is an assumption that errors are linear.
- It isn’t suitable for binary outcomes. For that, we have Logistic Regression.
- There are overfitting issues that cannot be resolved.
Conclusion
Data Science is a broad area that includes topics such as data mining, data analysis, data visualization, machine learning, deep learning, and, most crucially, mathematical principles such as linear algebra and statistical analysis. Because becoming a good professional Data Scientist requires a lot of prerequisites, the rewards and benefits are substantial. These days, a data scientist is the most sought-after job title.
