Date: 08-02-2025
Company name: Infosys
Role: Data Scientist
01. Can you explain the difference between a left join and a right join in SQL?
Answer- A left join returns all the rows from the left table and the matching rows from the right table. If there are no matching rows in the right table, the result will contain NULL values for those columns. A right join, on the other hand, returns all the rows from the right table and the matching rows from the left table.
02. Explain Categorical data in Pandas?
Answer- Categorical data is a discrete set of values for a particular outcome and has a fixed range. Also,
the data in the category need not be numerical, it can be textual in nature. Examples are
gender, social class, blood type, country affiliation, observation time, etc. There is no hard and
fast rule for how many values a categorical value should have. One should apply one’s domain
knowledge to make that determination on the data sets.
03. What Bayes' Theorem (Bayes Rule) is all about?
Answer- Often, we know how frequently some particular evidence is observed, given a known outcome. We have to use this known fact to compute the reverse, to compute the chance of that outcome happening, given the evidence. Conceptually, this is a way to go from P(Evidence | Known Outcome) to P(Outcome | Known Evidence). Bayes' theorem is a relatively simple, but fundamental result of probability theory that allows for the calculation of certain conditional probabilities. Conditional probabilities are just those probabilities that reflect the influence of one event on the probability of another.
04. What's the difference between Softmax and Sigmoid functions?
Answer-
Softmax function:
- Is used for multi-class classification in logistic regression models, when we have only one right answer or mutually exclusive outputs.
- Its probabilities sum will be 1.
- Is used in different layers of neural networks.
Sigmoid function:
- Is used for multi-label classification in logistic regression models, when we have more than one right answer or non-exclusive outputs.
- Its probability sum does not need to be 1.
- Is used as an activation function while building neural networks.
————————————————————
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Company name: Infosys
Role: Data Scientist
01. Can you explain the difference between a left join and a right join in SQL?
Answer- A left join returns all the rows from the left table and the matching rows from the right table. If there are no matching rows in the right table, the result will contain NULL values for those columns. A right join, on the other hand, returns all the rows from the right table and the matching rows from the left table.
02. Explain Categorical data in Pandas?
Answer- Categorical data is a discrete set of values for a particular outcome and has a fixed range. Also,
the data in the category need not be numerical, it can be textual in nature. Examples are
gender, social class, blood type, country affiliation, observation time, etc. There is no hard and
fast rule for how many values a categorical value should have. One should apply one’s domain
knowledge to make that determination on the data sets.
03. What Bayes' Theorem (Bayes Rule) is all about?
Answer- Often, we know how frequently some particular evidence is observed, given a known outcome. We have to use this known fact to compute the reverse, to compute the chance of that outcome happening, given the evidence. Conceptually, this is a way to go from P(Evidence | Known Outcome) to P(Outcome | Known Evidence). Bayes' theorem is a relatively simple, but fundamental result of probability theory that allows for the calculation of certain conditional probabilities. Conditional probabilities are just those probabilities that reflect the influence of one event on the probability of another.
04. What's the difference between Softmax and Sigmoid functions?
Answer-
Softmax function:
- Is used for multi-class classification in logistic regression models, when we have only one right answer or mutually exclusive outputs.
- Its probabilities sum will be 1.
- Is used in different layers of neural networks.
Sigmoid function:
- Is used for multi-label classification in logistic regression models, when we have more than one right answer or non-exclusive outputs.
- Its probability sum does not need to be 1.
- Is used as an activation function while building neural networks.
————————————————————
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Date: 11-02-2025
Company name: MuSigma
Role: Data Scientist
01. How does a Decision Tree handle continuous(numerical) features?
Answer- Decision Trees handle continuous features by converting these continuous features to a threshold-based boolean feature.
To decide The threshold value, we use the concept of Information Gain, choosing that threshold that maximizes the information gain
02. What are Loss Function and Cost Functions?
Answer- The loss function is to capture the difference between the actual and predicted values for a single record whereas cost functions aggregate the difference for the entire training dataset.
The Most commonly used loss functions are Mean-squared error and Hinge loss.
03. What is the difference between Python Arrays and lists?
Answer- Arrays in python can only contain elements of same data types i.e., data type of array should be homogeneous. It is a thin wrapper around C language arrays and consumes far less memory than lists.
Lists in python can contain elements of different data types i.e., data type of lists can be heterogeneous. It has the disadvantage of consuming large memory.
04. What is root cause analysis? What is a causation vs. a correlation?
Answer- Root cause analysis: a method of problem-solving used for identifying the root cause(s) of a problem Correlation measures the relationship between two variables, range from -1 to 1. Causation is when a first event appears to have caused a second event. Causation essentially looks at direct relationships while correlation can look at both direct and indirect relationships.
————————————————————
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Company name: MuSigma
Role: Data Scientist
01. How does a Decision Tree handle continuous(numerical) features?
Answer- Decision Trees handle continuous features by converting these continuous features to a threshold-based boolean feature.
To decide The threshold value, we use the concept of Information Gain, choosing that threshold that maximizes the information gain
02. What are Loss Function and Cost Functions?
Answer- The loss function is to capture the difference between the actual and predicted values for a single record whereas cost functions aggregate the difference for the entire training dataset.
The Most commonly used loss functions are Mean-squared error and Hinge loss.
03. What is the difference between Python Arrays and lists?
Answer- Arrays in python can only contain elements of same data types i.e., data type of array should be homogeneous. It is a thin wrapper around C language arrays and consumes far less memory than lists.
Lists in python can contain elements of different data types i.e., data type of lists can be heterogeneous. It has the disadvantage of consuming large memory.
04. What is root cause analysis? What is a causation vs. a correlation?
Answer- Root cause analysis: a method of problem-solving used for identifying the root cause(s) of a problem Correlation measures the relationship between two variables, range from -1 to 1. Causation is when a first event appears to have caused a second event. Causation essentially looks at direct relationships while correlation can look at both direct and indirect relationships.
————————————————————
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Date: 13-02-2025
Company name: Numerator
Role: Data Scientist
01. How to remove values to a python array?
Answer- Array elements can be removed using pop() or remove() method. The difference between these two functions is that the former returns the deleted value whereas the latter does not.
02. What are the advantages and disadvantages of views in the database?
Answer- Advantages of Views:
As there is no physical location where the data in the view is stored, it generates output without wasting resources.
Data access is restricted as it does not allow commands like insertion, updation, and deletion.
Disadvantages of Views:
The view becomes irrelevant if we drop a table related to that view.
Much memory space is occupied when the view is created for large tables.
03. What is the lambda function in Python?
Answer- Python Lambda Functions are anonymous function means that the function is without a name. As we already know that the def keyword is used to define a normal function in Python. Similarly, the lambda keyword is used to define an anonymous function in Python.
Eg. lambda_cube = lambda y: y*y*y.
04. Explain the difference between the CART and ID3 Algorithms.
Answer- The CART algorithm produces only binary Trees: non-leaf nodes always have two children (i.e., questions only have yes/no answers).
On the contrary, other Tree algorithms such as ID3 can produce Decision Trees with nodes having more than two children
————————————————————
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Company name: Numerator
Role: Data Scientist
01. How to remove values to a python array?
Answer- Array elements can be removed using pop() or remove() method. The difference between these two functions is that the former returns the deleted value whereas the latter does not.
02. What are the advantages and disadvantages of views in the database?
Answer- Advantages of Views:
As there is no physical location where the data in the view is stored, it generates output without wasting resources.
Data access is restricted as it does not allow commands like insertion, updation, and deletion.
Disadvantages of Views:
The view becomes irrelevant if we drop a table related to that view.
Much memory space is occupied when the view is created for large tables.
03. What is the lambda function in Python?
Answer- Python Lambda Functions are anonymous function means that the function is without a name. As we already know that the def keyword is used to define a normal function in Python. Similarly, the lambda keyword is used to define an anonymous function in Python.
Eg. lambda_cube = lambda y: y*y*y.
04. Explain the difference between the CART and ID3 Algorithms.
Answer- The CART algorithm produces only binary Trees: non-leaf nodes always have two children (i.e., questions only have yes/no answers).
On the contrary, other Tree algorithms such as ID3 can produce Decision Trees with nodes having more than two children
————————————————————
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Date: 15-02-2025
Company name: Ikea
Role: Data Scientist
01. What is the meaning of term weight initialization in neural networks?
Answer- In neural networking, weight initialization is one of the essential factors. A bad weight initialization prevents a network from learning. On the other side, a good weight initialization helps in giving a quicker convergence and a better overall error. Biases can be initialized to zero. The standard rule for setting the weights is to be close to zero without being too small.
02. What is the usage of the NVL() function?
Answer- The NVL() function is used to convert the NULL value to the other value. The function returns the value of the second parameter if the first parameter is NULL. If the first parameter is anything other than NULL, it is left unchanged. This function is used in Oracle, not in SQL and MySQL. Instead of NVL() function, MySQL have IFNULL() and SQL Server have ISNULL() function.
03. How to create a dictionary in Python?
Answer- In Python, a dictionary can be created by placing a sequence of elements within curly {} braces, separated by ‘comma’. Dictionary holds pairs of values, one being the Key and the other corresponding pair element being its Key:value. Values in a dictionary can be of any data type and can be duplicated, whereas keys can’t be repeated and must be immutable.
04. What is matplotlib and some of the basic plots in Matplotlib?
Answer- Matplotlib comes with a wide variety of plots. Plots help to understand trends, patterns, and to make correlations. They’re typically instruments for reasoning about quantitative information. Some of the basic plots are line plot, bar plot, scatter plot, etc.
————————————————————
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Company name: Ikea
Role: Data Scientist
01. What is the meaning of term weight initialization in neural networks?
Answer- In neural networking, weight initialization is one of the essential factors. A bad weight initialization prevents a network from learning. On the other side, a good weight initialization helps in giving a quicker convergence and a better overall error. Biases can be initialized to zero. The standard rule for setting the weights is to be close to zero without being too small.
02. What is the usage of the NVL() function?
Answer- The NVL() function is used to convert the NULL value to the other value. The function returns the value of the second parameter if the first parameter is NULL. If the first parameter is anything other than NULL, it is left unchanged. This function is used in Oracle, not in SQL and MySQL. Instead of NVL() function, MySQL have IFNULL() and SQL Server have ISNULL() function.
03. How to create a dictionary in Python?
Answer- In Python, a dictionary can be created by placing a sequence of elements within curly {} braces, separated by ‘comma’. Dictionary holds pairs of values, one being the Key and the other corresponding pair element being its Key:value. Values in a dictionary can be of any data type and can be duplicated, whereas keys can’t be repeated and must be immutable.
04. What is matplotlib and some of the basic plots in Matplotlib?
Answer- Matplotlib comes with a wide variety of plots. Plots help to understand trends, patterns, and to make correlations. They’re typically instruments for reasoning about quantitative information. Some of the basic plots are line plot, bar plot, scatter plot, etc.
————————————————————
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Date: 26-02-2025
Company name: Adecco
Role: Data Scientist
01. What is a stored procedure?
Answer- Stored Procedure is a function consists of many SQL statements to access the database system. Several SQL statements are consolidated into a stored procedure and execute them whenever and wherever required.
02. What is Dimensionality Reduction?
Answer- In the real world, Machine Learning models are built on top of features and parameters. These features can be multidimensional and large in number. Sometimes, the features may be irrelevant and it becomes a difficult task to visualize them. This is where dimensionality reduction is used to cut down irrelevant and redundant features with the help of principal variables. These principal variables conserve the features, and are a subgroup, of the parent variables.
03. What are Autoencoders?
Answer- An autoencoder is a kind of artificial neural network. It is used to learn efficient data codings in an unsupervised manner. It is utilised for learning a representation (encoding) for a set of data, mostly for dimensionality reduction, by training the network to ignore signal “noise”. Autoencoder also tries to generate a representation as close as possible to its original input from the reduced encoding.
04. What are some common Data Preparation Operations you would use for Time Series Data?
Answer-
a. Parsing time series information from various sources and formats.
b. Generating sequences of fixed-frequency dates and time spans.
c. Manipulating and converting date times with time zone information.
d. Resampling or converting a time series to a particular frequency.
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Company name: Adecco
Role: Data Scientist
01. What is a stored procedure?
Answer- Stored Procedure is a function consists of many SQL statements to access the database system. Several SQL statements are consolidated into a stored procedure and execute them whenever and wherever required.
02. What is Dimensionality Reduction?
Answer- In the real world, Machine Learning models are built on top of features and parameters. These features can be multidimensional and large in number. Sometimes, the features may be irrelevant and it becomes a difficult task to visualize them. This is where dimensionality reduction is used to cut down irrelevant and redundant features with the help of principal variables. These principal variables conserve the features, and are a subgroup, of the parent variables.
03. What are Autoencoders?
Answer- An autoencoder is a kind of artificial neural network. It is used to learn efficient data codings in an unsupervised manner. It is utilised for learning a representation (encoding) for a set of data, mostly for dimensionality reduction, by training the network to ignore signal “noise”. Autoencoder also tries to generate a representation as close as possible to its original input from the reduced encoding.
04. What are some common Data Preparation Operations you would use for Time Series Data?
Answer-
a. Parsing time series information from various sources and formats.
b. Generating sequences of fixed-frequency dates and time spans.
c. Manipulating and converting date times with time zone information.
d. Resampling or converting a time series to a particular frequency.
————————————————————
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Company : Wabtec
Role: Principal Data Scientist - AI Engineering
Exp: Fresher
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Company : Metalbrain Technologies Pvt. Ltd.
Role: Data Scientist
Exp: 1 Years
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Company : Blu Ocean Innovations Pvt. Ltd.
Role: Marketing Data Analyst
Exp: 1+ Years
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Company : Optum
Role: Jr. Analyst- DataOps
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Company : Metalbrain Technologies Pvt. Ltd.
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Date: 04-03-2025
Company name: Capgemini
Role: Data Scientist
01. What is the meaning of KPI in statistics?
Answer- KPI is an acronym for a key performance indicator. It can be defined as a quantifiable measure to understand whether the goal is being achieved or not. KPI is a reliable metric to measure the performance level of an organization or individual with respect to the objectives. An example of KPI in an organization is the expense ratio.
02.What is the concept of graph neural networks in deep learning?
Answer- Graph neural networks are designed to operate on structured data represented as
graphs. They can capture relational dependencies and propagate information across
nodes and edges in a graph. Graph neural networks have been successfully applied to
tasks such as social network analysis, recommendation systems, and molecular chemistry.
03. What is the concept of autoencoders in deep learning?
Answer- Autoencoders are neural networks trained to reconstruct their input data. They consist of
an encoder network that maps the input data to a lower-dimensional representation
(latent space) and a decoder network that reconstructs the input from the latent space.
Autoencoders are used for dimensionality reduction, feature learning, and anomaly
Detection.
04.What is the concept of learning rate in deep learning?
Answer- The learning rate determines the step size at which the weights are updated during
training. Choosing an appropriate learning rate is important, as a small value may result
in slow convergence, while a large value may cause unstable training or overshooting
the optimal solution.
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Company name: Capgemini
Role: Data Scientist
01. What is the meaning of KPI in statistics?
Answer- KPI is an acronym for a key performance indicator. It can be defined as a quantifiable measure to understand whether the goal is being achieved or not. KPI is a reliable metric to measure the performance level of an organization or individual with respect to the objectives. An example of KPI in an organization is the expense ratio.
02.What is the concept of graph neural networks in deep learning?
Answer- Graph neural networks are designed to operate on structured data represented as
graphs. They can capture relational dependencies and propagate information across
nodes and edges in a graph. Graph neural networks have been successfully applied to
tasks such as social network analysis, recommendation systems, and molecular chemistry.
03. What is the concept of autoencoders in deep learning?
Answer- Autoencoders are neural networks trained to reconstruct their input data. They consist of
an encoder network that maps the input data to a lower-dimensional representation
(latent space) and a decoder network that reconstructs the input from the latent space.
Autoencoders are used for dimensionality reduction, feature learning, and anomaly
Detection.
04.What is the concept of learning rate in deep learning?
Answer- The learning rate determines the step size at which the weights are updated during
training. Choosing an appropriate learning rate is important, as a small value may result
in slow convergence, while a large value may cause unstable training or overshooting
the optimal solution.
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Date: 06-03-2025
Company name: IBM
Role: Data Scientist
01. How to create empty tables with the same structure as another table?
Answer- To create empty tables: Using the INTO operator to fetch the records of one table into a new table while setting a WHERE clause to false for all entries, it is possible to create empty tables with the same structure. As a result, SQL creates a new table with a duplicate structure to accept the fetched entries, but nothing is stored into the new table since the WHERE clause is active.
02.Explain Correlation and Covariance?
Answer- Correlation is used for measuring and also for estimating the quantitative relationship between two variables. Correlation measures how strongly two variables are related. Examples like, income and expenditure, demand and supply, etc.
Covariance is a simple way to measure the correlation between two variables. The problem with covariance is that they are hard to compare without normalization.
03. What’s the difference between a generative and discriminative model?
Answer- A generative model will learn categories of data while a discriminative model will simply learn the distinction between different categories of data. Discriminative models will generally outperform generative models on classification tasks.
04.When does regularization become necessary in Machine Learning?
Answer- Regularization is necessary whenever the model begins to overfit/ underfit. It is a cost term for bringing in more features with the objective function. Hence, it tries to push the coefficients for many variables to zero and reduce cost term. It helps to reduce model complexity so that the model can become better at predicting (generalizing).
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Company name: IBM
Role: Data Scientist
01. How to create empty tables with the same structure as another table?
Answer- To create empty tables: Using the INTO operator to fetch the records of one table into a new table while setting a WHERE clause to false for all entries, it is possible to create empty tables with the same structure. As a result, SQL creates a new table with a duplicate structure to accept the fetched entries, but nothing is stored into the new table since the WHERE clause is active.
02.Explain Correlation and Covariance?
Answer- Correlation is used for measuring and also for estimating the quantitative relationship between two variables. Correlation measures how strongly two variables are related. Examples like, income and expenditure, demand and supply, etc.
Covariance is a simple way to measure the correlation between two variables. The problem with covariance is that they are hard to compare without normalization.
03. What’s the difference between a generative and discriminative model?
Answer- A generative model will learn categories of data while a discriminative model will simply learn the distinction between different categories of data. Discriminative models will generally outperform generative models on classification tasks.
04.When does regularization become necessary in Machine Learning?
Answer- Regularization is necessary whenever the model begins to overfit/ underfit. It is a cost term for bringing in more features with the objective function. Hence, it tries to push the coefficients for many variables to zero and reduce cost term. It helps to reduce model complexity so that the model can become better at predicting (generalizing).
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