Date: 27-02-2024
Company name: Elica
Role: Data Scientist
Topic: kmeans, LSTM, SQL, Excel, decision tree

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. If your Time-Series Dataset is very long, what architecture would you use?

If the dataset for time-series is very long, LSTMs are ideal for it because it can not only process single data points, but also entire sequences of data. A time-series being a sequence of data makes LSTM ideal for it.For an even stronger representational capacity, making the LSTM's multi-layered is better.Another method for long time-series dataset is to use CNNs to extract information.

3. What are some common Data Preparation Operations you would use for Time Series Data?

Parsing time series information from various sources and formats.
Generating sequences of fixed-frequency dates and time spans.
Manipulating and converting date times with time zone information.
Resampling or converting a time series to a particular frequency.

4. Describe the Difference Between Window Functions and Aggregate Functions in SQL.

The main difference between window functions and aggregate functions is that aggregate functions group multiple rows into a single result row; all the individual rows in the group are collapsed and their individual data is not shown. On the other hand, window functions produce a result for each individual row. This result is usually shown as a new column value in every row within the window.

5. What is Ribbon in Excel and where does it appear?

The Ribbon is basically your key interface with Excel and it appears at the top of the Excel window. It allows users to access many of the most important commands directly. It consists of many tabs such as File, Home, View, Insert, etc. You can also customize the ribbon to suit your preferences. To customize the Ribbon, right-click on it and select the “Customize the Ribbon” option.

6. Can you mention some advantages and disadvantages of decision trees?

The advantages of decision trees are that they are easier to interpret, are nonparametric and hence robust to outliers, and have relatively few parameters to tune.
On the other hand, the disadvantage is that they are prone to overfitting.

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Date: 01-03-2024
Company Name - Nestle
Role: Data Analyst
Topic - SQL and Tableau

1. Explain character-manipulation functions? Explains its different types in SQL.

Change, extract, and edit the character string using character manipulation routines. The function will do its action on the input strings and return the result when one or more characters and words are supplied into it.

The character manipulation functions in SQL are as follows:

A) CONCAT (joining two or more values): This function is used to join two or more values together. The second string is always appended to the end of the first string.

B) SUBSTR: This function returns a segment of a string from a given start point to a given endpoint.

C) LENGTH: This function returns the length of the string in numerical form, including blank spaces.

D) INSTR: This function calculates the precise numeric location of a character or word in a string.

E) LPAD: For right-justified values, it returns the padding of the left-side character value.

F) RPAD: For a left-justified value, it returns the padding of the right-side character value.

G) TRIM: This function removes all defined characters from the beginning, end, or both ends of a string. It also reduced the amount of wasted space.

H) REPLACE: This function replaces all instances of a word or a section of a string (substring) with the other string value specified.


2. How Do You Calculate the Daily Profit Measures Using LOD?

LOD expressions allow us to easily create bins on aggregated data such as profit per day.

Scenario: We want to measure our success by the total profit per business day.

Create a calculated field named LOD - Profit per day and enter the formula:

FIXED [Order Date] : SUM ([Profit])

Create another calculated field named LOD - Daily Profit KPI and enter the formula:

IF [LOD - Profit per day] > 2000 then “Highly Profitable.”

ELSEIF [LOD - Profit per day] <= 0 then “Unprofitable”

ELSE “Profitable”

END

To calculate daily profit measure using LOD, follow these steps to draw the visualization:

Bring YEAR(Order Date) and MONTH(Order Date) to the Columns shelf
Drag Order Id field to Rows shelf. Right-click on it, select Measure and click on Count(Distinct)
Drag LOD - Daily Profit KPI to the Rows shelf
Bring LOD - Daily Profit KPI to marks card and change mark type from automatic to area.

3. What are Superkey and candidate key?

A super key may be a single or a combination of keys that help to identify a record in a table. Know that Super keys can have one or more attributes, even though all the attributes are not necessary to identify the records.

A candidate key is the subset of Superkey, which can have one or more than one attributes to identify records in a table. Unlike Superkey, all the attributes of the candidate key must be helpful to identify the records.

Note that all the candidate keys can be Super keys, but all the super keys cannot be candidate keys.


4.What is Database Cardinality?

Database Cardinality denotes the uniqueness of values in the tables. It supports optimizing query plans and hence improves query performance. There are three types of database cardinalities in SQL, as given below:

Higher Cardinality
Normal Cardinality
Lower Cardinality

🚀Data Science Interview Questions asked in Verizon

1. How many cars are there in Chennai? How do u structurally approach coming up with that number?
2. Multiple Linear Regression?
3. OLS vs MLE?
4. R2 vs Adjusted R2? During Model Development which one do we consider?
5. Lift chart, drift chart
6. Sigmoid Function in Logistic regression
7. ROC what is it? AUC and Differentiation?
8. Linear Regression from Multiple Linear Regression
9. P-Value what is it and its significance? What does P in P-Value stand for? What is Hypothesis Testing? Null hypothesis vs Alternate Hypothesis?
10. Bias Variance Trade off?
11. Over fitting vs Underfitting in Machine learning?
12. Estimation of Multiple Linear Regression
13. Forecasting vs Prediction difference? Regression vs Time Series?
14. p,d,q values in ARIMA models
1. What will happen if d=0
2. What is the meaning of p,d,q values?
15. Is your data for Forecasting Uni or multi-dimensional?
16. How to find the nose to start with in a Decision tree.
17. TYPES of Decision trees - CART vs C4.5 vs ID3
18. Genie index vs entropy
19. Linear vs Logistic Regression
20. Decision Trees vs Random Forests
21. Questions on liner regression, how it works and all
22. Asked to write some SQL queries
23. Asked about past work experience
24. Some questions on inferential statistics (hypothesis testing, sampling techniques)
25. Some questions on table (how to filter, how to add calculated fields etc)
26. Why do u use Licensed Platform when other Open source packages are available?
27. What certification Have u done?
28. What is a Confidence Interval?
29. What are Outliers? How to Detect Outliers?
30. How to Handle Outliers?

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Date: 05-03-2024
Company name: Numerator
Role: Data Scientist
Topic : RNN, pop & remove, calculated field, SQL, Excel

1. What are the uses of using RNN in NLP?

The RNN is a stateful neural network, which means that it not only retains information from the previous layer but also from the previous pass. Thus, this neuron is said to have connections between passes, and through time.
For the RNN the order of the input matters due to being stateful. The same words with different orders will yield different outputs.
RNN can be used for unsegmented, connected applications such as handwriting recognition or speech recognition.

2. How to remove values to a python array?

Ans: 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.

3. 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.

4. Describe the Difference Between Window Functions and Aggregate Functions in SQL.

The main difference between window functions and aggregate functions is that aggregate functions group multiple rows into a single result row; all the individual rows in the group are collapsed and their individual data is not shown. On the other hand, window functions produce a result for each individual row. This result is usually shown as a new column value in every row within the window.

5. What is Ribbon in Excel and where does it appear?

The Ribbon is basically your key interface with Excel and it appears at the top of the Excel window. It allows users to access many of the most important commands directly. It consists of many tabs such as File, Home, View, Insert, etc. You can also customize the ribbon to suit your preferences. To customize the Ribbon, right-click on it and select the “Customize the Ribbon” option.

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Interview QnA | Date: 09-03-2024
Company name: Fractal Analytics
Role: Data Scientist
Topic : Machine learning, deep learning

1.What is the difference between Deep Learning and Machine Learning?

Deep Learning allows machines to make various business-related decisions using artificial neural networks that simulate the human brain, which is one of the reasons why it needs a vast amount of data for training. Machine Learning gives machines the ability to make business decisions without any external help, using the knowledge gained from past data. Machine Learning systems require relatively small amounts of data to train themselves, and most of the features need to be manually coded and understood in advance.

2.What is Cross-validation in Machine Learning?

Cross-validation allows a system to increase the performance of the given Machine Learning algorithm. This sampling process is done to break the dataset into smaller parts that have the same number of rows, out of which a random part is selected as a test set and the rest of the parts are kept as train sets. Cross-validation consists of the following techniques:
•Holdout method
•K-fold cross-validation
•Stratified k-fold cross-validation
•Leave p-out cross-validation

3.What is Epoch in Machine Learning?

Epoch in Machine Learning is used to indicate the count of passes in a given training dataset where the Machine Learning algorithm has done its job. Generally, when there is a large chunk of data, it is grouped into several batches. All these batches go through the given model, and this process is referred to as iteration. Now, if the batch size comprises the complete training dataset, then the count of iterations is the same as that of epochs.

4. What is Dimensionality Reduction?

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.

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Date: 11-03-2024
Company Name: L&T
Role: Data Scientist
Topics: Logistic regression, confusion Matrix, outliers, metrics

1. How can you assess a good logistic model?

A. An approach to determining the goodness of fit is through the Homer-Lemeshow statistics, which is computed on data after the observations have been segmented into groups based on having similar predicted probabilities. It examines whether the observed proportions of events are similar to the predicted probabilities of occurrence in subgroups of the data set using a Pearson chi-square test. Small values with large p-values indicate a good fit to the data while large values with p-values below 0.05 indicate a poor fit. The null hypothesis holds that the model fits the data and in the below example we would reject H0.


2. What is bias, variance trade off ?

A. Bias is the difference between the average prediction of our model and the correct value which we are trying to predict. Variance is the variability of model prediction for a given data point or a value which tells us spread of our data. If our model is too simple and has very few parameters then it may have high bias and low variance. On the other hand if our model has large number of parameters then it’s going to have high variance and low bias. So we need to find the right/good balance without overfitting and underfitting the data. This tradeoff in complexity is why there is a tradeoff between bias and variance. An algorithm can’t be more complex and less complex at the same time.


3. Why is mean square error a bad measure of model performance?

A. A disadvantage of the mean-squared error is that it is not very interpretable because MSEs vary depending on the prediction task and thus cannot be compared across different tasks. Assume, for example, that one prediction task is concerned with estimating the weight of trucks and another is concerned with estimating the weight of apples. Then, in the first task, a good model may have an RMSE of 100 kg, while a good model for the second task may have an RMSE of 0.5 kg. Therefore, while RMSE is viable for model selection, it is rarely reported and R2 is used instead.


4. How can the outlier values be treated

A. Below are some of the methods of treating the outliers

Trimming/removing the outlier: In this technique, we remove the outliers from the dataset.
Quantile based flooring and capping : In this technique, the outlier is capped at a certain value above the 90th percentile value or floored at a factor below the 10th percentile value.
Mean/Median imputation : As the mean value is highly influenced by the outliers, it is advised to replace the outliers with the median value.


5. What is a confusion matrix?

A. A confusion matrix is a method of summarising a classification algorithm's performance. Calculating a confusion matrix can help you understand what your classification model is getting right and where it is going wrong. It gives us: “true positive” for correctly predicted event values, “false positive” for incorrectly predicted event values, “true negative” for correctly predicted no-event values, “false negative” for incorrectly predicted no-event values.

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Date: 13-03-2024
Company name: Cloudera
Role: Data Analyst
Topic: rank vs dense rank, one hot encoding , label encoding, tableau worksheets , splitting column

1. What is the difference between the RANK() and DENSE_RANK() functions?

The RANK() function in the result set defines the rank of each row within your ordered partition. If both rows have the same rank, the next number in the ranking will be the previous rank plus a number of duplicates. If we have three records at rank 4, for example, the next level indicated is 7. The DENSE_RANK() function assigns a distinct rank to each row within a partition based on the provided column value, with no gaps. If we have three records at rank 4, for example, the next level indicated is 5.

2. Explain One-hot encoding and Label Encoding. How do they affect the dimensionality of the given dataset?

One-hot encoding is the representation of categorical variables as binary vectors. Label Encoding is converting labels/words into numeric form. Using one-hot encoding increases the dimensionality of the data set. Label encoding doesn’t affect the dimensionality of the data set. One-hot encoding creates a new variable for each level in the variable whereas, in Label encoding, the levels of a variable get encoded as 1 and 0.

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

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.

4. How can you split a column into 2 or more columns?

You can split a column into 2 or more columns by following the below steps:
1. Select the cell that you want to split. Then, navigate to the Data tab, after that, select Text to Columns. 2. Select the delimiter. 3. Choose the column data format and select the destination you want to display the split. 4. The final output will look like below where the text is split into multiple columns.

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Date: 15-03-2024
Company name: Amazon
Role: Data Scientist
Topic: data analysis, ensemble, types of error, F1 score

1. What are the common problems that data analysts encounter during analysis?

The common problems steps involved in any analytics project are:

Handling duplicate data
Collecting the meaningful right data at the right time
Handling data purging and storage problems
Making data secure and dealing with compliance issues

2. Explain the Type I and Type II errors in Statistics?

In Hypothesis testing, a Type I error occurs when the null hypothesis is rejected even if it is true. It is also known as a false positive.

A Type II error occurs when the null hypothesis is not rejected, even if it is false. It is also known as a false negative.

3. What’s the F1 score? How would you use it?

The F1 score is a measure of a model’s performance. It is a weighted average of the precision and recall of a model, with results tending to 1 being the best, and those tending to 0 being the worst.

4. Name an example where ensemble techniques might be useful?

Ensemble techniques use a combination of learning algorithms to optimize better predictive performance. They typically reduce overfitting in models and make the model more robust (unlikely to be influenced by small changes in the training data). You could list some examples of ensemble methods (bagging, boosting, the “bucket of models” method) and demonstrate how they could increase predictive power.

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Interview QnA | Date: 19-03-2024
Company - Google
Role- Jr.ML Engineer
Topics: Machine Learning

1.How will you handle missing values in data?

There are several ways to handle missing values in the given data-

1.Dropping the values

2.Deleting the observation (not always recommended).

3.Replacing value with the mean, median and mode of the observation.

4.Predicting value with regression

5.Finding appropriate value with clustering

2. What is SVM? Can you name some kernels used in SVM?

SVM stands for support vector machine. They are used for classification and prediction tasks. SVM consists of a separating plane that discriminates between the two classes of variables. This separating plane is known as hyperplane. Some of the kernels used in SVM are –

Polynomial Kernel
Gaussian Kernel
Laplace RBF Kernel
Sigmoid Kernel
Hyperbolic Kernel

3.What is market basket analysis?

Market Basket Analysis is a modeling technique based upon the theory that if you buy a certain group of items, you are more (or less) likely to buy another group of items.

4.What is the benefit of batch normalization?

The model is less sensitive to hyperparameter tuning.

High learning rates become acceptable, which results in faster training of the model.

Weight initialization becomes an easy task.

Using different non-linear activation functions becomes feasible.

Deep neural networks are simplified because of batch normalization.

It introduces mild regularisation in the network.

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