Date: 18-07-2023
Company Name - Genpact
Role: Data Scientist
Topics: euclidian distance, normalisation vs standardization, ensemble learning, batch vs SGD, root cause analysis


Q. How will you measure the Euclidean distance between the two arrays in NumPy?

A. The magnitude or length of the line segment between two locations is known as the Euclidean distance in mathematics. We first create two numpy arrays in the first procedure. The Euclidean distance is then computed directly using numpy's linalg.norm() function.


Q. Difference between Normalisation and Standardization?

A. Normalization is the process of rescaling values into a range of [0,1]. Typically, standardisation entails rescaling data to a mean of 0 and a standard deviation of 1. (unit variance).


Q. What is Ensemble Learning?

A. Ensemble learning is the process of systematically generating and combining many models, such as classifiers or experts, to tackle a specific computational intelligence problem. Ensemble learning is largely used to improve a model's performance (classification, prediction, function approximation, etc.) or to lessen the risk of an unintentional poor model selection.


Q. Difference between Batch and Stochastic Gradient Descent?

A. Batch Gradient Descent is highly slow on very big training sets since it entails calculations over the entire training set at each step. As a result, Batch GD becomes extremely computationally expensive. SGD is stochastic in nature, which means it chooses up a "random" instance of training data at each step and then computes the gradient, which is significantly faster than Batch GD because there is much less data to modify at once.


Q. What is root cause analysis?

A. A root cause is a component that contributed to a nonconformance and should be eradicated permanently through process improvement. The root cause is the most fundamental problem—the most fundamental reason—that puts in motion the entire cause-and-effect chain that leads to the problem (s). Root cause analysis (RCA) is a word that refers to a variety of approaches, tools, and procedures used to identify the root causes of problems. Some RCA approaches are more directed toward uncovering actual root causes than others, while others are more general problem-solving procedures, and yet others just provide support for the root cause analysis core activity.

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Date: 25-07-2023
Company: Adobe
Role: Data Scientist
Topics: overfitting, underfitting, feature scaling, linear model, logistic function


1.What are the conditions for Overfitting and Underfitting?

Ans:
• In Overfitting the model performs well for the training data, but for any new data it fails to provide output. For Underfitting the model is very simple and not able to identify the correct relationship. Following are the bias and variance conditions.

• Overfitting – Low bias and High Variance results in the overfitted model. The decision tree is more prone to Overfitting.

• Underfitting – High bias and Low Variance. Such a model doesn’t perform well on test data also. For example – Linear Regression is more prone to Underfitting.


2. Which models are more prone to Overfitting?

Ans: Complex models, like the Random Forest, Neural Networks, and XGBoost are more prone to overfitting. Simpler models, like linear regression, can overfit too – this typically happens when there are more features than the number of instances in the training data.


3. When does feature scaling should be done?

Ans: We need to perform Feature Scaling when we are dealing with Gradient Descent Based algorithms (Linear and Logistic Regression, Neural Network) and Distance-based algorithms (KNN, K-means, SVM) as these are very sensitive to the range of the data points.


4. What is a logistic function? What is the range of values of a logistic function?

Ans. f(z) = 1/(1+e -z )
The values of a logistic function will range from 0 to 1. The values of Z will vary from -infinity to +infinity.


5. What are the drawbacks of a linear model?

Ans. There are a couple of drawbacks of a linear model:

A linear model holds some strong assumptions that may not be true in application. It assumes a linear relationship, multivariate normality, no or little multicollinearity, no auto-correlation, and homoscedasticity
A linear model can’t be used for discrete or binary outcomes.
You can’t vary the model flexibility of a linear model.

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Date: 30-07-2023
Company Name - Deloitte
Role - Data Scientist
Topics: Missing Values, Hypothesis Testing, Decision Trees, Central Limit Theorem, Overfitting vs Underfitting, Imbalanced data


1. You are given a data set consisting of variables with more than 30 percent missing values. How will you deal with them?
Ans.
If the data set is large, we can just simply remove the rows with missing data values. It is the quickest way, we use the rest of the data to predict the values.
For smaller data sets, we can substitute missing values with values like mean, mode, forward or backward fill. There are different ways to do so, such as df.mean(), df.fillna(mean).



Q2. Hypothesis Testing. Null and Alternate hypothesis

Ans. Hypothesis testing is defined as the process of choosing hypotheses for a particular probability distribution, on the basis of observed data Hypothesis testing is simply a core and important topic in statistics.
A null hypothesis is a statistical hypothesis in which there is no significant difference exist between the set of variables. It is the original or default statement, with no effect, often represented by H0 (H-zero). It is always the hypothesis that is tested.

Alternative Hypothesis is a statistical hypothesis used in hypothesis testing, which states that there is a significant difference between the set of variables. It is often referred to as the hypothesis other than the null hypothesis, often denoted by H1 (H-one). The acceptance of alternative hypothesis depends on the rejection of the null hypothesis i.e. until and unless null hypothesis is rejected, an alternative hypothesis cannot be accepted.


Q3. Why use Decision Trees?

Ans. First, a decision tree is a visual representation of a decision situation (and hence aids communication). Second, the branches of a tree explicitly show all those factors within the analysis that are considered relevant to the decision (and implicitly those that are not).


4. What is the difference between observational and experimental data?

Observational data comes from observational studies which are when you observe certain variables and try to determine if there is any correlation.

Experimental data comes from experimental studies which are when you control certain variables and hold them constant to determine if there is any causality.

An example of experimental design is the following: split a group up into two. The control group lives their lives normally. The test group is told to drink a glass of wine every night for 30 days. Then research can be conducted to see how wine affects sleep.


Q5. Central Limit Theorem?

Ans. The central limit theorem states that if you have a population with mean μ and standard deviation σ and take sufficiently large random samples from the population with replacement , then the distribution of the sample means will be approximately normally distributed.


Q6. Over Fitting and Under Fitting

Ans. Overfitting is a modeling error which occurs when a function is too closely fit to a limited set of data points. Underfitting refers to a model that can neither model the training data nor generalize to new data.


Q7. how to deal with imbalance data in classification modelling?

Ans. Follow these techniques:
1.Use the right evaluation metrics.
2. Use K-fold Cross-Validation in the right way.
3. Ensemble different resampled datasets.
4. resample with different ratios.
5. Cluster the abundant class.
6.Design your own models.

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Date: 01-08-2023
Company name: Capgemini
Role: Data Scientist
Topic: adaboost, sliding window, subquery, workbook vs worksheet vs dashboard in tableau, random forest, Naive Bayes

1. What is the AdaBoost Algorithm?
AdaBoost also called Adaptive Boosting is a technique in Machine Learning used as an Ensemble Method. The most common algorithm used with AdaBoost is decision trees with one level that means with Decision trees with only 1 split. These trees are also called Decision Stumps. What this algorithm does is that it builds a model and gives equal weights to all the data points. It then assigns higher weights to points that are wrongly classified. Now all the points which have higher weights are given more importance in the next model. It will keep training models until and unless a lower error is received.

2. What is the Sliding Window method for Time Series Forecasting?

Time series can be phrased as supervised learning. Given a sequence of numbers for a time series dataset, we can restructure the data to look like a supervised learning problem.
In the sliding window method, the previous time steps can be used as input variables, and the next time steps can be used as the output variable.
In statistics and time series analysis, this is called a lag or lag method. The number of previous time steps is called the window width or size of the lag. This sliding window is the basis for how we can turn any time series dataset into a supervised learning problem.


3. What do you understand by sub-queries in SQL?

A subquery is a query inside another query where a query is defined to retrieve data or information back from the database. In a subquery, the outer query is called as the main query whereas the inner query is called subquery. Subqueries are always executed first and the result of the subquery is passed on to the main query. It can be nested inside a SELECT, UPDATE or any other query. A subquery can also use any comparison operators such as >,< or =.


4. Explain the Difference Between Tableau Worksheet, Dashboard, Story, and Workbook?

Tableau uses a workbook and sheet file structure, much like Microsoft Excel.
A workbook contains sheets, which can be a worksheet, dashboard, or a story.
A worksheet contains a single view along with shelves, legends, and the Data pane.
A dashboard is a collection of views from multiple worksheets.
A story contains a sequence of worksheets or dashboards that work together to convey information.


5. How is a Random Forest related to Decision Trees?

Random forest is an ensemble learning method that works by constructing a multitude of decision trees. A random forest can be constructed for both classification and regression tasks.
Random forest outperforms decision trees, and it also does not have the habit of overfitting the data as decision trees do.
A decision tree trained on a specific dataset will become very deep and cause overfitting. To create a random forest, decision trees can be trained on different subsets of the training dataset, and then the different decision trees can be averaged with the goal of decreasing the variance.


6. What are some disadvantages of using Naive Bayes Algorithm?

Some disadvantages of using Naive Bayes Algorithm are:
It relies on a very big assumption that the independent variables are not related to each other.
It is generally not suitable for datasets with large numbers of numerical attributes.
It has been observed that if a rare case is not in the training dataset but is in the testing dataset, then it will most definitely be wrong.

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Date - 06-08-2023
Company name: Walmart
Role: Data Scientist
Topic: kmean, cnn layers, constraint in SQL, tableau, NLP, bagging and boosting

1. Explain some cases where k-Means clustering fails to give good results

k-means has trouble clustering data where clusters are of various sizes and densities.
Outliers will cause the centroids to be dragged, or the outliers might get their own cluster instead of being ignored. Outliers should be clipped or removed before clustering.
If the number of dimensions increase, a distance-based similarity measure converges to a constant value between any given examples. Dimensions should be reduced before clustering them.


2. Explain the different layers in CNN.

The different layers involved in the architecture of CNN are as follows:
1. Input Layer: The input layer in CNN should contain image data. Image data is represented by a three-dimensional matrix. We have to reshape the image into a single column.
For Example, Suppose we have an MNIST dataset and you have an image of dimension 28 x 28 =784, you need to convert it into 784 x 1 before feeding it into the input. If we have “k” training examples in the dataset, then the dimension of input will be (784, k).
2. Convolutional Layer: To perform the convolution operation, this layer is used which creates several smaller picture windows to go over the data.
3. ReLU Layer: This layer introduces the non-linearity to the network and converts all the negative pixels to zero. The final output is a rectified feature map.

3. What Would You Do If Some Countries/Provinces (Any Geographical Entity) are Missing and Displaying a Null When You Use Map View in Tableau?

When working with maps and geographical fields, unknown or ambiguous locations are identified by the indicator in the lower right corner of the view.
Click the indicator and choose from the following options:
Edit Locations - correct the locations by mapping your data to known locations
Filter Data - exclude the unknown locations from the view using a filter. The locations will not be included in calculations
Show Data at Default Position - show the values at the default position of (0, 0) on the map.

4. What are Constraints in SQL?
Constraints are used to specify the rules concerning data in the table. It can be applied for single or multiple fields in an SQL table during the creation of the table or after creating using the ALTER TABLE command. The constraints are:
NOT NULL - Restricts NULL value from being inserted into a column.
CHECK - Verifies that all values in a field satisfy a condition.
DEFAULT - Automatically assigns a default value if no value has been specified for the field.
UNIQUE - Ensures unique values to be inserted into the field.
INDEX - Indexes a field providing faster retrieval of records.
PRIMARY KEY - Uniquely identifies each record in a table.
FOREIGN KEY - Ensures referential integrity for a record in another table.

5. What is Bag of Words in NLP?

Bag of Words is a commonly used model that depends on word frequencies or occurrences to train a classifier. This model creates an occurrence matrix for documents or sentences irrespective of its grammatical structure or word order.


6. What is the difference between bagging and boosting?

Bagging is a homogeneous weak learners’ model that learns from each other independently in parallel and combines them for determining the model average. Boosting is also a homogeneous weak learners’ model but works differently from Bagging. In this model, learners learn sequentially and adaptively to improve model predictions of a learning algorithm

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Date: 16-08-2023
Company name: Wipro
Role: Data Analyst
Topic: concurrency, checkpoint, groups, nested if, recursive stored procedure

1. What is concurrency control in DBMS?
This is a process of managing simultaneous operations in a database so that database integrity is not compromised. The following are the two approaches involved in concurrency control:
Optimistic approach – Involves versioning
Pessimistic approach – Involves locking

2. What is a checkpoint in DBMS and when does it occur?
A checkpoint is a mechanism where all the previous logs are removed from the system and are permanently stored on the storage disk. So, basically, checkpoints are those points from where the transaction log record can be used to recover all the committed data up to the point of crash.

3. What are groups in Tableau?
A group is a combination of dimension members that make higher level categories. For example, if you are working with a view that shows average test scores by major, you may want to group certain majors together to create major categories.

4. How are nested IF statements used in Excel?
The function IF() can be nested when we have multiple conditions to meet. The FALSE value in the first IF function is replaced by another IF function to make a further test.

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A stored procedure that calls itself until a boundary condition is reached, is called a recursive stored procedure. This recursive function helps the programmers to deploy the same set of code several times as and when required.

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