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Date: 01-06-2023
Company name: Tiger Analytics
Role: Data Analyst
Topic: map and filter, story in tableau, pivot in excel, unique in sql

1. What is the map() and filter() function in Python?
The map() function is a higher-order function. This function accepts another function and a sequence of ‘iterables’ as parameters and provides output after applying the function to each iterable in the sequence. The filter() function is used to generate an output list of values that return true when the function is called.

2. Define the story in Tableau?
The story can be defined as a sheet which is a collection of series of worksheets and dashboards used to convey the insights of data. A story can be used to show the connection between facts and outcomes that impacts the decision-making process. A story can be published on the web or can be presented to the audience.

3. How to fetch unique records from a table in SQL?
SQL DISTINCT clause is used to remove the duplicated columns from the result set.
The distinct keyword is used with the select keyword in conjunction. It is helpful when we avoid duplicate values present in the specific columns/tables. The unique values are fetched when we use the distinct keyword.

4. How are Pivot tables used to filter data in Excel?
You can filter data according to your requirements with Excel Pivot tables. Place the field on which you want the data to be filtered. Then open the drop-down list of the field you put in the Filter area from the pivot table and choose your line.

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Date - 02-06-2023
Company name: Datanyze
Role: SQL Developer
Topic: col rename, like , constraint

1. How to change a table name in SQL?
This is the command to change a table name in SQL:
ALTER TABLE table_name
RENAME TO new_table_name;
We will start off by giving the keywords ALTER TABLE, then we will follow it up by giving the original name of the table, after that, we will give in the keywords RENAME TO and finally, we will give the new table name.

2. How to use LIKE in SQL?
The LIKE operator checks if an attribute value matches a given string pattern. Here is an example of LIKE operator
SELECT * FROM employees WHERE first_name like ‘Steven’;
With this command, we will be able to extract all the records where the first name is like “Steven”.

3. If we drop a table, does it also drop related objects like constraints, indexes, columns, default, views and sorted procedures?
Yes, SQL server drops all related objects, which exists inside a table like constraints, indexes, columns, defaults etc. But dropping a table will not drop views and sorted procedures as they exist outside the table.

4. Explain SQL Constraints.
SQL Constraints are used to specify the rules of data type in a table. They can be specified while creating and altering the table. The following are the constraints in SQL: NOT NULL CHECK DEFAULT UNIQUE PRIMARY KEY FOREIGN KEY

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Date: 06-06-2023
Company name: Netspi
Job role: Data Analyst
Topic: SQL, data cleaning, EDA

1. Explain data cleansing.

Data cleaning, also known as data cleansing or data scrubbing or wrangling, is basically a process of identifying and then modifying, replacing, or deleting the incorrect, incomplete, inaccurate, irrelevant, or missing portions of the data as the need arises. This fundamental element of data science ensures data is correct, consistent, and usable. 

2. What is an Affinity Diagram?

Ans. An Affinity Diagram is an analytical tool used to cluster or organize data into subgroups based on their relationships. These data or ideas are mostly generated from discussions or brainstorming sessions and are used in analyzing complex issues.

3. Which questions should you ask the user/client before you create a dashboard?

Though this depends on the user’s requirements, still some of the common questions that I would ask the client before creating a dashboard are :

What is the purpose of the dashboard?Should the dashboard be retrospective or real-time?How detailed the dashboard should be?How tech and data-savvy is the end-user?Does the data need to be segmented?Should I explain the dashboard design to you?

4. What is an Alias in SQL?

An alias is a feature of SQL that is supported by most, if not all, RDBMSs. It is a temporary name assigned to the table or table column for the purpose of a particular SQL query. In addition, aliasing can be employed as an confusion technique to secure the real names of database fields. A table alias is also called a correlation name.
An alias is represented explicitly by the AS keyword but in some cases, the same can be performed without it as well.


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Date: 10-06-2023
Company name: Tredence
Role: ML engineer
Topic: margin in svm, r2 and adjusted r2, curse of dimensions

1. What is the primary difference between R square and adjusted R square?

In linear regression, you use both these values for model validation. However, there is a clear distinction between the two. R square accounts for the variation of all independent variables on the dependent variable. In other words, it considers each independent variable for explaining the variation. In the case of Adjusted R square, it accounts for the significant variables alone for indicating the percentage of variation in the model. By significant, we refer to the P values less than 0.05.

2. What is the curse of dimensionality?

Curse of Dimensionality refers to a set of problems that arise when working with high-dimensional data. The dimension of a dataset corresponds to the number of attributes/features that exist in a dataset. A dataset with a large number of attributes, generally of the order of a hundred or more, is referred to as high dimensional data. Some of the difficulties that come with high dimensional data manifest during analyzing or visualizing the data to identify patterns, and some manifest while training machine learning models. The difficulties related to training machine learning models due to high dimensional data are referred to as the ‘Curse of Dimensionality’.

3. What are some Stopping Criteria for k-Means Clustering?

a. Convergence. No further changes, points stay in the same cluster.
b. The maximum number of iterations. When the maximum number of iterations has been reached, the algorithm will be stopped. This is done to limit the runtime of the algorithm.
c. Variance did not improve by at least x * initial variance

4. What are hard margin and soft Margin SVMs?

Hard margin SVMs work only if the data is linearly separable and these types of SVMs are quite sensitive to the outliers. But our main objective is to find a good balance between keeping the margins as large as possible and limiting the margin violation i.e. instances that end up in the middle of margin or even on the wrong side, and this method is called soft margin SVM.


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Date - 11-06-2023
Company- IBM
Role- Data Analyst


1.What is a heatmap? Give an example.

A heatmap is a type of visualization used to demonstrate a set of data through varying shades of colours where the darkest shade of a specific colour denotes an extreme value (high intensity/density). It is typically used to compare two or more measures.

A quick example of a heatmap would be to understand the anatomy of the human body and observe the level of warmth depending upon the temperature of specific organs. If the red-yellow combination of colours is used, the areas that show red will denote the maximum temperature.


2. What is DRIVE Program Methodology?

It is a product of iterative sessions previously used and tested by enterprise deployments. It is based on best practises and allows a user to follow a specific set of actions to avoid errors and expedite reporting or visualization process.


3. When does regularization come into play in Machine Learning?

At times when the model begins to underfit or overfit, regularization becomes necessary. It is a regression that diverts or regularizes the coefficient estimates towards zero. It reduces flexibility and discourages learning in a model to avoid the risk of overfitting. The model complexity is reduced and it becomes better at predicting.


4. What is the order of operations in Excel?

Excel follows PEMDAS: parentheticals, exponents, multiplication, division, addition, and then subtraction. If you type in “=1+2/4” the answer will be 3/2 rather than ¾.


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Date: 13-06-2023
Company: Cloudera
Role: Data Scientist
Topic: machine learning, SQL, python

Q1 What are Loss Function and Cost Functions? Explain the key Difference Between them?

When calculating loss we consider only a single data point, then we use the term loss function.

Whereas, when calculating the sum of error for multiple data then we use the cost function. There is no major difference.

In other words, 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.

Mean-Squared Error(MSE): In simple words, we can say how our model predicted values against the actual values.

MSE = summation(predicted value - actual value)**2/n(no of data points)
Hinge loss: It is used to train the machine learning classifier, which is

L(y) = max(0,1- y_true*y_pred)

Where y = -1 or 1 indicating two classes and y represents the output form of the classifier. The most common cost function represents the total cost as the sum of the fixed costs and the variable costs in the equation y = mx + b


Q2 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

Q3 What are the subsets of SQL?

Ans. The following are the four significant subsets of the SQL:

Data definition language (DDL): It defines the data structure that consists of commands like CREATE, ALTER, DROP, etc.

Data manipulation language (DML): It is used to manipulate existing data in the database. The commands in this category are SELECT, UPDATE, INSERT, etc.

Data control language (DCL): It controls access to the data stored in the database. The commands in this category include GRANT and REVOKE.

Transaction Control Language (TCL): It is used to deal with the transaction operations in the database. The commands in this category are COMMIT, ROLLBACK, SET TRANSACTION, SAVEPOINT, etc.

Q4 Difference between a shallow and a deep copy?

It's faster to do shallow repetitions. It does, however, handle pointers and references in a "lazy" manner. It just copies over the pointer price rather than producing a current copy of the specific knowledge the pointer links to. As a result, each of the initial and subsequent copies can have pointers that relate to the same underlying knowledge. Deep repetition clones the underlying data completely. It is not shared by the first and, as a result, by the copy.


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Date: 16-06-2023
Company - Hitachi
Role: Data Scientist
Topic: Machine Learning, Statistics


1. What is the difference between supervised learning and unsupervised learning? Give concrete examples.

Supervised learning involves learning a function that maps an input to an output.
For example, if I had a dataset with two variables, age (input) and height (output), I could implement a supervised learning model to predict the height of a person based on their age.

Unlike supervised learning, unsupervised learning is used to draw inferences and find patterns from input data without references to labeled outcomes. A common use of unsupervised learning is grouping customers by purchasing behavior to find target markets.


2.How do you assess the statistical significance of an insight?

Ans: You would perform hypothesis testing to determine statistical significance. First, you would state the null hypothesis and alternative hypothesis.
Second, you would calculate the p-value, the probability of obtaining the observed results of a test assuming that the null hypothesis is true. Last, you would set the level of the significance (alpha) and if the p-value is less than the alpha, you would reject the null — in other words, the result is statistically significant.


3. What is the Law of Large Numbers?

Ans: The Law of Large Numbers is a theory that states that as the number of trials increases, the average of the result will become closer to the expected value.
Eg. flipping heads from fair coin 100,000 times should be closer to 0.5 than 100 times.

4.If a Company says that they want to double the number of ads in Newsfeed, how would you figure out if this is a good idea or not?

Ans: You can perform an A/B test by splitting the users into two groups: a control group with the normal number of ads and a test group with double the number of ads. Then you would choose the metric to define what a “good idea” is. For example, we can say that the null hypothesis is that doubling the number of ads will reduce the time spent on Facebook and the alternative hypothesis is that doubling the number of ads won’t have any impact on the time spent on Facebook. However, you can choose a different metric like the number of active users or the churn rate. Then you would conduct the test and determine the statistical significance of the test to reject or not reject the null.


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Date: 18-06-2023
Company name: IBM
Role: Data Analyst
Topic: Join, OLTP, Joining vs blending, worksheet excel, DBMS


1. 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.


2. What is OLTP?

OLTP, or online transactional processing, allows huge groups of people to execute massive amounts of database transactions in real time, usually via the internet. A database transaction occurs when data in a database is changed, inserted, deleted, or queried.


3. What is the difference between joining and blending in Tableau?

Joining term is used when you are combining data from the same source, for example, worksheet in an Excel file or tables in Oracle databaseWhile blending requires two completely defined data sources in your report.


4. How to prevent someone from copying the cell from your worksheet in excel?

If you want to protect your worksheet from being copied, go into Menu bar > Review > Protect sheet > Password.

By entering password you can prevent your worksheet from getting copied.


5. What are the different integrity rules present in the DBMS?

The different integrity rules present in DBMS are as follows:
Entity Integrity: This rule states that the value of the primary key can never be NULL. So, all the tuples in the column identified as the primary key should have a value.
Referential Integrity: This rule states that either the value of the foreign key is NULL or it should be the primary key of any other relation.

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Date- 24-06-2023
Company- Accenture
Role- Data Analyst
Topic- VBA, Tableau, PowerBI

1.How would you track down the last line and segment in VBA?

To track down the last column, utilize the underneath lines code in the VBA module:

Sub FindingLastRow()

Faint lastRow As Long

lastRow = ActiveSheet.Cells.SpecialCells(xlLastCell).Row

MsgBox (lastRow)

End Sub

To track down the last section, utilize the beneath lines code in the VBA module:

Sub FindingLastColumn()

Faint lastRow As Long

lastColumn = ActiveSheet.Cells.SpecialCells(xlLastCell).Column

MsgBox (lastColumn)

End Sub


2.How to increase size of pie in tableau?

Creating a pie chart requires atleast one measure and one dimension in row and shelf column. Then you can select a pie chart from either the show me option on the right side of the screen or from the marks card change automatic to pie. Then give some detailing to the pie chart by using a dimension in colour and measure in angle. Option to increase the size also comes under marks card. Click on the size option and then move the slider towards right to increase its size.


3.How to calculate the average in Power BI?

Average can be calculated in two ways-

one is when we add a measure to a visual; by default, it summarizes any measure. When we click on the drop-down for the measure- we can change from Sum to Average. This gives us an average.

The second one is creating a calculated measure for average using the AVERAGE() DAX function.


4.Where is the data stored in Power BI?

The nation or area for the business identity is chosen by the first user in your organization who signs up for Power BI or Microsoft 365. The cloud’s shared identity and access management service, Azure Active Directory (AAD), establishes a tenant in the data center region nearest to the chosen nation or area. AAD is a multi-tenant service, with each enterprise represented in the data center as a separate tenant. The data is saved in the area you choose at sign-up. This region will be the same for all users in your organization, regardless of their location. The chosen region should, ideally, be in the same geographical area as the majority of your consumers.


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Date: 25-06-2023
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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