Lead Data Scientist Interview Questions & Answers

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It is a traditional database schema with a central table. Satellite tables map IDs to physical names or descriptions and can be connected to the central fact table using the ID fields; these tables are known as lookup tables and are principally useful in real-time applications, as they save a lot of memory. Sometimes star schemas involve several layers of summarization to recover information faster.

Recommender systems are a subclass of information filtering systems that are meant to predict the preferences or ratings that a user would give to a product.

A subclass of information filtering systems that are meant to predict the preferences or ratings that a user would give to a product. Recommender systems are widely used in movies, news, research articles, products, social tags, music, etc.

Before the interview, do your homework on the analytics environment that the interviewing company uses. During the IT interview, you will be asked to review your technical competencies and skillsets. How well the company feels your technical skills fit with the data analytics approaches and tools they use in their environment can have a make-or-break effect on whether you get the job.

The process of filtering used by most recommender systems to find patterns and information by collaborating perspectives, numerous data sources, and several agents.

Eigenvectors are for understanding linear transformations. In data analysis, we usually calculate the eigenvectors for a correlation or covariance matrix. Eigenvalues are the directions along which a particular linear transformation acts by flipping, compressing or stretching.

Selection bias occurs when sample obtained is not represantative of the population intended to be analyzed.

No, they do not because in some cases they reach a local minima or a local optima point. You would not reach the global optima point. This is governed by the data and the starting conditions.

It is the logical error of focusing aspects that support surviving some process and casually overlooking those that did not because of their lack of prominence. This can lead to wrong conclusions in numerous different means.

It is a theorem that describes the result of performing the same experiment a large number of times. This theorem forms the basis of frequency-style thinking. It says that the sample mean, the sample variance and the sample standard deviation converge to what they are trying to estimate.

Root cause analysis was initially developed to analyze industrial accidents but is now widely used in other areas. It is a problem-solving technique used for isolating the root causes of faults or problems. A factor is called a root cause if its deduction from the problem-fault-sequence averts the final undesirable event from reoccurring.

When we remove sub-nodes of a decision node, this procsss is called pruning or opposite process of splitting.

Big data doesn't always work as advertised, which is why your IT interviewer will likely probe you about big data setbacks or limits that you've encountered, and ask how you worked through them. Be prepared to answer this question in a straightforward, factual manner, and cap your answer with a discussion of what you gained from the experience and how it benefits you now.

There are four types of kernels in SVM.

☛ Linear Kernel
☛ Polynomial kernel
☛ Radial basis kernel
☛ Sigmoid kernel

You will want to update an algorithm when:

☛ You want the model to evolve as data streams through infrastructure
☛ The underlying data source is changing
☛ There is a case of non-stationarity

This question will determine how the candidate approaches solving real-world issues they will face in their role as a data scientist. It will also determine how they approach problem-solving from an analytical standpoint. This information is vital to understand because data scientists must have strong analytical and problem-solving skills. Look for answers that reveal:

☛ Examples of problem-solving methods
☛ Steps to take to identify the barriers to performance
☛ Benchmarks for assessing performance

This is a statistical hypothesis testing for randomized experiments with two variables, A and B. The objective of A/B testing is to detect any changes to a web page to maximize or increase the outcome of a strategy.

Logistic Regression is also known as the logit model. It is a technique to forecast the binary outcome from a linear combination of predictor variables.

Both Regression and classification machine learning techniques come under Supervised machine learning algorithms. In Supervised machine learning algorithm, we have to train the model using labeled dataset, While training we have to explicitly provide the correct labels and algorithm tries to learn the pattern from input to output. If our labels are discreate values then it will a classification problem, e.g A,B etc. but if our labels are continuous values then it will be a regression problem, e.g 1.23, 1.333 etc.

It has two data structures namely:

Homogeneous data structures–

It contains the same type of objects – Vector, Matrix, and Array.

Heterogeneous data structures–

It contains a different type of objects – Data frames and lists.

Supervised Machine learning:
Supervised machine learning requires training labeled data.

Unsupervised Machine learning:
Unsupervised machine learning doesn’t required labeled data.

Companies understand that they have to train and orient you to their business and technical environments, but they also expect you to bring skills, experience, and fresh ideas to the job.

The end business user and the IT interviewer will be especially interested in your project work. For the IT person, be sure to go into the data quality, analysis, publication, and actionalization processes, covering both the end business and the technical enablement details. For the end business person, review the project from a business results perspective, but avoid using technical jargon unless asked.

• Since R is open source language but still it does not consist of any graphical user interface.
• Also, it easily connects to Excel/Microsoft Office easily. Although, it does not provide any spreadsheet view of data.

☛ Selection bias
☛ Under coverage bias
☛ Survivorship bias

Selection bias is the bias introduced by the selection of individuals, groups or data for analysis in such a way that proper randomization is not achieved, thereby ensuring that the sample obtained is not representative of the population intended to be analyzed. It is sometimes referred to as the selection effect. The phrase “selection bias” most often refers to the distortion of a statistical analysis, resulting from the method of collecting samples. If the selection bias is not taken into account, then some conclusions of the study may not be accurate.