Introduction
Data science has become one of the most sought-after and lucrative career paths. Data scientists play a crucial role in extracting insights from vast amounts of data, enabling businesses to make informed decisions, predict trends, and solve complex problems. With demand skyrocketing across various industries, many professionals are aspiring to enter this exciting field, but securing a data scientist position requires thorough preparation and a solid understanding of key concepts.
Moreover, there is a prediction that by 2025 the sector of data science in India will grow by 16 billion dollars. So there are plenty of job opportunities for those who are interested in pursuing a Data scientist career. Let’s start with Data scientist interview questions. Moving forward, let’s discuss easy-level data scientist interview questions.
Basic Level Data Scientist Interview Questions
1: What is Data Science?
Answer: Data science is the field of study that uses various techniques, processes and algorithms to extract useful information from structured or unstructured data. These techniques and tools includes statistics, Artificial Intelligence, Machine Learning, and so on. The extracted information is further used across various applications, businesses, industries, and so on.
2: What is the difference between Data Analytics and Data Science?
Answer:
Data Analytics | Data Science |
|---|---|
| Data Analytics has the goal of displaying the precise data of retrieved insights. | Data Science discovers meaningful insights from massive datasets and extracts the finest solutions to resolve the problem. |
| Data Analysts collect, store, handle, analyze maintain the results. | Data Scientists use different algorithms and machine learning principles to improve the ways in which the data support the goals. |
3: What are the differences between supervised and unsupervised learning?
Answer:
Supervised Learning | Unsupervised Learning |
|---|---|
| It is used for labeled data. | It is used for unlabelled data. |
| It predicts the output. | It finds the hidden patterns in the data. |
| It takes direct feedback to check if the output is correct or not. | It does not take into account any such feedback. |
| It is categorized into Classification and Regression problems. | It is categorized into Association and Clustering problems. |
4: What is Machine learning?
Answer: Machine Learning is a subfield of Data science that allows our computers to learn from earlier experiences and predict the output without being explicitly programmed.
5: During sampling, what types of biases can occur?
Answer: In the process of sampling, there are three types of biases. These are as follows.
- Selection Bias: Selection bias occurs whenever the selected sample is not representative of the population.
- Under coverage Bias: Under coverage bias is helpful whenever subpopulations are underrepresented in the sample.
- Survivorship Bias: Survivorship bias occurs whenever the successful members of the population are considered in the study.
6: What does Linear Regression mean?
Answer: Linear regression can be defined as a statistical technique that allows us to understand the relationship between dependent and independent variables. In linear regression, the relationship between two variables is linear which means relationship is expressed as a straight line.
7: What are the drawbacks of a linear model?
Answer: Linear model drawbacks are given below:
- It can not be used for binary and count outcomes.
- In the linear model, there is an overfitting problem. Overfitting is nothing but when the model ignores the bigger picture and only sets for a very small amount of data.
8: What do you understand by the term Logistic regression?
Answer: Logistic regression can be defined as a statistical technique that allows us to understand the relationship between binary dependent and independent variables. It uses logistic function to model a relation between the binary variables. Logistic regression is also known as the logit model.
9: What are the sampling techniques?
Answer: There are two types of sampling techniques:
Probability Sampling Techniques: The probability sampling technique is helpful in research studies. It ensures that the sample must be representative of the population.
Non-Probability Sampling Techniques: The non-probability sampling technique is helpful when obtaining the list of members of the population is tough or maybe impossible.
10: When does Bias occur?
Answer: Bias can occur during data processing, developing, and deploying models. It can also occur during model evaluation.
Moving forward, let’s discuss medium-level Data Scientist interview questions.
11: How are skewed and uniform distributions different from each other?
Answer: When the data is neither symmetric nor normal and distributed on one side of the plot, then a skewed distribution occurs. On the other hand, uniform distribution occurs when the data is equally distributed.
12: What do you mean by cluster sampling technique?
Answer: Cluster sampling is also known as Multi-stage sampling. It is used to study large, spread-out populations. This technique divides the population into clusters(groups). Then, any random cluster is picked and considered for the study. After this, the individuals from clusters are then included in the sample.
13: What do you mean by a decision tree?
Answer: In Data science, Decision trees are non-parametric supervised algorithms. It is utilized for classification as well as regression. Decision trees consist of root nodes, leaf nodes, and branches. It has a hierarchical tree structure.
14: What do you mean by Eigenvector?
Answer: To understand linear transformation, we can use eigenvectors. For the covariance matrix, we calculate the eigenvectors. Eigenvectors are unit vectors. Unit vectors means that their magnitudes and length is equal to 1.
15: What is Back Propagation?
Answer: Backpropagation or backward propagation is a generally used algorithm for training feedforward artificial neural networks. For improving the accuracy of predictions in data mining, Back propagation plays an important role as a mathematical tool.
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