18 Questions, that were asked in a lot of data science interviews
What exactly is SQL?
SQL (Structured Query Language) is a programming language for manipulating querying data in databases. SQL can be used to insert, delete, and update data in a database, as well as retrieve data from it.
In SQL, how would you compute the median?
In SQL, the PERCENTILE CONT() function is used to calculate the median. This function takes two arguments: the column name for which the median is to be calculated and the value 0.5 (which corresponds to the median).
To calculate the median salary for all employees in a table, for example, use the following query:
SELECT
AS median salary PERCENTILE CONT(0.5) WITHIN GROUP (ORDER BY salary)
FROM WORKERS;
What exactly is a decision tree?
A decision tree is a machine-learning model that predicts the value of a target variable. Splitting the data set into smaller and smaller subsets until each subset contains only one data point yields decision trees.
Describe how you would use a decision tree to predict whether or not a customer will churn.
To use a decision tree to predict whether or not a customer will churn, you must train the model on data that includes information about previous churners. After training the model, you can use it to predict whether or not new customers will churn.
What exactly is gradient boosting?
Gradient boosting is a machine learning algorithm used to increase the accuracy of a machine learning model. Gradient boosting works by first training a series of weak models and then combining their predictions to produce a final prediction.
How do you ensure the quality of your data?
Answer: Quality assurance in data science is a process that involves multiple steps, such as data cleaning, data validation, and data integration. I use various techniques to ensure data quality, including data visualization, statistical analysis, and machine learning algorithms.
Can you explain a difficult data analysis challenge you have faced and how you overcame it?
Answer: [Provide an example of a difficult data analysis challenge you have faced and how you overcame it, such as dealing with large and complex datasets, solving data integrity problems, or handling missing data]. Explain the steps you took to address the problem, the techniques you used to analyze the data, and the results you achieved.
How do you select a model for a given problem?
Answer: Choosing the right model for a given problem depends on the nature of the data and the requirements of the problem. Typically, I start by evaluating different types of models and their performance on a sample of the data. Then I use techniques like cross-validation to measure the generalization of the chosen model. After that, I experiment with different parameters and evaluate the model with metrics like accuracy, precision, and recall.
Can you explain a specific data visualization you have created and the insights it provided?
Answer: [Provide an example of a specific data visualization you have created, such as a heatmap, scatter plot, or bar chart, and the insights it provided]. Explain the data you were visualizing, the tools you used to create the visualization, and the key insights you were able to draw from it.
How well do you understand the data science process?
Answer: The data science process typically includes steps such as problem definition, data collection and cleaning, data exploration, model building and evaluation, and data deployment.
What is your background in data visualization?
Answer: I have experience creating visualizations such as line charts, bar charts, histograms, scatter plots, and heatmaps using various data visualization tools such as Matplotlib, Seaborn, and Tableau. I recognize the significance of data visualization in gaining insights and communicating results.
What is your background in machine learning algorithms?
Answer: I've used a variety of machine learning algorithms, including supervised learning techniques like linear and logistic regression, decision trees, and random forests, as well as unsupervised learning techniques like clustering and dimensionality reduction. I am familiar with the advantages and drawbacks of various machine learning algorithms, in addition to the best applications for each.
How should missing data in a dataset be handled?
Answer: There are several approaches to dealing with missing data, including replacing missing values with the mean or median of the data, employing machine learning algorithms that can handle missing data, such as random forests, and dropping the rows or columns containing missing data. The best method depends on the dataset and the nature of the missing data.
Can you describe a specific data analysis you carried out and the insights it provided?
Answer: [Explain a specific data analysis you completed and the insights it provided, such as a time series analysis of sales data or a regression analysis of customer demographics]. Explain the data you were analyzing, the techniques you used, and the key insights you gained from the analysis.
How do you choose a model for a specific problem?
Answer: I usually start by choosing a few models that are appropriate for the problem at hand and evaluating their performance using metrics like accuracy and precision. The model is then optimized using techniques such as cross-validation and hyperparameter tuning.
How do you assess the performance of a model?
Answer: Metrics such as accuracy, precision, recall, and F1 score can be used to assess the model's performance. I also use various visualization techniques to evaluate the model, such as the confusion matrix and the ROC Curve. In addition, I evaluate the model's performance using techniques such as cross-validation, k-fold, and the holdout method.
How should a data-driven problem be approached?
Answer: I approach a data-driven problem by first understanding it, then gathering data, cleaning and pre-processing the data, and finally exploring the data to find patterns and insights. Following that, I begin building the models and fine-tuning them to improve accuracy. Finally, I assess the models and implement the solution.