Today's Interview QnAs
Company- Musigma
Role- ML Engineer

1. What is correlation and covariance in statistics?

Correlation is defined as the measure of the relationship between two variables. If two variables are directly proportional to each other, then its positive correlation. If the variables are indirectly proportional to each other, it is known as a negative correlation. Covariance is the measure of how much two random variables vary together.


2.What is ‘Naive’ in a Naive Bayes?

A naive Bayes classifier assumes that the presence (or absence) of a particular feature of a class is unrelated to the presence (or absence) of any other feature, given the class variable. Basically, it’s “naive” because it makes assumptions that may or may not turn out to be correct.


3. How can you select k for k-means?

The two methods to calculate the optimal value of k in k-means are:

Elbow method
Silhouette score method

Silhouette score is the most prevalent while determining the optimal value of k.


4.Explain the Law of Large Numbers.

The ‘Law of Large Numbers’ states that if an experiment is repeated independently a large number of times, the average of the individual results is close to the expected value. It also states that the sample variance and standard deviation also converge towards the expected value.

————————————————————-

🚨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

Today's Interview QnAs
Company - Cred
Role- Business Analyst

1. What is RUP methodology ?

Rational Unified Process is a product application improvement method with numerous devices to help with the coding the last product and assignments identified with this objective. RUP is an object oriented approach that guarantees successful project management and top-notch software production.

2. How does CATWOE help in business analysis and decision making?

Customers, Actors, Transformation process, Worldwide, Owner and Environment constraint (CATWOE) helps in making decisions ahead of time. It includes analysing how those decisions will affect customers (C);who are involved as actors(A);what difference transformation (T) processes are which might affect the system, global picture and Worldwide(W) issues; who is responsible/has ownership (O) for the business; what the environment (E) impacts will be of the projects/business.

3. What is RAD methodology ?

The Rapid Application Development model is a kind of incremental model. The phases of a project are produced in parallel as individuals projects. The development in the project are timeboxed, delivered and afterward assembled into a working model.

4. What is INVEST ?

INVEST is an abbreviated of Independent, Negotiable, Valuable, Estimable, Sized appropriately and Testable. This term is used by business analyst and project managers to deliver quality services and products.

————————————————————-

🚨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

PowerBi Interview Questions asked by TCS

In this video we are covering common questions of PowerBI Asked in TCS company. Link Below👇🏻

➡️ https://youtu.be/l-NuQuvgr7A

🚨JOB OPENINGS UPDATE🚨

Role : Data Scientist / Machine Learning Engineer
Company : Bgps Management Solutions Private Limited
Exp : 0-3 yr
Link : https://ai.cloudyml.com/3RrxWo3

Role: Data Analyst
Company : Tlx Tech Solutions Private Limited
Exp: 0-2 yr
Link: https://ai.cloudyml.com/3NUUbQy

Role: Data Analyst - company: Python/Artificial Intelligence
Imindyourbusiness
Exp: 0-2 yr
Link: https://ai.cloudyml.com/3PfFOHl

Role: Fresher Business Analyst - Graduate With MS Excel Knowledge
Company : Universal Manpower Online
Exp: 0-2 yr
Link: https://ai.cloudyml.com/3c490mz

ALL THE BEST💙

🚨WATCH THIS To Get into INFOSYS.

Complete Interview Experience for the role of SYSTEM ENGINEER, shared by a candidate, is covered in our Latest YouTube Video. Don't Miss this video. Link Below

👉🏻 https://youtu.be/6Cc35WLo-DU

Today's Interview QnAs
Company - United Breweries
Role- ML Engineer

1.What are the various steps involved in an data analytics project?

The steps involved in a data analytics project are:

Data collection
Data cleansing
Data pre-processing
EDA
Creation of train test and validation sets
Model creation
Hyperparameter tuning
Model deployment


2. Explain Star Schema.

Star schema is a data warehousing concept in which all schema is connected to a central schema.


3. What is root cause analysis?

Root cause analysis is the process of tracing back of occurrence of an event and the factors which lead to it. It’s generally done when a software malfunctions. In data science, root cause analysis helps businesses understand the semantics behind certain outcomes.


4. Define Confounding Variables.

A confounding variable is an external influence in an experiment. In simple words, these variables change the effect of a dependent and independent variable. A variable should satisfy below conditions to be a confounding variable :

Variables should be correlated to the independent variable.
Variables should be informally related to the dependent variable.
For example, if you are studying whether a lack of exercise has an effect on weight gain, then the lack of exercise is an independent variable and weight gain is a dependent variable. A confounder variable can be any other factor that has an effect on weight gain. Amount of food consumed, weather conditions etc. can be a confounding variable.

Watch our latest YouTube Video to get ideas about the projects that you can do as a beginner in Data Science. Don't Miss. Link below👇🏻

➡️ https://youtu.be/KRNh3kX0t14

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

1. What is subquery 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 subquery, the outer query is called as the main query where the inner query is called subquery. Subqueries are always executed first and then result of subquery is passed to main query. It can be nested inside a SELECT, UPDATE or any other query.

2. What is Tower of Hanoi?

Tower of Hanoi is a mathematical puzzle that shows how recursion might be utilised as a device in building up an algorithm to take care of a specific problem. Using decision tree and a breadth-first search(BFS) algorithm in AI, we can solve the Tower of Hanoi.

3. What are the two main methods two detect outliers?

Box plot method: if the value is higher or lesser than 1.5*IQR (interquartile range) above the upper quartile (Q3) or below the lower quartile (Q1) respectively, then it is considered an outlier.

Standard deviation method: if value higher or lower than mean ± (3*standard deviation), then it is considered an outlier.

4. What is the role of a data model for any organization?

With the help of a data model, you can always keep your client informed in advance for a time period. However, when you enter a new market then you are facing new challenges almost every day. A data model helps you in understanding these challenges in the best way and deriving the accurate outputs from the same.

————————————————————-

🚨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

🚨Python Problem Asked in NETFLIX Company.

Watch our latest YouTube video in which we have covered Python interview questions asked by NETFLIX. Link given below

👉🏻 https://youtu.be/uDPDhUcUz4o

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

1.How will you define the number of clusters in a clustering algorithm?

By determining the Silhouette score and elbow method, we determine the number of clusters in the algorithm.

2.What is Ensemble Learning? Define types.

Ensemble learning is clubbing of multiple weak learners (ml classifiers) and then using aggregation for result prediction. It is observed that even if the classifiers perform poorly individually, they do better when their results are aggregated. An example of ensemble learning is random forest classifier.

3. What are the support vectors in SVM?

Support vectors are data points that are closer to the hyperplane and influence the position and orientation of the hyperplane. Using these support vectors, we maximise the margin of the classifier. Deleting the support vectors will change the position of the hyperplane. These are the points that help us build our SVM.

4.What is pruning in Decision Tree?

Pruning is the process of reducing the size of a decision tree. The reason for pruning is that the trees prepared by the base algorithm can be prone to overfitting as they become incredibly large and complex.

————————————————————-

🚨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

🚨SQL Interview Questions asked by CRED Company. Watch our latest YouTube Video to get this explained interview QnA. Link Below👇🏻

➡️ https://youtu.be/dgTJ4IBlNbw

Today's Interview QnAs
Company - Wipro
Role - ML Engineer

1.What is the main advantage of Naive Bayes?

A Naive Bayes classifier converges very quickly as compared to other models like logistic regression. As a result, we need less training data in the case of a naive Bayes classifier.


2.What should you do when your model is suffering from low bias and high variance?

When the model is suffering from low bias and high variance, it is essentially overfitting. In such a situation, techniques such as Regularization can be used or the model can be simplified by reducing the number of features in the dataset.


3.State the differences between causality and correlation?

Causality applies to situations where one action, say X, causes an outcome, say Y, whereas Correlation is just relating one action (X) to another action(Y) but X does not necessarily cause Y.


4.A data set is given to you and it has missing values which spread along 1 standard deviation from the mean. How much of the data would remain untouched?

It is given that the data is spread across mean that is the data is spread across an average. So, we can presume that it is a normal distribution. In a normal distribution, about 68% of data lies in 1 standard deviation from averages like mean, mode or median. That means about 32% of the data remains uninfluenced by missing values.

————————————————————-

🚨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

🚨Watch our latest YouTube Video to Crack CAPGEMINI Aptitude Round for Data Scientist Role. Link Below


👉🏻 https://youtu.be/WrgxXHlnKsA

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

1.How to calculate age in excel?
The easiest way to calculate age in MS Excel is using the formula –

“=DATEDIF(birth_date,as_of_date,”y”)”

The result will be the number of complete years.

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 a legend in Power BI?

Legends are part of Power BI visuals. They represent categories in a visual and are usually color-coded. In some visuals, we can add a category dimension as legend explicitly. One example can be in stacked bar/column charts, where the stacks represent different categories, and these categories are color-coded. These categories are represented in the legend. And every visual where the legend is applicable has a separate formatting section where we can specify its font and font size, whether we need it to be visible or not, or even its position(top, bottom, left, right)

4.What is ETL in SQL?

ETL stands for Extract, Transform and Load. It is a three step process, where we would have to start off by extracting the data from sources. Once we collate the data from different sources, what we have is raw data. This raw data has to be transformed into tidy format, which will come in the second phase. Finally, we would have to load this tidy data into tools which would help us to find insights.

————————————————————-

🚨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

🚨Learn PowerBi in 1 Hour.

Watch our PowerBI Crash Course Video on YouTube for Data Analyst Role. Link Below

👉🏻 https://youtu.be/Ir5nfSio804

Today's Interview QnA

Company Name - Cloudera

Role: Data Scientist


1. What are the assumptions required for linear regression? What if some of these assumptions are violated?

The assumptions are as follows:

The sample data used to fit the model is representative of the population

The relationship between X and the mean of Y is linear

The variance of the residual is the same for any value of X (homoscedasticity)

Observations are independent of each other

For any value of X, Y is normally distributed.


Extreme violations of these assumptions will make the results redundant. Small violations of these assumptions will result in a greater bias or variance of the estimate.


2.What is multicollinearity and how to remove it?

Multicollinearity exists when an independent variable is highly correlated with another independent variable in a multiple regression equation. This can be problematic because it undermines the statistical significance of an independent variable.
You could use the Variance Inflation Factors (VIF) to determine if there is any multicollinearity between independent variables — a standard benchmark is that if the VIF is greater than 5 then multicollinearity exists.


3. What is overfitting and how to prevent it?

Overfitting is an error where the model ‘fits’ the data too well, resulting in a model with high variance and low bias. As a consequence, an overfit model will inaccurately predict new data points even though it has a high accuracy on the training data.

Few approaches to prevent overfitting are:

- Cross-Validation:Cross-validation is a powerful preventative measure against overfitting. Here we use our initial training data to generate multiple mini train-test splits. Now we use these splits to tune our model.

- Train with more data: It won’t work every time, but training with more data can help algorithms detect the signal better or it can help my model to understand general trends in particular.

- We can remove irrelevant information or the noise from our dataset.

- Early Stopping: When you’re training a learning algorithm iteratively, you can measure how well each iteration of the model performs.

Up until a certain number of iterations, new iterations improve the model. After that point, however, the model’s ability to generalize can weaken as it begins to overfit the training data.

Early stopping refers stopping the training process before the learner passes that point.

- Regularization: It refers to a broad range of techniques for artificially forcing your model to be simpler. There are mainly 3 types of Regularization techniques:L1, L2,&,Elastic- net.

- Ensembling : Here we take number of learners and using these we get strong model. They are of two types : Bagging and Boosting.

————————————————————-

🚨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

🚨Watch our latest YouTube Video and become ready for Google Python Interview Round for Data Science Roles. Link below

👉🏻 https://youtu.be/eKwq8NOijHg