Are you gearing up for a data science interview at Gojek, a leading technology company? Congratulations! To help you ace your interview, we’ve compiled a comprehensive guide with essential data science interview questions and concise, expert-backed answers. Let’s dive in!

Table of Contents

**Machine Learning Model Interview Questions**

**Question:** How do you evaluate the performance of a regression model?

** Answer:** Performance of a regression model is evaluated using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²).

**Question:** What is overfitting, and how can it be prevented?

** Answer:** Overfitting occurs when a model learns noise in the training data and performs poorly on new data. It can be prevented by using cross-validation, regularization techniques (L1, L2), pruning in decision trees, and ensuring the model is not overly complex.

**Question:** What is the purpose of cross-validation?

** Answer:** Cross-validation is used to assess how well a model generalizes to an independent dataset. It involves splitting the data into multiple folds, training the model on some folds, and testing it on the remaining fold, then averaging the results.

**Question:** How does gradient boosting work?

** Answer:** Gradient boosting works by sequentially training models, where each new model tries to correct the errors made by the previous models. It minimizes the loss function by combining the predictions of all models to improve accuracy.

**Question:** Explain the difference between precision and recall.

** Answer:** Precision is the ratio of true positives to the sum of true positives and false positives, indicating the accuracy of positive predictions. Recall is the ratio of true positives to the sum of true positives and false negatives, indicating the ability to identify all positive instances.

**Question:** What is feature selection, and why is it important?

** Answer:** Feature selection involves choosing the most relevant features for model training to improve performance and reduce overfitting. It helps in simplifying the model, reducing training time, and enhancing generalization by eliminating irrelevant or redundant features.

**SQL Interview Questions **

**Question:** Explain the difference between INNER JOIN and LEFT JOIN.

** Answer:** INNER JOIN returns rows with matching values in both tables, while LEFT JOIN returns all rows from the left table and matching rows from the right table. If there is no match, LEFT JOIN returns NULLs for columns from the right table.

**Question:** What is a primary key?

** Answer:** A primary key is a unique identifier for a record in a table. It ensures that each record is unique and cannot be NULL. It is defined using the PRIMARY KEY constraint.

**Question:** What is the purpose of the GROUP BY clause?

** Answer:** The GROUP BY clause groups rows that have the same values in specified columns into aggregate data. It is often used with aggregate functions like COUNT, SUM, AVG, MAX, and MIN.

**Question:** What is a foreign key?

** Answer:** A foreign key is a column or set of columns in one table that references the primary key in another table. It enforces referential integrity by ensuring that values in the foreign key column(s) match values in the referenced table.

**Question:** Explain the difference between UNION and UNION ALL.

** Answer:** UNION combines the result sets of two queries and removes duplicate rows, while UNION ALL combines the result sets and includes all duplicates.

**Question:** What is indexing in SQL, and why is it used?

** Answer:** Indexing is a technique to improve the speed of data retrieval operations on a table. Indexes are created on columns to allow faster searches, queries, and access patterns, at the cost of additional storage and maintenance overhead.

**Statistics concepts Interview Questions**

**Question:** Explain the Central Limit Theorem.

** Answer:** The Central Limit Theorem states that the sampling distribution of the sample mean approaches a normal distribution as the sample size increases, regardless of the population distribution. It allows us to use normal theory statistical methods for making inferences about population means.

**Question:** What is hypothesis testing?

** Answer:** Hypothesis testing is a statistical method used to make inferences about a population based on sample data. It involves formulating a hypothesis, collecting data, and using statistical tests to determine whether the hypothesis should be accepted or rejected.

**Question:** What is the difference between Type I and Type II errors?

*Answer:*

- Type I error: Rejecting a true null hypothesis (false positive).
- Type II error: Failing to reject a false null hypothesis (false negative).

**Question:** Explain the concept of p-value.

** Answer:** The p-value is the probability of obtaining results as extreme as observed (or more extreme) under the assumption that the null hypothesis is true. A lower p-value indicates stronger evidence against the null hypothesis.

**Question:** What is correlation?

** Answer:** Correlation measures the strength and direction of the linear relationship between two variables. It ranges from -1 (perfect negative correlation) to +1 (perfect positive correlation), with 0 indicating no correlation.

**Question:** What is regression analysis used for?

** Answer:** Regression analysis is used to quantify the relationship between one or more predictor variables (independent variables) and a response variable (dependent variable). It helps in predicting and understanding the relationship between variables.

**Question:** Explain the difference between descriptive and inferential statistics.

*Answer:*

- Descriptive statistics: Summarizes and describes data using measures such as mean, median, mode, standard deviation, etc.
- Inferential statistics: Uses sample data to make inferences or generalizations about a population, testing hypotheses and drawing conclusions.

**Question:** What is ANOVA (Analysis of Variance) used for?

** Answer:** ANOVA is used to compare the means of two or more groups to determine whether there are statistically significant differences between them. It partitions the total variation into between-group variation and within-group variation.

**Conclusion**

Preparing for a data science interview at Gojek requires a solid understanding of fundamental concepts, methodologies, and practical applications in data science. Reviewing these key questions and expert answers will help you showcase your skills and readiness during your interview. Best of luck!

Remember, demonstrating not just technical prowess but also a deep understanding of how your skills contribute to solving real-world challenges will set you apart in your data science journey at Gojek.