iTECH PODCAST

20. What is “Clustering?” Name the properties of clustering algorithms.

The sample answer is…

Clustering is a method in which data is classified into clusters and groups. A clustering algorithm has the following properties:

· Hierarchical or flat

· Hard and soft

· Iterative

· Disjunctive

---

This episode is sponsored by

· Anchor: The easiest way to make a podcast. https://anchor.fm/app

19. How can you define outlier?

The sample answer is

A data analyst interview question and answers guide will not complete without this question. An outlier is a term commonly used by data analysts when referring to a value that appears to be far removed and divergent from a set pattern in a sample. There are two kinds of outliers – Univariate and Multivariate.

The two methods used for detecting outliers are:

· Box plot method – According to this method, if the value is higher or lesser than 1.5*IQR (interquartile range), such that it lies above the upper quartile (Q3) or below the lower quartile (Q1), the value is an outlier.

· Standard deviation method – This method states that if a value is higher or lower than mean ± (3*standard deviation), it is an outlier.

---

This episode is sponsored by

· Anchor: The easiest way to make a podcast. https://anchor.fm/app

18. Which is the name of the different data validation methods used by data analysts?

There are many ways to validate datasets. Some of the most commonly used data validation methods by data analysts .

The sample answer is…

1. Field level validation – in this method, data validation is done in each field as and when a user enters the data. It helps to correct the errors as you go.

2. From level validation – in this method, the data is validated after the user completes the form and submits it. It checks the entire data entry form at once, validates all the fields in it, and highlights the errors(if any) so that the user can correct it.

3. Data saving validation – this data validation technique is used during the process of saving an actual file or database record. Usually, it is done when multiple data entry forms must be validated.

4. Search criteria validation – this validation technique is used to offer the user accurate and related matches for their searched keywords or phrases. The main purpose of this validation method is to ensure that the user’s search queries can return the most relevant results.

---

This episode is sponsored by

· Anchor: The easiest way to make a podcast. https://anchor.fm/app

17.  What should a data analyst do with missing or suspected data?

A data analyst need to:

1. Use data analysis strategies like deletion method, single imputation method and model based methods to detect missing data.

2. Prepare a validation report containing all information about the suspected or missing data

3. Scrutinize the suspicious data to assess their validity.

4. Replace all the invalid data (if any) with a proper validation code.

---

This episode is sponsored by

· Anchor: The easiest way to make a podcast. https://anchor.fm/app

16. What is the KNN imputation method ?

KNN (K — Nearest Neighbors) is one of many (supervised learning) algorithms used in data mining and machine learning, it's a classifier algorithm where the learning is based “how similar” is a data (a vector) from other .

The sample answer is…

This method is used to impute the missing attribute values which are imputed by the attribute values that are most similar to the attribute whose values are missing. The similarity of the two attributes is determined by using the distance function.

---

This episode is sponsored by

· Anchor: The easiest way to make a podcast. https://anchor.fm/app

15. What are the important responsibilities of a data analyst?

This is the most commonly asked data analyst interview question. You must have a clear idea as to what your job entails.

The sample answer is…

1. Collect and interpret data from multiple sources and analyze results.

2. Filter and clean data gathered from multiple sources.

3. Analyze complex datasets and identify the hidden patterns in them.

4. Keep databases secured.

---

This episode is sponsored by

· Anchor: The easiest way to make a podcast. https://anchor.fm/app

14. what are the key requirements for becoming a data analyst?

This data analyst interview question tests your knowledge about the required skill set to become a data analyst.

The sample answer is …

1. Be well versed with programming language like python, Sql , databases like MySql, SQLite and also have extensive knowledge on reportingas well as data analyzing packages like MsExcel, Tableau, Power Bi and Powerpoint for data presenation.

2. Be able to analyze, organize, collect and disseminate big data efficiently.

3. Must have substantial technical knowledge in fields like database design, data mining and segmentation techniques

---

This episode is sponsored by

· Anchor: The easiest way to make a podcast. https://anchor.fm/app

13. What is difference between Data mining and data Analysis?

Before we discuss in question number 6 . what is difference between data mining and data profiling? Today we discuss about what is difference between data mining and data analysis.

The sample answer is…

1. Data mining – used to recognize patterns in data stored.

1. Data analysis – used to order and organize raw data in a meaningful manner.

2. Data mining – mining is performed on clean and well documented data.

2. Data analysis –the analysis of data involves data cleaning . so , data is not present in a well documented format.

3. Result extracted from data mining are not easy to interpret.

3. Result extracted from data analysis are easy to interpret.

---

This episode is sponsored by

· Anchor: The easiest way to make a podcast. https://anchor.fm/app

12. How can you handle missing values in a dataset?

The sample answer is…

1. Listwise deletion – in listwise deletion method, an entire record is excluded from analysis if any single value is missing

2. Average imputation – use the average value of the responses from the other participants to fill in the missing value

3. Regression substitution – You can use multiple-regression analysis to estimate a missing value

4. Multiple imputation - It creates plausible values based on the correlations for the missing data and then averages the simulated datasets by incorporating random errors in your predications.

---

This episode is sponsored by

· Anchor: The easiest way to make a podcast. https://anchor.fm/app

11. What are the best practices for data cleaning?

If you are sitting for a data analyst job, this is one of the most frequently asked data analyst interview questions.

Data cleansing primarily refers to the process of detecting and removing errors and inconsistencies from the data to improve data quality.

The sample answer is…

1. Make a data cleaning plan by understanding where the common error take place and keep communication open

2. Identity and remove duplicates values before working with the data. This will lead to an effective data analysis process

3. Focus on the accuracy of the data. Maintain the value types of data, provide a mandatory constraints and set cross-field validation.

4. Standardize the data at the point of entry so that it is less chaotic and you will be able to ensure that all information is standardized, leading to fewer errors on entry.

---

This episode is sponsored by

· Anchor: The easiest way to make a podcast. https://anchor.fm/app