Today's Interview QnAS
Company - Accenture
Role- Data Scientist

1.What do you mean by Associative Rule Mining (ARM)?

Associative Rule Mining is one of the techniques to discover patterns in data like features (dimensions) which occur together and features (dimensions) which are correlated. It is mostly used in Market-based Analysis to find how frequently an itemset occurs in a transaction. Association rules have to satisfy minimum support and minimum confidence at the very same time. Association rule generation generally comprised of two different steps:

“A min support threshold is given to obtain all frequent item-sets in a database.”
“A min confidence constraint is given to these frequent item-sets in order to form the association rules.”
Support is a measure of how often the “item set” appears in the data set and Confidence is a measure of how often a particular rule has been found to be true.

2. What is Marginalisation? Explain the process.

Marginalisation is summing the probability of a random variable X given joint probability distribution of X with other variables. It is an application of the law of total probability.

P(X=x) = ∑YP(X=x,Y)

Given the joint probability P(X=x,Y), we can use marginalization to find P(X=x). So, it is to find distribution of one random variable by exhausting cases on other random variables.

3.How do we check the normality of a data set or a feature?

Visually, we can check it using plots. There is a list of Normality checks, they are as follow:

Shapiro-Wilk W Test
Anderson-Darling Test
Martinez-Iglewicz Test
Kolmogorov-Smirnov Test
D’Agostino Skewness Test

4.What is OOB error and how does it occur?

For each bootstrap sample, there is one-third of data that was not used in the creation of the tree, i.e., it was out of the sample. This data is referred to as out of bag data. In order to get an unbiased measure of the accuracy of the model over test data, out of bag error is used. The out of bag data is passed for each tree is passed through that tree and the outputs are aggregated to give out of bag error. This percentage error is quite effective in estimating the error in the testing set and does not require further cross-validation.

————————————————————-

🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS WITH CLOUDYML FROM SCRATCH. TRUSTED BY 7000+ HAPPY LEARNERS

This course package, you are getting Python, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, Excel, Case studies, Interview QnAs PDF, Capstone End-to-End Projects of ML and DL with 1-1 Live Doubt Clearance Support over Skype chat everyday. Check the course NOW!

Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo

🚨How Tableau Can Land You On Data Analyst Role ??

Blog Link👉🏻https://ai.cloudyml.com/cloudyml-tableau-blog

Today's Interview QnAs
Company - Wipro
Role- Data Analyst

1. What are different types of Collation Sensitivity?

Following are the different types of Collation Sensitivity:
- Case sensitive: A and a, B and b
- Kana sensitive: Japanese Kana characters
- Width sensitive: single byte characters and double-byte characters.
- Accent Sensitive.


2. What is OLTP ?

OLTP or Online Transaction Processing is a type of data processing that consists of executing a number of transactions occurring concurrently—online banking, shopping, order entry, or sending text messages, for example. These transactions traditionally are referred to as economic or financial transactions, recorded and secured so that an enterprise can access the information anytime for accounting or reporting purposes.


3. What is OLAP?

OLAP stands for On-Line Analytical Processing. OLAP is a classification of software technology which authorizes analysts, managers, and executives to gain insight into information through fast, consistent, interactive access in a wide variety of possible views of data that has been transformed from raw information to reflect the real dimensionality of the enterprise as understood by the clients.

OLAP implement the multidimensional analysis of business information and support the capability for complex estimations, trend analysis, and sophisticated data modeling.


4. How OLAP Works?

Fundamentally, OLAP has a very simple concept. It pre-calculates most of the queries that are typically very hard to execute over tabular databases, namely aggregation, joining, and grouping. These queries are calculated during a process that is usually called 'building' or 'processing' of the OLAP cube. This process happens overnight, and by the time end users get to work - data will have been updated.

————————————————————-

🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS WITH CLOUDYML FROM SCRATCH. TRUSTED BY 7000+ HAPPY LEARNERS

This course package, you are getting Python, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, Excel, Case studies, Interview QnAs PDF, Capstone End-to-End Projects of ML and DL with 1-1 Live Doubt Clearance Support over Skype chat everyday. Check the course NOW!

Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo

🚨JOB OPENINGS UPDATE🚨

Role: Data Scientist
Company: Amakshar Technology
Exp: 0-3 yr
Link: https://ai.cloudyml.com/3cpUKET

Role: Automotive Data Analyst - KK Nagar(Madurai)
Company: Kukulkan
Exp : 0-1 yr
Link: https://ai.cloudyml.com/3PkXotZ

Role: Business Analyst - Retail Analytics - KPO
Company: Huquo Consulting Pvt. Ltd
Exp: 0-1 yr
Link: https://ai.cloudyml.com/3o9MWtw

Role: Quant Analyst / BI Analyst- Statistics
Company: Decimal Point Analytics
Exp: 0-1 yr
Link: https://ai.cloudyml.com/3PDA9et

ALL THE BEST💙

Today's Interview QnAs
Company Name: PepsiCo
Role: ML Engineer

1.What’s a Fourier transform?

A Fourier transform is a generic method to decompose generic functions into a superposition of symmetric functions. Or as this more intuitive tutorial puts it, given a smoothie, it’s how we find the recipe. The Fourier transform finds the set of cycle speeds, amplitudes, and phases to match any time signal. A Fourier transform converts a signal from time to frequency domain—it’s a very common way to extract features from audio signals or other time series such as sensor data.


2.How is a decision tree pruned?

Pruning is what happens in decision trees when branches that have weak predictive power are removed in order to reduce the complexity of the model and increase the predictive accuracy of a decision tree model. Pruning can happen bottom-up and top-down, with approaches such as reduced error pruning and cost complexity pruning. Reduced error pruning is perhaps the simplest version: replace each node. If it doesn’t decrease predictive accuracy, keep it pruned. While simple, this heuristic actually comes pretty close to an approach that would optimize for maximum accuracy.


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.

————————————————————-

🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS WITH CLOUDYML FROM SCRATCH. TRUSTED BY 7000+ HAPPY LEARNERS

This course package, you are getting Python, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, Excel, Case studies, Interview QnAs PDF, Capstone End-to-End Projects of ML and DL with 1-1 Live Doubt Clearance Support over Skype chat everyday. Check the course NOW!

Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo

Give your 10 mins to read our new blog and become aware of the common Myths of Data Science !

Blog Link - https://www.cloudyml.com/blog/myths-of-data-science/

🚨Top 10 Data Analysis Case Studies To Put in Resume

Check our latest YouTube Video in which we have briefly explained about those projects. Link given below. Don't Miss !

👉🏻 https://youtu.be/FQNPSKdlKNg

Today's Interview QnAs
Company- Numerator
Role- Data Analyst

1.What is quick filter in tableau?

Whenever using a filter in Tableau, it comes with some options to change the functionality of filter very easily, such as using it as a single value drop down or single value list or multiple value list or multiple value drop down and various other options. After we set a filter to a sheet just right click on the sheet and there you can see all the quick filter options. Changes made to these options will also change the aesthetics of filter shown on the sheet.

2.How to calculate percentage in tableau?

To calculate the percentage of data on your worksheet. Go to Analysis pane and select Percentages of, there you will see a lot percentage options such as percentage of table, column, row, pane, row in pane, column in pane and cell. Select any of the above options then define the total value o which percentage is to be calculated. The option you choose will be uniform to all the rows and columns and there is no way to specify different options to rows and columns.

3. What is Power Pivot?

The Power Pivot is an in-memory data modeling component. It provides highly compressed data storage with fast calculation. It helps you build a data model, relationships, creating formulas, calculated columns, Pivot Tables, and Pivot Charts from multiple resources.

4. What is x-velocity in Power Pivot?

X-Velocity is the in-memory analytics engine behind Power Pivot that loads and handles huge data in Power BI. It stores data in columnar storage that results in faster processing.

————————————————————-

🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS WITH CLOUDYML FROM SCRATCH. TRUSTED BY 7000+ HAPPY LEARNERS

This course package, you are getting Python, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, Excel, Case studies, Interview QnAs PDF, Capstone End-to-End Projects of ML and DL with 1-1 Live Doubt Clearance Support over Skype chat everyday. Check the course NOW!

Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo

If you are preparing for Data Science and Analytics domain and want to get confidence in your skills and to get complete hands on practical learning from scratch then check our Mega Combo Course.

It Covers all the important topics which are required to become interview ready from scratch with 1-1 Live Doubt Clearance Support.

✅Python
✅Advanced SQL
✅Maths & Statistics
✅Machine Learning
✅Deep Learning
✅AWS Sagamaker
✅Model Deployment
✅Tableau
✅PowerBi
✅MS Excel
✅Amazon QuickSight (NEW)
✅Google Data Studio (NEW)
✅Interview QnA PDFs
✅Capstone Projects


Follow the link below and visit our website. Check reviews of our learners and then decide.

Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo

Stay Safe and Happy Learning 💙

Today's Interview QnAs

Company name - Kenstar

Role - Data Scientist


1. How do you analyse the performance of the predictions generated by regression models versus classification models?

In the regression model, the most commonly known evaluation metrics include:

• R-squared (R2)

• Root Mean Squared Error (RMSE)

• Residual Standard Error (RSE)

• Mean Absolute Error (MAE)


Classification is the problem of identifying to which of a set of categories/classes a new observation belongs, based on the training set of data containing records whose class label is known. Following are the performance metrics used for evaluating a classification model:

• Accuracy

• Precision and Recall

• Specificity

• F1-score

• AUC-ROC


2. How do you assess logistic regression versus simple linear regression models?

• Linear Regression is used to handle regression problems whereas Logistic regression is used to handle the classification problems.

• Linear regression provides a continuous output but Logistic regression provides discreet output. • The purpose of Linear Regression is to find the best-fitted line while Logistic regression is one step ahead and fitting the line values to the sigmoid curve.

• The method for calculating loss function in linear regression is the mean squared error whereas for logistic regression it is maximum likelihood estimation.


3. What is cross-validation and why would you use it?

Cross-validation is a statistical method used to estimate the performance (or accuracy) of machine learning models. It is used to protect against overfitting in a predictive model, particularly in a case where the amount of data may be limited. In cross-validation, you make a fixed number of folds (or partitions) of the data, run the analysis on each fold, and then average the overall error estimate.


4. What’s the name of the matrix used to evaluate predictive models?

Confusion Matrix: A confusion matrix is a tabular summary of the number of correct and incorrect predictions made by a classifier. It can be used to evaluate the performance of a classification model through the calculation of performance metrics like accuracy, precision, recall, and F1-score.

————————————————————-

🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS WITH CLOUDYML FROM SCRATCH. TRUSTED BY 7000+ HAPPY LEARNERS

This course package, you are getting Python, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, Excel, Case studies, Interview QnAs PDF, Capstone End-to-End Projects of ML and DL with 1-1 Live Doubt Clearance Support over Skype chat everyday. Check the course NOW!

Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo

Make your career in Data Science and Analytics from scratch with our Mega Combo Course which will give you complete Hands-on Practical Learning Experience with 1-1 Doubt Clearance Support Everyday. Visit Course Now for complete details.

Course link 👉🏻 https://www.cloudyml.com/mega-combo-package/

🚨SQL Interview Problems Asked in InData Labs. Watch our latest YouTube Video to get explained Solutions. Link given below

👉🏻 https://youtu.be/r-SvsSuTkYM

Today's Interview QnAs
Company name - PWC
Role : ML Engineer

1. What is DBSCAN Clustering?

DBSCAN groups ‘densely grouped’ data points into a single cluster. It can identify clusters in large spatial datasets by looking at the local density of the data points. The most exciting feature of DBSCAN clustering is that it is robust to outliers. It also does not require the number of clusters to be told beforehand, unlike K-Means, where we have to specify the number of centroids.

2. What are the different forms of joins in a table?

SQL has many kinds of different joins including INNER JOIN, SELF JOIN, CROSS JOIN, and OUTER JOIN. In fact, each join type defines the way two tables are related in a query. OUTER JOINS can further be divided into LEFT OUTER JOINS, RIGHT OUTER JOINS, and FULL OUTER JOINS.

3.How is the grid search parameter different from the random search?

Model Hyperparameter tuning is very useful to enhance the performance of a machine learning model. The only difference between both the approaches is in grid search we define the combinations and do training of the model whereas in RandomizedSearchCV the model selects the combinations randomly. Both are very effective ways of tuning the parameters that increase the model generalizability.
Random search is a technique where random combinations of the hyperparameters are used to find the best solution for the built model. The drawback of random search is that it yields high variance during computing. Since the selection of parameters is completely random; and since no intelligence is used to sample these combinations, luck plays its part.

————————————————————-

🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS WITH CLOUDYML FROM SCRATCH. TRUSTED BY 7000+ HAPPY LEARNERS

This course package, you are getting Python, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, Amazon QuickSight, Google Data Studio, Excel, Case studies, Interview QnAs PDF, Capstone End-to-End Projects of ML and DL with 1-1 Live Doubt Clearance Support over Skype chat everyday. Check the course NOW!

Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo

Watch our latest YouTube Video on Data Structure and Algorithms in which we have covered Largest and Smallest Element Array Problem. Link Below

👉🏻 https://youtu.be/aA4zEGcrXDc

Today's Interview QnAs
Company Name : Jabra
Role : Data Scientist


1. What relationships exist between a logistic regression’s coefficient and the Odds Ratio?

The coefficients and the odds ratios then represent the effect of each independent variable controlling for all of the other independent variables in the model and each coefficient can be tested for significance.

2. What’s the relationship between Principal Component Analysis (PCA) and Linear & Quadratic Discriminant Analysis (LDA & QDA)

LDA focuses on finding a feature subspace that maximizes the separability between the groups. While Principal component analysis is an unsupervised Dimensionality reduction technique, it ignores the class label. PCA focuses on capturing the direction of maximum variation in the data set.The PC1 the first principal component formed by PCA will account for maximum variation in the data.PC2 does the second-best job in capturing maximum variation and so on.

The LD1 the first new axes created by Linear Discriminant Analysis will account for capturing most variation between the groups or categories and then comes LD2 and so on.


3. What’s the difference between logistic and linear regression? How do you avoid local minima?


Linear Regression is used to handle regression problems whereas Logistic regression is used to handle the classification problems.
Linear regression provides a continuous output but Logistic regression provides discreet output.
The purpose of Linear Regression is to find the best-fitted line while Logistic regression is one step ahead and fitting the line values to the sigmoid curve.
The method for calculating loss function in linear regression is the mean squared error whereas for logistic regression it is maximum likelihood estimation.
We can try to prevent our loss function from getting stuck in a local minima by providing a momentum value. So, it provides a basic impulse to the loss function in a specific direction and helps the function avoid narrow or small local minima by using stochastic gradient descent.

4. Explain the difference between type 1 and type 2 errors.

Type 1 error is a false positive error that ‘claims’ that an incident has occurred when, in fact, nothing has occurred. The best example of a false positive error is a false fire alarm – the alarm starts ringing when there’s no fire. Contrary to this, a Type 2 error is a false negative error that ‘claims’ nothing has occurred when something has definitely happened. It would be a Type 2 error to tell a pregnant lady that she isn’t carrying a baby.

————————————————————-

🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS WITH CLOUDYML FROM SCRATCH. TRUSTED BY 7000+ HAPPY LEARNERS

This course package, you are getting Python, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, Amazon QuickSight, Google Data Studio, Excel, Case studies, Interview QnAs PDF, Capstone End-to-End Projects of ML and DL with 1-1 Live Doubt Clearance Support over Skype chat everyday. Check the course NOW!

Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo

Today's Interview QnAs
Company- DataZymes
Role- Data Analyst

1.What is the Difference Between .twbx And .twb?

.twbx
The .twbx contains all of the necessary information to build the visualization along with the data source. This is called a packaged workbook, and it compresses the package of files altogether.

.twb
The .twb contains instructions about how to interact with the data source. When it's building a visualization, Tableau will look at the data source and then build the visualization with an extract. It can’t be shared alone as it contains only instructions, and the data source needs to be attached separately.

2.What is the Difference Between Joining and Blending?

Combining the data from two or more different sources is data blending, such as Oracle, Excel, and SQL Server. In data blending, each data source contains its own set of dimensions and measures.
Combining the data between two or more tables or sheets within the same data source is data joining. All the combined tables or sheets contain a common set of dimensions and measures.

3. What is the difference between count, counta, and countblank?

The count function is very often used in Excel. Here, let’s look at the difference between count, and it’s variants - counta and countblank.

1. COUNT
It counts the number of cells that contain numeric values only. Cells that have string values, special characters, and blank cells will not be counted.
2. COUNTA
It counts the number of cells that contain any form of content. Cells that have string values, special characters, and numeric values will be counted. However, a blank cell will not be counted.
3. COUNTBLANK
As the name suggests, it counts the number of blank cells only. Cells that have content will not be taken into consideration.

4.What is a Self-Join?

A self-join is a type of join that can be used to connect two tables. As a result, it is a unary relationship. Each row of the table is attached to itself and all other rows of the same table in a self-join. As a result, a self-join is mostly used to combine and compare rows from the same database table.

————————————————————-

🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS WITH CLOUDYML FROM SCRATCH. TRUSTED BY 7000+ HAPPY LEARNERS

This course package, you are getting Python, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, Amazon QuickSight, Google Data Studio, Excel, Case studies, Interview QnAs PDF, Capstone End-to-End Projects of ML and DL with 1-1 Live Doubt Clearance Support over Skype chat everyday. Check the course NOW!

Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo

Aptitude Problems asked in TCS Data Science Interview.

Watch our latest YouTube Video in which we have covered some important Aptitude Problems with explained solution. Link given below.

👉🏻 https://youtu.be/AJvHVNCX_as