Date: 02-05-2023
Company name: Walmart
role: Data Scientist
Topic : Computer vision, excel, time series

1.What are the different types of Pooling? Explain their characteristics.

Max pooling: Once we obtain the feature map of the input, we will apply a filter of determined shapes across the feature map to get the maximum value from that portion of the feature map. It is also known as subsampling because from the entire portion of the feature map covered by filter or kernel we are sampling one single maximum value.
Average pooling: Computes the average value of the feature map covered by kernel or filter, and takes the floor value of the result.
Sum pooling: Computes the sum of all elements in that window.


2. What is a Moving Average Process in Time series?

In time-series analysis, moving-average process, is a common approach for modeling univariate time series. The moving-average model specifies that the output variable depends linearly on the current and various past values of a stochastic term.

3. What is the difference between SQL having vs where?

The WHERE clause specifies the criteria which individual records must meet to be selected by a query. It can be used without the GROUP by clause. The HAVING clause cannot be used without the GROUP BY clause . The WHERE clause selects rows before grouping. The HAVING clause selects rows after grouping. The WHERE clause cannot contain aggregate functions. The HAVING clause can contain aggregate functions


4. What is Relative cell referencing in excel?

In Relative referencing, there is a change when copying a formula from one cell to another cell with respect to the destination. cells’ address Meanwhile, there is no change in Absolute cell referencing when a formula is copied, irrespective of the cell’s destination. This type of referencing is there by default. Relative cell referencing doesn’t require a dollar sign in the formula.

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Date: 03-05-2023
Company name: IBM
Role: Data Analyst
Topic: Join, OLTP, Joining vs blending, worksheet excel

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.


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Date- 05-05-2023
Company name: CRED
Role: Data Scientist
Topic: sets & groups, bag of word, nested triggers, TPR vs FPR

1. What do Tableau's sets and groups mean?

Data is grouped using sets and groups according to predefined criteria. The primary distinction between the two is that although a set can have only two options—either in or out—a group can divide the dataset into several groups. A user should decide which group or sets to apply based on the conditions.


2.What do you mean by a Bag of Words (BOW)?

It is used for word frequency or occurrences to train a classifier.
It contains a text representation that describes the frequency with which words appear in a document.
It has two steps:
-A list of terms that are well-known.
-A metric for determining the existence of well-known terms.

3. What are Nested Triggers?

Triggers may implement DML by using INSERT, UPDATE, and DELETE statements. These triggers that contain DML and find other triggers for data modification are called Nested Triggers.


4. What is a True positive rate and a false positive rate?

True positive rate or Recall: It gives us the percentage of the true positives captured by the model out of all the Actual Positive class.

TPR = TP/ (TP+FN)

False Positive rate: It gives us the percentage of all the false positives by my model prediction from the all Actual Negative class.

FPR = FP/(FP+TN)


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Date: 06-05-2023
Company name: Adecco
Role: SQL Developer
Topic: case, cursors, aggregate function , update statement

1. What is the case when statement in SQL?

The CASE expression goes through conditions and returns a value when the first condition is met (like an if-then-else statement). So, once a condition is true, it will stop reading and return the result. If no conditions are true, it returns the value in the ELSE clause. If there is no ELSE part and no conditions are true, it returns NULL.

2. Explain the update statement in SQL.

The SQL UPDATE Query is used to modify the existing records in a table. You can use the WHERE clause with the UPDATE query to update the selected rows, otherwise all the rows would be affected.

Syntax
The basic syntax of the UPDATE query with a WHERE clause is as follows −
UPDATE table_name
SET column1 = value1, column2 = value2...., columnN = valueN
WHERE [condition];

3. What are the two types of cursors?

There are 2 types of Cursors: Implicit Cursors, and Explicit Cursors. These are explained as following below.
Implicit Cursors:
Implicit Cursors are also known as Default Cursors of SQL SERVER. These Cursors are allocated by SQL SERVER when the user performs DML operations.
Explicit Cursors :
Explicit Cursors are Created by Users whenever the user requires them. Explicit Cursors are used for Fetching data from Table in Row-By-Row Manner.

4. What are the aggregate functions in SQL?

In database management an aggregate function is a function where the values of multiple rows are grouped together as input on certain criteria to form a single value of more significant meaning.

Various Aggregate Functions
1) Count()
2) Sum()
3) Avg()
4) Min()
5) Max()

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Date: 07-05-2023
Company name: Snapdeal
Role: Data Analyst
Topic : macros, views in PBI, data warehousing, relations in sql

1. What is Data Warehousing?
The process of collecting, extracting, transforming, and loading data from multiple sources and storing them in one database is known as data warehousing. A data warehouse can be considered as a central repository where data flows from transactional systems and other relational databases and is used for data analytics. A data warehouse comprises a wide variety of an organization’s historical data that supports the decision-making process in an organization.

2. List the different types of relationships in SQL.
One-to-One - This can be defined as the relationship between two tables where each record in one table is associated with the maximum of one record in the other table.
One-to-Many & Many-to-One - This is the most commonly used relationship where a record in a table is associated with multiple records in the other table.
Many-to-Many - This is used in cases when multiple instances on both sides are needed for defining a relationship.
Self-Referencing Relationships - This is used when a table needs to define a relationship with itself.

3. What are the different views available in Power BI Desktop?
There are three different views in Power BI, each of which serves another purpose:
Report View - In this view, users can add visualizations and additional report pages and publish the same on the portal.
Data View - In this view, data shaping can be performed using Query Editor tools.
Model View - In this view, users can manage relationships between complex datasets.


4. What are macros in Excel?
Excel allows you to automate the tasks you do regularly by recording them into macros. So, a macro is an action or a set of them that you can perform n number of times. For example, if you have to record the sales of each item at the end of the day, you can create a macro that will automatically calculate the sales, profits, loss, etc and use the same for the future instead of manually calculating it every day.

Date: 08-05-2023
Company name: Capgemini
Role: Data Scientist
Topic: adaboost, sliding window, subquery, workbook vs worksheet vs dashboard in tableau

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.
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Date: 09-05-2023
Company name: Accenture
Role: SQL Developer
Topic:
Constraint, joins, operators

1. What is a UNIQUE constraint?

The UNIQUE Constraint prevents identical values in a column from appearing in two records. The UNIQUE constraint guarantees that every value in a column is unique.

2. 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.

3. What is the case when in SQL Server?

The CASE statement is used to construct logic in which one column’s value is determined by the values of other columns. The condition to be tested is specified by the WHEN statement. If the WHEN condition returns TRUE, the THEN sentence explains what to do.
When none of the WHEN conditions return true, the ELSE statement is executed. The END keyword brings the CASE statement to a close.

4. What is the main difference between ‘BETWEEN’ and ‘IN’ condition operators?

BETWEEN operator is used to display rows based on a range of values in a row whereas the IN condition operator is used to check for values contained in a specific set of values.

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Date ; 10-05-2023
Company name: LTI
Role: Data Scientist
Topic: missing values, choice of algorithm, regularisation

1. How do you handle missing values in your data?

Answer: One common approach is to impute the missing values using statistical techniques such as mean, median, or mode imputation. Another approach is to use machine learning algorithms that can handle missing values, such as decision trees or k-nearest neighbors. If the missing values are related to a specific feature or subset of the data, we can also consider using a more targeted approach, such as regression imputation or multiple imputation.

2. Can you explain the difference between supervised and unsupervised learning?

Answer: Supervised learning is a type of machine learning where the algorithm learns from labeled data, where the target variable or outcome is already known. The algorithm uses this labeled data to identify patterns and relationships, and then applies this learning to new, unseen data to make predictions or classifications. In contrast, unsupervised learning is used when there is no target variable or outcome variable to be predicted, and the algorithm is tasked with finding patterns and relationships within the data on its own.

3. How do you decide which machine learning algorithm to use for a particular problem?

Answer: The choice of machine learning algorithm depends on several factors, including the type of problem, the nature of the data, and the available computational resources. Some common factors to consider when selecting a machine learning algorithm include the size of the dataset, the number of features, the desired level of interpretability, the complexity of the relationships between the features and the target variable, and the computational resources available for training and testing the model.

4. What is regularization, and why is it important in machine learning?

Answer: Regularization is a technique used in machine learning to prevent overfitting, which occurs when a model is too complex and fits the training data too closely, leading to poor performance on new, unseen data. Regularization works by adding a penalty term to the loss function that the model is optimizing, which discourages the model from relying too heavily on any one feature or set of features.

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Date: 13-05-2023
Company name: Netspi
Job role: Data Analyst
Topic: data cleaning, dashboard, alias in SQL

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. 

Date: 14-05-2023
Company name: TCS
Role: ML Engineer
Topic: KNN, Logistic Regression, RNN, LSTM

1. What is the Impact of Outliers on Logistic Regression?

The estimates of the Logistic Regression are sensitive to unusual observations such as outliers, high leverage, and influential observations. Therefore, to solve the problem of outliers, a sigmoid function is used in Logistic Regression.


2. What is the difference between vanilla RNNs and LSTMs?


The main difference between vanilla RNNs and LSTMs is that LSTMs are able to better remember long-term dependencies, while vanilla RNNs tend to forget them. This is due to the fact that LSTMs have a special type of memory cell that can retain information for longer periods of time, while vanilla RNNs only have a single layer of memory cells.

3. What is Masked Language Model in NLP?


Masked language models help learners to understand deep representations in downstream tasks by taking an output from the corrupt input. This model is often used to predict the words to be used in a sentence.


4. Why is the KNN Algorithm known as Lazy Learner?

When the KNN algorithm gets the training data, it does not learn and make a model, it just stores the data. Instead of finding any discriminative function with the help of the training data, it follows instance-based learning and also uses the training data when it actually needs to do some prediction on the unseen datasets. As a result, KNN does not immediately learn a model rather delays the learning thereby being referred to as Lazy Learner.

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