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Date: 05-11-2022
Company name: Snapdeal
Role: Data Analyst
Topic: SQL relationship, views, macros

1. 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.

2. 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.


3. 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.

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Date: 06-11-2022
Company Name: Accenture
Role: Data Scientist
Topic: Silhouette, trend seasonality, bag of words, bagging boosting

1. What do you understand by the term silhouette coefficient?

The silhouette coefficient is a measure of how well clustered together a data point is with respect to the other points in its cluster. It is a measure of how similar a point is to the points in its own cluster, and how dissimilar it is to the points in other clusters. The silhouette coefficient ranges from -1 to 1, with 1 being the best possible score and -1 being the worst possible score.


2. What is the difference between trend and seasonality in time series?

Trends and seasonality are two characteristics of time series metrics that break many models. Trends are continuous increases or decreases in a metric’s value. Seasonality, on the other hand, reflects periodic (cyclical) patterns that occur in a system, usually rising above a baseline and then decreasing again.


3. What is Bag of Words in NLP?

Bag of Words is a commonly used model that depends on word frequencies or occurrences to train a classifier. This model creates an occurrence matrix for documents or sentences irrespective of its grammatical structure or word order.


4. What is the difference between bagging and boosting?

Bagging is a homogeneous weak learners’ model that learns from each other independently in parallel and combines them for determining the model average. Boosting is also a homogeneous weak learners’ model but works differently from Bagging. In this model, learners learn sequentially and adaptively to improve model predictions of a learning algorithm

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Date: 07-11-2022
Company name: Brookfield
Role: ML Engineer
Topic: Gini index, gradient descent, logistic regression, dbscan

1. Explain Gradient Descent algorithm.

Ans. Gradient descent is an optimization algorithm used to minimize some function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In machine learning, we use gradient descent to update the parameters of our model. Parameters refer to coefficients in Linear Regression and weights in neural networks.

2. What is logistic regression used for classification instead of linear regression?

Ans. Using linear Regression , all predictions >= 0.5 can be considered as 1 and rest all < 0.5 can be considered as 0. But then the question arises why classification can’t be performed using it? Suppose we are classifying a mail as spam or not spam and our output is y, it can be 0(spam) or 1(not spam). In case of Linear Regression, hθ(x) can be > 1 or < 0. Although our prediction should be in between 0 and 1, the model will predict value out of the range i.e. maybe > 1 or < 0. So, that’s why for a Classification task, Logistic/Sigmoid Regression plays its role.


3. What is the Gini Index?

Ans. Gini Index is a score that evaluates how accurate a split is among the classified groups. Gini index evaluates a score in the range between 0 and 1, where 0 is when all observations belong to one class, and 1 is a random distribution of the elements within classes. In this case, we want to have a Gini index score as low as possible. Gini Index is the evaluation metrics we shall use to evaluate our Decision Tree Model.

4. Why is DBSCAN used over K means and other clustering methods?

Ans. Partitioning methods (K-means, PAM clustering) and hierarchical clustering work for finding spherical-shaped clusters or convex clusters. In other words, they are suitable only for compact and well-separated clusters. Moreover, they are also severely affected by the presence of noise and outliers in the data.
Real life data may contain irregularities, like:
Clusters can be of arbitrary shape like non convex clusters
Data may contain noise.
Given such data, k-means algorithm has difficulties in identifying these clusters with arbitrary shapes.

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Date: 08-11-2022
Company name: Capgemini
Role: Data Analyst
Topic: python, tableau

1. What is the Difference Between a Shallow Copy and Deep Copy in python?

Deep copy creates a different object and populates it with the child objects of the original object. Therefore, changes in the original object are not reflected in the copy. copy.deepcopy() creates a Deep Copy. Shallow copy creates a different object and populates it with the references of the child objects within the original object. Therefore, changes in the original object are reflected in the copy. copy.copy creates a Shallow Copy.

2. How can you remove duplicate values in a range of cells?

1. To delete duplicate values in a column, select the highlighted cells, and press the delete button. After deleting the values, go to the ‘Conditional Formatting’ option present in the Home tab. Choose ‘Clear Rules’ to remove the rules from the sheet.

2. You can also delete duplicate values by selecting the ‘Remove Duplicates’ option under Data Tools present in the Data tab.


3. Define shelves and sets in Tableau?

Shelves: Every worksheet in Tableau will have shelves such as columns, rows, marks, filters, pages, and more. By placing filters on shelves we can build our own visualization structure. We can control the marks by including or excluding data.
Sets: The sets are used to compute a condition on which the dataset will be prepared. Data will be grouped together based on a condition. Fields which is responsible for grouping are known assets. For example – students having grades of more than 70%.

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Date: 09-11-2022
Company name: Amazon
Role: Data Scientist
Topic: pooling, KNN, ER in SQL, Suite in python

1. What is Pooling in CNN, and How Does It Work?

Pooling is used to reduce the spatial dimensions of a CNN. It performs down-sampling operations to reduce the dimensionality and creates a pooled feature map by sliding a filter matrix over the input matrix.

2. what is suite in python?

A group of individual statements, which make a single code block are called suites in Python. Compound or complex statements, such as if, while, def, and class require a header line and a suite.

if expression :
suite
elif expression :
suite
else :
suite

3. Define Entity, Entity type, and Entity set.

Entity can be anything, be it a place, class or object which has an independent existence in the real world.

Entity Type represents a set of entities that have similar attributes.

Entity Set in the database represents a collection of entities having a particular entity type.


4. Why is the KNN Algorithm Considered a “Lazy Learner?”

The KNN method simply saves the training data when it receives it; it does not learn and create a model. Instead of using the training data to discover any discriminative functions, instance-based learning is used, and the training data is also used for making predictions on datasets that have not yet been viewed.

As a result, KNN is referred to as a “Lazy Learner” since it delays learning a model instead of doing so right away.

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Date: 10-11-2022
Company name: CRED
Role: ML Engineer
Topic: kmeans, dbscan, linear regression, boxplot

1. Is Feature Scaling required for the K means Algorithm?

Yes, K-Means typically needs to have some form of normalization done on the datasets to work properly since it is sensitive to both the mean and variance of the datasets.For performing feature scaling, generally, StandardScaler is recommended, but depending on the specific use cases, other techniques might be more suitable as well.


2. What are the assumptions of linear regression?

· Linearity of independent and dependent variable
· Errors should be normally distributed with mean of zero
· Errors have equal variance
· Errors are independent


3. What is the use of a box plot?

Box plots help visualize the distribution of quantitative values in a field. They are also valuable for comparisons across different categorical variables or identifying outliers, if either of those exist in a dataset.
Box plots typically detail the minimum value, 25th percentile (Q1), median (50th percentile), 75th percentile (Q3) and the maximum value in a visual manner.


4. How does the epsilon value affect the DBSCAN Clustering Algorithm?

The DBSCAN Algorithm is sensitive to the choice of epsilon. When we have clusters with varying densities, then two cases arise i.e.,
If epsilon is too small: In such cases, we define the sparser clusters as noise i.e, result in the elimination of sparse clusters as outliers.
If epsilon is too large: In such cases, the denser clusters may be merged together, which gives the incorrect clusters.

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Date: 11-11-2022
Company name: Walmart
role: Data Scientist
Topic: pooling, relative cell excel, where vs having, moving average

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: 12-11-2022
Company name: DataZymes
Role: Database Developer
Topic: E-R model, Attribute, Relation, database transaction, atomicity

1. What do you understand about the E-R model?
Answer: E-R model is an Entity-Relationship model which defines the conceptual view of the database.
The E-R model basically shows the real-world entities and their association/relations. Entities here represent the set of attributes in the database.

2. Explain the terms ‘Attribute’ and ‘Relations’
Answer:
Attribute is described as the properties or characteristics of an entity. For Example, Employee ID, Employee Name, Age, etc., can be attributes of the entity Employee.
Relation is a two-dimensional table containing a number of rows and columns where every row represents a record of the relation. Here, rows are also known as ‘Tuples’ and columns are known as ‘Attributes’.

3. What is the Database transaction?
Answer: Sequence of operation performed which changes the consistent state of the database to another is known as the database transaction. After the completion of the transaction, either the successful completion is reflected in the system or the transaction fails and no change is reflected.

4. What do you understand about ‘Atomicity’ and ‘Aggregation’?
Answer: Atomicity is the condition where either all the actions of the transaction are performed or none. This means, when there is an incomplete transaction, the database management system itself will undo the effects done by the incomplete transaction.
Aggregation is the concept of expressing the relationship with the collection of entities and their relationships.

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Date: 13-11-2022
Company name: Numerator
Role: Data Scientist
Topic: views, calculated field in tableau,
Rnn, arrays

1. What are the uses of using RNN in NLP?

The RNN is a stateful neural network, which means that it not only retains information from the previous layer but also from the previous pass. Thus, this neuron is said to have connections between passes, and through time.
For the RNN the order of the input matters due to being stateful. The same words with different orders will yield different outputs.
RNN can be used for unsegmented, connected applications such as handwriting recognition or speech recognition.


2. How to remove values to a python array?

Ans: Array elements can be removed using pop() or remove() method. The difference between these two functions is that the former returns the deleted value whereas the latter does not.


3. What are the advantages and disadvantages of views in the database?

Answer: Advantages of Views:
As there is no physical location where the data in the view is stored, it generates output without wasting resources.
Data access is restricted as it does not allow commands like insertion, updation, and deletion.
Disadvantages of Views:
The view becomes irrelevant if we drop a table related to that view.


4. How to create a calculated field in Tableau?

Click the drop down to the right of Dimensions on the Data pane and select “Create > Calculated Field” to open the calculation editor.
Name the new field and create a formula.

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Date: 14-11-2022
Company name: TCS
Role: Data Analyst
Topic: excel, data cleaning, data wrangling, types of error , Tableau

1. Define the term 'Data Wrangling.

Data Wrangling is the process wherein raw data is cleaned, structured, and enriched into a desired usable format for better decision making. It involves discovering, structuring, cleaning, enriching, validating, and analyzing data. This process can turn and map out large amounts of data extracted from various sources into a more useful format.

2. What are the best methods for data cleaning?

Create a data cleaning plan by understanding where the common errors take place and keep all the communications open. Before working with the data, identify and remove the duplicates. This will lead to an easy and effective data analysis process.Focus on the accuracy of the data. Set cross-field validation, maintain the value types of data, and provide mandatory constraints.Normalize the data at the entry point so that it is less chaotic. You will be able to ensure that all information is standardized, leading to fewer errors on entry.


3. Explain the Type I and Type II errors in Statistics?

In Hypothesis testing, a Type I error occurs when the null hypothesis is rejected even if it is true. It is also known as a false positive.

A Type II error occurs when the null hypothesis is not rejected, even if it is false. It is also known as a false negative.

4. How do you make a dropdown list in MS Excel?

First, click on the Data tab that is present in the ribbon.Under the Data Tools group, select Data Validation.Then navigate to Settings > Allow > List.Select the source you want to provide as a list array.

5. State some ways to improve the performance of Tableau?

Use an Extract to make workbooks run faster.
Reduce the scope of data to decrease the volume of data.
Reduce the number of marks on the view to avoid information overload.
Hide unused fields.
Use Context filters.
Use indexing in tables and use the same fields for filtering.
Remove unnecessary calculations and sheets.

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Date: 15-11-2022
Company name: Tiger Analytics
Role: Data Analyst
Topic: Distinct, pivot table, story , map & filter

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: 16-11-2022
Company name: Swiggy
Role: Data Analyst
Topic: python, SQL, excel, tableau

1. How many report formats are available in Excel?

There are three report formats available in Excel; they are:
1. Compact Form
2. Outline Form
3. Tabular Form


2. What are sets in Tableau?

Sets are custom fields that define a subset of data based on some conditions. A set can be based on a computed condition, for example, a set may contain customers with sales over a certain threshold. Computed sets update as your data changes. Alternatively, a set can be based on specific data point in your view.


3. What is the difference between DROP and TRUNCATE commands?

DROP command removes a table and it cannot be rolled back from the database whereas TRUNCATE command removes all the rows from the table.


4. What is slicing in Python?

Ans: Slicing is used to access parts of sequences like lists, tuples, and strings. The syntax of slicing is-[start:end:step]. The step can be omitted as well. When we write [start:end] this returns all the elements of the sequence from the start (inclusive) till the end-1 element. If the start or end element is negative i, it means the ith element from the end.

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Date: 17-Nov-2022
Company name: Swiggy
Role: Data Scientist
Topic: dropout in deep learning, sets in tableau, slicing in python, drop and truncate in SQL

1. What is the meaning of dropout in Deep Learning?

Dropout is a technique that is used to avoid overfitting a model in Deep Learning. If the dropout value is too low, then it will have minimal effect on learning. If it is too high, then the model can under-learn, thereby, causing lower efficiency.


2. What are sets in Tableau?

Sets are custom fields that define a subset of data based on some conditions. A set can be based on a computed condition, for example, a set may contain customers with sales over a certain threshold. Computed sets update as your data changes. Alternatively, a set can be based on specific data point in your view.

3. What is the difference between DROP and TRUNCATE commands?

DROP command removes a table and it cannot be rolled back from the database whereas TRUNCATE command removes all the rows from the table.


4. What is slicing in Python?

Ans: Slicing is used to access parts of sequences like lists, tuples, and strings. The syntax of slicing is-[start:end:step]. The step can be omitted as well. When we write [start:end] this returns all the elements of the sequence from the start (inclusive) till the end-1 element. If the start or end element is negative i, it means the ith element from the end.

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