Date: 16-02-2023
Company name: Capgemini
Role: Data Scientist
Topic: decorators, acid, one hot encoding, kpi

1. What are decorators in Python?

Ans: Decorators are used to add some design patterns to a function without changing its structure. Decorators generally are defined before the function they are enhancing. To apply a decorator we first define the decorator function. Then we write the function it is applied to and simply add the decorator function above the function it has to be applied to. For this, we use the @ symbol before the decorator.

2. What is the ACID property in a database?

The full form of ACID is atomicity, consistency, isolation, and durability.
• Atomicity refers that if any aspect of a transaction fails, the whole transaction fails and the database state remains unchanged.
• Consistency means that the data meets all validity guidelines.
• Concurrency management is the primary objective of isolation.
• Durability ensures that once a transaction is committed, it will occur regardless of what happens in between such as a power outage, fire, or some other kind of disturbance.


3. What is the meaning of KPI in statistics?

KPI is an acronym for a key performance indicator. It can be defined as a quantifiable measure to understand whether the goal is being achieved or not. KPI is a reliable metric to measure the performance level of an organization or individual with respect to the objectives. An example of KPI in an organization is the expense ratio.

4. 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.

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Date: 17-02-2023
Company name: Hitachi
Role: SQL Developer
Topic: SQL vs MySQL, cross join, stored procedure, pattern matching

1. What is the difference between SQL and MySQL?

SQL is a standard language for retrieving and manipulating structured databases. On the contrary, MySQL is a relational database management system, like SQL Server, Oracle or IBM DB2, that is used to manage SQL databases.


2. What is a Cross-Join?

Cross join can be defined as a cartesian product of the two tables included in the join. The table after join contains the same number of rows as in the cross-product of the number of rows in the two tables. If a WHERE clause is used in cross join then the query will work like an INNER JOIN.


3. What is a Stored Procedure?

A stored procedure is a subroutine available to applications that access a relational database management system (RDBMS). Such procedures are stored in the database data dictionary. The sole disadvantage of stored procedure is that it can be executed nowhere except in the database and occupies more memory in the database server.


4. What is Pattern Matching in SQL?

SQL pattern matching provides for pattern search in data if you have no clue as to what that word should be. This kind of SQL query uses wildcards to match a string pattern, rather than writing the exact word. The LIKE operator is used in conjunction with SQL Wildcards to fetch the required information.

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Date: 20-02-2023
Company name: Kenstar
Role: ML Engineer
Topic: linear regression, svm, xgboost, naive bayes, random forest

1. What are the disadvantages of the linear regression model?
One of the most significant demerits of the linear model is that it is sensitive and dependent on the outliers. It can affect the overall result. Another notable demerit of the linear model is overfitting. Similarly, underfitting is also a significant disadvantage of the linear model.

2. Why Naive Bayes is called Naive?

We call it naive because its assumptions (it assumes that all of the features in the dataset are equally important and independent) are really optimistic and rarely true in most real-world applications:
we consider that these predictors are independent
we consider that all the predictors have an equal effect on the outcome (like the day being windy does not have more importance in deciding to play golf or not)

3. How does Random Forest handle missing values?

The Random Forest methods encourage two ways of handling missing values:
Drop data points with missing values. This is not recommended due to the fact that all the available data points is not used.
Fill in the missing values with the median (for numerical values) or mode (for categorical values). This method will brush too broad a stroke for datasets with many gaps and significant structure.
There are other methods of filling in missing values such as calculating the similarity between the missing features, and the missing values estimated by weighting.


4. Why does XGBoost perform better than SVM?

In case of missing values, XGB is internally designed to handle missing values. The missing values are interpreted in such a way that if there endures any trend in the missing values, it is captured by the model. Users are required to supply a different value than other observations and pass that as a parameter. XGBoost tries different things as it encounters a missing value on each node and learns which path to take for missing values in future. On the other hand, Support Vector Machine (SVM) does not perform well with the missing data and it is always a better option to impute the missing values before running SVM.

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Date: 21-02-2023
Company name: Tredence
Role: Data Analyst
Topic: data integrity, joining vs blending, slicing, now and current date

1. What is Data Integrity?

Data Integrity is the assurance of accuracy and consistency of data over its entire life-cycle and is a critical aspect of the design, implementation, and usage of any system which stores, processes, or retrieves data. It also defines integrity constraints to enforce business rules on the data when it is entered into an application or a database.

2. What is the Difference Between Joining and Blending in Tableau?

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 slicing in Python?

As the name suggests, ‘slicing’ is taking parts of.
Syntax for slicing is [start : stop : step]
start is the starting index from where to slice a list or tuple
stop is the ending index or where to stop.
step is the number of steps to jump.
Default value for start is 0, stop is number of items, step is 1.
Slicing can be done on strings, arrays, lists, and tuples.

4. What is the difference between NOW() and CURRENT_DATE() in SQL?

NOW() returns a constant time that indicates the time at which the statement began to execute. (Within a stored function or trigger, NOW() returns the time at which the function or triggering statement began to execute.
The simple difference between NOW() and CURRENT_DATE() is that NOW() will fetch the current date and time both in format ‘YYYY-MM_DD HH:MM:SS’ while CURRENT_DATE() will fetch the date of the current day ‘YYYY-MM_DD’.

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Date: 22-02-2023
Company name: CRED
Role: SQL Developer
Topic: subsets, normalisation, relationship

1. What are the different subsets of SQL?

Data Definition Language (DDL) – It allows you to perform various operations on the database such as CREATE, ALTER, and DELETE objects.
Data Manipulation Language(DML) – It allows you to access and manipulate data. It helps you to insert, update, delete and retrieve data from the database.
Data Control Language(DCL) – It allows you to control access to the database. Example – Grant, Revoke access permissions.

2. List the different types of relationships in SQL.

There are different types of relations in the database:
One-to-One – This is a connection between two tables in which each record in one table corresponds to the maximum of one record in the other.
One-to-Many and Many-to-One – This is the most frequent connection, in which a record in one table is linked to several records in another.
Many-to-Many – This is used when defining a relationship that requires several instances on each sides.
Self-Referencing Relationships – When a table has to declare a connection with itself, this is the method to employ.

3. How to create empty tables with the same structure as another table?

To create empty tables:
Using the INTO operator to fetch the records of one table into a new table while setting a WHERE clause to false for all entries, it is possible to create empty tables with the same structure. As a result, SQL creates a new table with a duplicate structure to accept the fetched entries, but nothing is stored into the new table since the WHERE clause is active.

4. What is Normalization and what are the advantages of it?

Normalization in SQL is the process of organizing data to avoid duplication and redundancy. Some of the advantages are:
Better Database organization
More Tables with smaller rows
Efficient data access
Greater Flexibility for Queries
Quickly find the information
Easier to implement Security

Date: 23-02-2023
Company name: IKEA
Role: Data Scientist
Topic: stacking,cursors, powerbi, charts

1. How does Stacking work?
The idea of stacking is to learn several different weak learners and combine them by training a meta-model to output predictions based on the multiple predictions returned by these weak models.
If a stacking ensemble is composed of L weak learners, then to fit the model the following steps are followed:
Split the training data into two folds.
Choose L weak learners and fit them to the data of the first fold.
For each of the L weak learners, make predictions for observations in the second fold.
Fit the meta-model on the second fold, using predictions made by the weak learners as inputs.

2. Can you provide me examples of when a scatter graph would be more appropriate than a line chart or vice versa?
A scatter graph would be more appropriate than a line chart when you are looking to show the relationship between two variables that are not linearly related. For example, if you were looking to show the relationship between a person’s age and their weight, a scatter graph would be more appropriate than a line chart. A line chart would be more appropriate than a scatter graph when you are looking to show a trend over time. For example, if you were looking at the monthly sales of a company over the course of a year, a line chart would be more appropriate than a scatter graph.

3. Where is data stored in Power BI?
When data is ingested into Power BI, it is basically stored in Fact and Dimension tables.
Fact tables: The central table in a star schema of a data warehouse, a fact table stores quantitative information for analysis and is not normalized in most cases.
Dimension tables: It is just another table in the star schema that is used to store attributes and dimensions that describe objects stored in a fact table.


4. What is Cursor? How to use a Cursor?
After any variable declaration, DECLARE a cursor. A SELECT Statement must always be coupled with the cursor definition.
To start the result set, move the cursor over it. Before obtaining rows from the result set, the OPEN statement must be executed.
To retrieve and go to the next row in the result set, use the FETCH command.
To disable the cursor, use the CLOSE command.
Finally, use the DEALLOCATE command to remove the cursor definition and free up the resources connected with it.

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Date: 24-02-2023
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: 26-02-2023
Company name: Musigma
Role: ML Engineer
Topic: random forest, linear regression, naive bayes, gradient descent

1. How is the Error calculated in a Linear Regression model?

Measuring the distance of the observed y-values from the predicted y-values at each value of x.
Squaring each of these distances.
Calculating the mean of each of the squared distances.
MSE = (1/n) * Σ(actual – forecast)2
The smaller the Mean Squared Error, the closer you are to finding the line of best fit
How bad or good is this final value always depends on the context of the problem, but the main goal is that its value is as minimal as possible.

2. Explain the intuition behind the Gradient Descent algorithm.

Gradient descent is an optimization algorithm that’s used when training a machine learning model and is based on a convex function and tweaks its parameters iteratively to minimize a given function to its local minimum (that is, slope = 0).
For a start, we have to select a random bias and weights, and then iterate over the slope function to get a slope of 0.
The way we change update the value of the bias and weights is through a variable called the learning rate. We have to be wise on the learning rate because choosing:
A small leaning rate may lead to the model to take some time to learn
A large learning rate will make the model converge as our pointer will shoot and we’ll not be able to get to minima.

3. How is a Random Forest related to Decision Trees?

Random forest is an ensemble learning method that works by constructing a multitude of decision trees. A random forest can be constructed for both classification and regression tasks.
Random forest outperforms decision trees, and it also does not have the habit of overfitting the data as decision trees do.
A decision tree trained on a specific dataset will become very deep and cause overfitting. To create a random forest, decision trees can be trained on different subsets of the training dataset, and then the different decision trees can be averaged with the goal of decreasing the variance.

4. What are some disadvantages of using Naive Bayes Algorithm?

Some disadvantages of using Naive Bayes Algorithm are:
It relies on a very big assumption that the independent variables are not related to each other.
It is generally not suitable for datasets with large numbers of numerical attributes.
It has been observed that if a rare case is not in the training dataset but is in the testing dataset, then it will most definitely be wrong.

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Date - 27-02-2023
Company name: Walmart
Role: Data Scientist
Topic: kmean, cnn layers, constraint in SQL, tableau

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. Explain the different layers in CNN.

The different layers involved in the architecture of CNN are as follows:
1. Input Layer: The input layer in CNN should contain image data. Image data is represented by a three-dimensional matrix. We have to reshape the image into a single column.
For Example, Suppose we have an MNIST dataset and you have an image of dimension 28 x 28 =784, you need to convert it into 784 x 1 before feeding it into the input. If we have “k” training examples in the dataset, then the dimension of input will be (784, k).
2. Convolutional Layer: To perform the convolution operation, this layer is used which creates several smaller picture windows to go over the data.
3. ReLU Layer: This layer introduces the non-linearity to the network and converts all the negative pixels to zero. The final output is a rectified feature map.

3. What Would You Do If Some Countries/Provinces (Any Geographical Entity) are Missing and Displaying a Null When You Use Map View in Tableau?

When working with maps and geographical fields, unknown or ambiguous locations are identified by the indicator in the lower right corner of the view.
Click the indicator and choose from the following options:
Edit Locations - correct the locations by mapping your data to known locations
Filter Data - exclude the unknown locations from the view using a filter. The locations will not be included in calculations
Show Data at Default Position - show the values at the default position of (0, 0) on the map.

4. What are Constraints in SQL?

Constraints are used to specify the rules concerning data in the table. It can be applied for single or multiple fields in an SQL table during the creation of the table or after creating using the ALTER TABLE command. The constraints are:
NOT NULL - Restricts NULL value from being inserted into a column.
CHECK - Verifies that all values in a field satisfy a condition.
DEFAULT - Automatically assigns a default value if no value has been specified for the field.
UNIQUE - Ensures unique values to be inserted into the field.
INDEX - Indexes a field providing faster retrieval of records.
PRIMARY KEY - Uniquely identifies each record in a table.
FOREIGN KEY - Ensures referential integrity for a record in another table.

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Date - 28-02-2023
Company name: IBM
Role: Data Analyst
Topic: Clustering, excel, SQL, parameter in tableau

1. What is Density-based Clustering?

Density-Based Clustering is an unsupervised machine learning method that identifies different groups or clusters in the data space. These clustering techniques are based on the concept that a cluster in the data space is a contiguous region of high point density, separated from other such clusters by contiguous regions of low point density.

Partition-based(K-means) and Hierarchical clustering techniques are highly efficient with normal-shaped clusters while density-based techniques are efficient in arbitrary-shaped clusters or detecting outliers.

2. How to create empty tables with the same structure as another table?

To create empty tables:
Using the INTO operator to fetch the records of one table into a new table while setting a WHERE clause to false for all entries, it is possible to create empty tables with the same structure. As a result, SQL creates a new table with a duplicate structure to accept the fetched entries, but nothing is stored into the new table since the WHERE clause is active.

3. What is a Parameter in Tableau? Give an Example.

A parameter is a dynamic value that a customer could select, and you can use it to replace constant values in calculations, filters, and reference lines.

For example, when creating a filter to show the top 10 products based on total profit instead of the fixed value, you can update the filter to show the top 10, 20, or 30 products using a parameter.

4. How will you write the formula for the following in Excel? - Multiply the value in cell A1 by 10, add the result by 5, and divide it by 2.

To write a formula for the above-stated question, we have to follow the PEDMAS Precedence. The correct answer is ((A1*10)+5)/2.

Answers such as =A1*10+5/2 and =(A1*10)+5/2 are not correct. We must put parentheses brackets after a particular operation.

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