Frequently Asked Data Science Interview Questions
Frequently Asked Data Science Interview Questions, We think the hiring managers aren’t trying to find the right answers.
They want to assess your professional background, technological expertise, and critical thinking. Additionally, they are looking for data scientists that are knowledgeable in both the technical and business aspects.
Frequently Asked Data Science Interview Questions
The 12 trickiest data science interview questions and their responses have been compiled by us.
To cover all the bases, they are grouped into three categories: situational, data analysis, and machine learning.
Situational
1) What was the most difficult data science project you ever completed?
You don’t need to think too much. The recruiting manager is evaluating your abilities to manage challenging assignments.
The project name and a succinct description should come first. After that, describe why it was difficult and how you overcame it. It is all about the specifics, equipment, procedures, language, creative thinking, and commitment.
Reviewing your last five projects is a useful practice before attending an interview. The discussion points, business use cases, technologies, and data science approaches will all be covered.
2) How will you determine whether a random dataset meets the demands of the business if we give it to you?
It is a partial question that can turn off the interviewee. You must request a business use case and more details regarding the baseline metric.
After that, you may begin looking at business use cases and data. You will be describing statistical techniques used to assess the accuracy and dependability of data.
Then, compare it to the business use case and consider how it can enhance current solutions.
Keep in mind that the purpose of this question is to gauge your capacity for critical thought and your comfort with managing random information. Give an explanation of your reasoning and draw a conclusion.
3) How will you use your expertise in machine learning to produce income?
This is a hard issue, so be ready with the numbers and examples of how machine learning has brought in money for various businesses.
Do not worry if you do not understand numbers. There are various approaches to answering this query. Machine learning is utilized in e-commerce recommendation systems, disease diagnosis, multilingual customer support, and stock price predictions.
You must explain to them your area of specialty and how it aligns with the company’s objective. You can suggest fraud detection, growth forecasting, threat detection, and policy suggestion systems if they are a fintech company.
Data Analysis
4) Why do we use A/B Testing?
Statistical hypothesis testing for randomized studies with two variables, A and B, is known as A/B testing. It is mostly employed in user experience research, which involves comparing user responses to two different product versions.
It is used in data science to test different machine learning models when creating and analyzing data-driven solutions for a business.
5) Create a SQL query that lists all orders together with the customers’ details.
Your interviewers will give you further details on database tables, such as the fact that the Orders table has ID, CUSTOMER, and VALUE fields and the Customers table has ID and Name data fields.
In order to display ID, Name as Customer Name, and VALUE, we will link two tables based on the ID and CUSTOMER columns.
SELECT a.ID, a.Name as Customer Name, b.VALUE FROM Customers as a LEFT JOIN Orders as b ON a.ID = b.CUSTOMER
The aforementioned illustration is pretty straightforward. To pass the interview round, you need to be ready for a challenging SQL query.
6) What are Markov chains?
Markov Chains are a probabilistic way of changing from one state to another. Based on the current condition and the amount of time that has passed, determine the likelihood of changing to a future state.
Search engines, speech recognition, and information theory all use the Markov Chain. Read the wiki page to find out more.
7) How should anomalous values be handled?
Dropping outliers as they influence the overall data analysis is the straightforward method. Make sure your dataset is huge and the values you are eliminating are invalid before you do that, though.
In addition, you can:
Data normalization
use StandardScaler or MinMaxScaler
Utilize algorithms, like random forests, that are not impacted by outliers.
8) What is TF-IDF?
The term frequency-inverse document frequency of records, or TF-IDF, is used to determine a word’s importance within a corpus or sequence of texts.
Each term in a document or corpus is evaluated for value as part of the text indexing process. It is frequently used for text vectorization, which involves turning a word or phrase into a number and using it for NLP (Natural Language Processing) activities.
9) What is the difference between error and residual?
The discrepancy between the actual value and its theoretical value is known as an error. It frequently refers to the unseen value produced by the DGP (Data Generating Process)
The residual is the discrepancy between the value that was seen and the value that a model anticipated.
10) Do gradient descent methods always converge to similar points?
No, never. At local minima or maxima points, it can easily become stuck. If there are several local optima, the data and initial conditions will determine how quickly they all converge. Global minima are challenging to achieve.
11) What is the Time Series Forecasting Sliding Window Method?
The lag method, also known as the sliding window method, uses the previous time steps as inputs and the following time step as an output.
The number of prior steps, or the window width, affects the preceding steps. For univariate forecasting, the sliding window approach is well known.
A time series dataset is transformed into a supervised learning challenge.
For instance, suppose the window width is three and the sequence is [44,95,102,108,130,140,160,190,220,250,300,400].
The result will resemble
X y 44,95,102 108 96,105,108 130 105,108,130 140 108,130,140 160 … …
12) How do you prevent your model from becoming overfit?
When your model succeeds on the train and validation datasets but fails on the unknown test dataset, this is known as overfitting.
It can be avoided by:
maintaining a basic model, Don’t prepare for lengthier Epocs, Enhancement engineering, applying cross-validation methods, Employing regularisation strategies, and Model assessment with Shap.
