12 Challenging Data Science Interview Questions
Siddharth K M
1 year ago
Data Science interviews can be a bit challenging for aspiring data scientists. I think the recruiting managers aren't trying to find the correct answers. They aim to evaluate your technical know-how, critical thinking, and professional history. Additionally, they are searching for data scientists knowledgeable on both the technical and commercial aspects.
I have compiled the 12 trickiest data science interview questions and their responses. To cover all the basics, they are divided into three categories—situational, data analysis, and machine learning. You can also join the data science training in Bangalore, which provides interview preparation sessions for aspiring candidates.
You don't need to think too much. The hiring manager is assessing your capacity to handle difficult tasks.
The project name and a brief summary ought to appear first. Then, explain why it was challenging and how you overcame it if everything comes down to the details, tools, processes, terminology, inventiveness, and dedication.
Reviewing your previous five projects is a useful practice before attending an interview.
You must request a business use case and further details on the baseline metric. You'll be outlining statistical methods for evaluating the veracity and reliability of data. Then, compare it to the business use case and consider how it may enhance current solutions.
Remember that the goal of this question is to determine your ability for critical thought and how at ease you are with unstructured material. Give an explanation of your reasoning and draw a conclusion.
This is a hard problem, so be ready with the figures and examples of how machine learning has brought money to various businesses.
If you struggle with math, don't stress. Machine learning is utilized in e-commerce recommendation systems, illness diagnosis, multilingual customer support, and stock price predictions.
You must explain to them how your area of expertise fits with the organization's goal. You can suggest fraud detection, growth forecasting, threat detection, and policy suggestion tools if they are a fintech company.
A/B testing refers to statistical hypothesis testing for randomized experiments with two variables, A and B. It is frequently employed in user experience research, which contrasts customer responses to two various product versions.
It is used in data science to test different machine learning models while creating and analyzing data-driven solutions for a business.
Your interviewers will provide you with further details on database tables, such as the fact that the Orders table includes ID, CUSTOMER, and VALUE fields and the Customers table has ID and Name data fields.
In order to show ID, Name as Customer Name, and VALUE, we will link two tables based on the ID and CUSTOMER columns.
Markov Chains are a probabilistic method of switching between states. The present 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 employ the Markov Chain.
Dropping outliers as they influence the overall data analysis is a straightforward method. Make sure your dataset is huge, and the values you are eliminating are invalid before you do it. The waste indicates that it was a mistaken addition.
In addition, you can:
To assess a word's significance within a corpus or sequence of texts, the technique known as frequency-inverse document frequency of records, or TF-IDF, is utilized. Each word in a document or corpus is evaluated for value as part of the text indexing process. It is commonly employed for text vectorization, the process of converting a word or phrase into a number for use in NLP (Natural Language Processing) operations.
An error is a difference between a value's real and theoretical values. It typically refers to the hidden value that the DGP produces (Data Generating Process)
The difference between the value seen and the value predicted by a model is known as the residual.
No, never. At local minima or maxima locations, it can easily become stuck. The data and beginning circumstances will determine how quickly they all converge if there are several local optima. Global minima are challenging to achieve.
The lag technique, also known as the sliding window method, uses the previous time steps as inputs and the next time step as an output. The number of previous steps or the window's width impacts them. The sliding window method for univariate forecasting is widely known. A supervised learning challenge is created from a time series dataset.
Overfitting occurs when your model performs well on the train and validation datasets but fails on the unidentified test dataset.
It can be avoided by:
I hope this article will be helpful and add value to your career. To master data science tools and techniques and become a certified data scientist, visit the best data science course in Bangalore. Its premium features including domain-specialized training, 15+ real time projects, personal mentorship and job referrals will help you get hired in MAANG companies.
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