CloudyML - Data Science & Analytics

Date: 06-02-2024
Company name: Elica
Role: Data Scientist
Topic: kmeans, KNN, LSTM, powerbi, SQL

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. If your Time-Series Dataset is very long, what architecture would you use?

If the dataset for time-series is very long, LSTMs are ideal for it because it can not only process single data points, but also entire sequences of data. A time-series being a sequence of data makes LSTM ideal for it.For an even stronger representational capacity, making the LSTM's multi-layered is better.Another method for long time-series dataset is to use CNNs to extract information.

3. How would you define Power BI as an effective solution ?

Power BI is a strong business analytical tool that creates useful insights and reports by collating data from unrelated sources. This data can be extracted from any source like Microsoft Excel or hybrid data warehouses. Power BI drives an extreme level of utility and purpose using interactive graphical interface and visualizations.

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.

5. Explain the difference between drop and truncate.

In SQL, the DROP command is used to remove the whole database or table indexes, data, and more. Whereas the TRUNCATE command is used to remove all the rows from the table.

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25.1K views14:56

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CloudyML - Data Science & Analytics

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21.5K viewsedited 14:16

CloudyML - Data Science & Analytics

👇🏻Data Engineering Interview QnA👇🏻

➡️Can you discuss the pros and cons of using Hadoop for real-time processing?

Hadoop was originally designed for batch processing of large datasets and may not be suitable for real-time processing due to its high latency and limited support for streaming data. However, newer technologies such as Apache Spark and Apache Flink provide faster processing and better support for streaming data. Hadoop's strengths lie in its scalability, fault tolerance, and ability to handle large datasets efficiently.

➡️How do you handle input data that is not in the expected format in a MapReduce job?

Handling input data that is not in the expected format can be challenging in a MapReduce job. One approach is to use a custom InputFormat that can parse the data into the desired format before processing it. Another approach is to use a custom mapper that can transform the input data into the expected format. If the input data cannot be transformed easily, it may be necessary to preprocess the data outside of Hadoop before ingesting it into the Hadoop cluster.

➡️Explain The Five Vs of Big Data.

The five Vs of Big Data are –
Volume – Amount of data in the Petabytes and Exabytes
Variety – Includes formats like an videos, audio sources, textual data, etc.
Velocity – Everyday data growth which are includes conversations in forums,blogs,social media posts,etc.
Veracity – Degree of accuracy of data are available
Value – Deriving insights from collected data to the achieve business milestones and new heights

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26.4K views17:28

CloudyML - Data Science & Analytics

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06:17

CloudyML - Data Science & Analytics

Date - 14-02-2024
Company name: Elica
Role: ML Engineer
Topic: PCA, SGD, decision tree, ARM

1. What is the difference between stochastic gradient descent (SGD) and gradient descent (GD)?

Gradient Descent and Stochastic Gradient Descent are the algorithms that find the set of parameters that will minimize a loss function.
The difference is that in Gradient Descent, all training samples are evaluated for each set of parameters. While in Stochastic Gradient Descent only one training sample is evaluated for the set of parameters identified.

2. Can you mention some advantages and disadvantages of decision trees?

The advantages of decision trees are that they are easier to interpret, are nonparametric and hence robust to outliers, and have relatively few parameters to tune.
On the other hand, the disadvantage is that they are prone to overfitting.

3. What do you mean by Associative Rule Mining (ARM)?

Associative Rule Mining is one of the techniques to discover patterns in data like features (dimensions) which occur together and features (dimensions) which are correlated. It is mostly used in Market-based Analysis to find how frequently an itemset occurs in a transaction. Association rules have to satisfy minimum support and minimum confidence at the very same time.

4. What is the Principle Component Analysis?

The idea here is to reduce the dimensionality of the data set by reducing the number of variables that are correlated with each other. Although the variation needs to be retained to the maximum extent.
The variables are transformed into a new set of variables that are known as Principal Components’. These PCs are the eigenvectors of a covariance matrix and therefore are orthogonal.

Stay Safe & Happy Learning💙

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24.4K viewsedited 05:33

CloudyML - Data Science & Analytics

Date: 15-02-2024
Company name : Walmart
Role: ML Engineer
Topic: Cross validation, bagging, boosting, sampling

1. What is Cross-validation in Machine Learning?

Cross-validation is a resampling method that uses different portions of the data to test and train a model on different iterations. It is mainly used in settings where the goal is prediction, and one wants to estimate how accurately a predictive model will perform in practice This sampling process is done to break the dataset into smaller parts that have the same number of rows, out of which a random part is selected as a test set and the rest of the parts are kept as train sets. Cross-validation consists of the following techniques:
• Holdout method
• K-fold cross-validation
• Stratified k-fold cross-validation
• Leave p-out cross-validation

2. What is bagging and boosting in Machine Learning?

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.

3.What is systematic sampling and cluster sampling ?

Systematic sampling is a type of probability sampling method. The sample members are selected from a larger population with a random starting point but a fixed periodic interval. This interval is known as the sampling interval. The sampling interval is calculated by dividing the population size by the desired sample size.

Cluster sampling involves dividing the sample population into separate groups, called clusters. Then, a simple random sample of clusters is selected from the population. Analysis is conducted on data from the sampled clusters.

4.What is market basket analysis?

Market Basket Analysis is a modeling technique based upon the theory that if you buy a certain group of items, you are more (or less) likely to buy another group of items.

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26K views06:03

CloudyML - Data Science & Analytics

Date: 22-02-2024
Company name: IKEA
Role: Data Analyst
Topic: Statistics, SQL

1. How can we deal with problems that arise when the data flows in from a variety of sources?

There are many ways to go about dealing with multi-source problems. However, these are done primarily to solve the problems of:

Identifying the presence of similar/same records and merging them into a single recordRe-structuring the schema to ensure there is good schema integration

2. Where is Time Series Analysis used?

Since time series analysis (TSA) has a wide scope of usage, it can be used in multiple domains. Here are some of the places where TSA plays an important role:

Statistics
Signal processing
Econometrics
Weather forecasting
Earthquake prediction
Astronomy
Applied science

3. What are the ideal situations in which t-test or z-test can be used?

It is a standard practice that a t-test is used when there is a sample size less than 30 and the z-test is considered when the sample size exceeds 30 in most cases.

4. What is the usage of the NVL() function?

The NVL() function is used to convert the NULL value to the other value. The function returns the value of the second parameter if the first parameter is NULL. If the first parameter is anything other than NULL, it is left unchanged. This function is used in Oracle, not in SQL and MySQL. Instead of NVL() function, MySQL have IFNULL() and SQL Server have ISNULL() function.

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

If a table is dropped, all things associated with that table are dropped as well. This includes the relationships defined on the table with other tables, access privileges, and grants that the table has, as well as the integrity checks and constraints.

However, if a table is truncated, there are no such problems as mentioned above. The table retains its original structure and the data is dropped.

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18.1K views05:51

CloudyML - Data Science & Analytics

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16.7K views09:38

CloudyML - Data Science & Analytics

Date: 23-02-2024
Company name: Oracle
Role: Data Analyst
Topic: outlier, dax, filter, recursive stored procedure

1. What are the ways to detect outliers?

Outliers are detected using two methods:

Box Plot Method: According to this method, the value is considered an outlier if it exceeds or falls below 1.5*IQR (interquartile range), that is, if it lies above the top quartile (Q3) or below the bottom quartile (Q1).

Standard Deviation Method: According to this method, an outlier is defined as a value that is greater or lower than the mean ± (3*standard deviation).

2. What is a Recursive Stored Procedure?

A stored procedure that calls itself until a boundary condition is reached, is called a recursive stored procedure. This recursive function helps the programmers to deploy the same set of code several times as and when required.

3. What is the shortcut to add a filter to a table in EXCEL?

The filter mechanism is used when you want to display only specific data from the entire dataset. By doing so, there is no change being made to the data. The shortcut to add a filter to a table is Ctrl+Shift+L.

4. What is DAX in Power BI?

DAX stands for Data Analysis Expressions. It's a collection of functions, operators, and constants used in formulas to calculate and return values. In other words, it helps you create new info from data you already have.

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19.8K views06:15

CloudyML - Data Science & Analytics

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20.7K viewsedited 14:42

CloudyML - Data Science & Analytics

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14:29

CloudyML - Data Science & Analytics

Date - 26-02-2024
Company name: IBM
Role: Data Analyst
Topic: rank vs dense rank, 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.

5. What is the difference between the RANK() and DENSE_RANK() functions?

The RANK() function in the result set defines the rank of each row within your ordered partition. If both rows have the same rank, the next number in the ranking will be the previous rank plus a number of duplicates. If we have three records at rank 4, for example, the next level indicated is 7. The DENSE_RANK() function assigns a distinct rank to each row within a partition based on the provided column value, with no gaps. If we have three records at rank 4, for example, the next level indicated is 5.

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19K views06:21

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