Sunday, March 17, 2024

Python Interview Questions For Data Analyst

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Q6 Can You Tell Me What Are Eigenvectors And Eigenvalues

Data Analysis Interview Questions & Answers Using Python | #1 (Dropbox)

Eigenvectors: Eigenvectors are basically used to understand linear transformations. These are calculated for a correlation or a covariance matrix.

For definition purposes, you can say that Eigenvectors are the directions along which a specific linear transformation acts either by flipping, compressing or stretching.

Eigenvalue: Eigenvalues can be referred to as the strength of the transformation or the factor by which the compression occurs in the direction of eigenvectors.

What If I Miss A Session Of This Python Course

At Intellipaat, you need not worry about missing a class. In the LMS, we provide recorded videos of each session so that learners can refer to it at any time. Our support team will also be available to assist you with any such concerns. So, if you miss any session, simply contact the support team. They will schedule an extra class and one-on-one meetings with your trainers to let you catch up.

What Is The Difference Between A Heatmap And A Treemap

Heatmap Treemap
A heat map is used to compare the categories with color and size. A treemap is a powerful visualization that does the same as that of the heat map.
With heat maps, we can compare two different measures together. Treemaps are also used for illustrating hierarchical data and part-to-whole relationships.

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What Is The Difference Between R

R-Squared and Adjusted R-Squared are both data analysis techniques.

  • R-Squared technique: The R-Squared technique is a statistical measure of the variation in the dependent variables, as explained by the independent variables.
  • Adjusted R-Squared technique: The Adjusted R-Squared technique is a modified version of the R-squared technique, adjusted for the number of predictors in a model. It provides the percentage of variation explained by the specific independent variables that directly impact the dependent variables.

Python Coding Interview Question #: Class Performance

merge sort in python in 2022

This Box interview question asks you:

You are given a table containing assignment scores of students in a class. Write a query that identifies the largest difference in total score of all assignments. Output just the difference in total score between the two students.

Link to the question: Table you need to use is box_scores, which has the following columns:

id

Data from the table look like this:

As a first step towards answering the question, you should sum the scores from all assignments:

import pandas as pdimport numpy as npbox_scores = box_scores+box_scores+box_scores

This part of the code will give you this:

Now that you know that, the next step is to find the largest difference between the total scores. You need to use the max and min functions to do that. Or, to be more specific, a difference between these two functions output. Add this to the above code, and youve got a final answer:

import pandas as pdimport numpy as npbox_scores = box_scores+box_scores+box_scoresbox_scores.max - box_scores.min

This is the output youre looking for:

The question asked to output only this difference, so no other columns are needed.

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Q218 What Is The Difference Between Pass Continue And Break

Pass: It is used when you need some block of code syntactically, but you want to skip its execution. This is basically a null operation. Nothing happens when this is executed.

Continue: It allows to skip some part of a loop when some specific condition is met, and the control is transferred to the beginning of the loop. The loop does not terminate but continues with the next iteration.

Break: It allows the loop to terminate when some condition is met, and the control of the program flows to the statement immediately after the body of the loop. If the break statement is inside a nested loop , then the break statement will terminate the innermost loop.

Q1 What Is The Difference Between Data Mining And Data Analysis

Data Mining
Used to recognize patterns in data stored. Used to order & organize raw data in a meaningful manner.
Mining is performed on clean and well-documented data. The analysis of data involves Data Cleaning. So, data is not present in a well-documented format.
Results extracted from data mining are not easy to interpret. Results extracted from data analysis are easy to interpret.

Table 1: Data Mining vs Data Analysis Data Analyst Interview Questions

So, if you have to summarize, Data Mining is often used to identify patterns in the data stored. It is mostly used for Machine Learning, and analysts have to just recognize the patterns with the help of algorithms. Whereas, Data Analysis is used to gather insights from raw data, which has to be cleaned and organized before performing the analysis.

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Technical Data Analyst Interview Questions:

24.What are some of the best tools you used for data analysis and presentation? \ What data analytics software are you familiar with?

25.What are the most beneficial statistical methods for Data Analysts? \ Which statistical methods have you used so far?

26.What are the characteristics of a good data model?

27.What was the largest data set youve worked with?

28.What are the most useful tools for data analytics?

29.Which scripting languages are you familiar with?

30.What are the different data validation methods you use?

31.What are the different types of Hypothesis testing?

32.What are the different types of sampling techniques used by data analysts?

33.As a Data Analyst, what steps do you take when theres suspected or missing data? Walk me through your data visualization\ data validation process.

34.As a Data Analyst, how do you treat outliers in a dataset? \ How do you use the box plot method?

35.When theres a multi-source problem, how do you tackle it?

36.What is a hash table collision, and how do you prevent it from happening?

37.What is the significance of Exploratory Data Analysis?

38.Please explain the concept of Hierarchical clustering algorithm?

39.What does Data Cleansing mean? What are the best ways to practice it? \ Can you tell me what Data Cleansing means how do you practice data cleansing?

40.What are some of the best practices for data cleaning? What steps do you take? \ Please create a data cleaning plan showcasing some of the best methods to do so?

Whats The Largest Data Set Youve Worked With

Data Analyst Interview Question 4 – Python #shorts

What theyâre really asking: Can you handle large data sets?

Many businesses have more data at their disposal than ever before. Hiring managers want to know that you can work with large, complex data sets. Focus your answer on the size and type of data. How many entries and variables did you work with? What types of data were in the set?

The experience you highlight doesn’t have to come from a job. Youâll often have the chance to work with data sets of varying sizes and types as a part of a data analysis course, bootcamp, certificate program, or degree. As you put together a portfolio, you may also complete some independent projects where you find and analyze a data set. All of this is valid material to build your answer.

Interviewer might also ask:

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Explain The Constraints In Sql

SQL constraints are used to specify rules for data in the table.

  • NOT NULL: NOT NULL constraint allows to specify that a column can not contain any NULL value.
  • UNIQUE: The UNIQUE constraint does not allow to insert a duplicate value in a column. It maintains the uniqueness of a column in a table. More than one UNIQUE column can be used in a table.
  • PRIMARY KEY: A PRIMARY KEY constraint for a table enforces the table to accept unique data for a specific column. This constraint creates a unique index for accessing the table faster.
  • FOREIGN KEY: A FOREIGN KEY creates a link between two tables by one specific column of both tables. The specified column in one table must be a PRIMARY KEY and referred by the column of another table known as FOREIGN KEY.
  • CHECK: A CHECK constraint controls the values in the associated column. It determines whether the value is valid or not from a logical expression.
  • DEFAULT: Each column must contain a value . While inserting data into a table, if no value is supplied to a column, the column gets the DEFAULT value.

What Are Dict And List Comprehensions

Python comprehensions are like decorators, that help to build altered and filtered lists, dictionaries, or sets from a given list, dictionary, or set. Comprehension saves a lot of time and code that might be considerably more complex and time-consuming.

Comprehensions are beneficial in the following scenarios:

  • Performing mathematical operations on the entire list
  • Performing conditional filtering operations on the entire list
  • Combining multiple lists into one
  • Flattening a multi-dimensional list
my_list = squared_list =   # list comprehension

# output =>

squared_dict =   # dict comprehension

# output =>

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Advanced Python Data Science Interview Questions And Answers

Go through the following python interview questions for data science that are slightly advanced. These python data science interview questions might be difficult for you to answer but it is important that you prepare for these python interview questions as well before going for your interview.

1) How will you use Pandas library to import a CSV file from a URL?

import pandas as pd

2) How will you transpose a NumPy array?

nparr.T

3) What are universal functions for n-dimensional arrays?

Universal functions are the functions that perform mathematical operations on each element of an n-dimensional array. Example: np.sqrt and np.exp evaluate square root and exponential of each element of an array respectively.

4) List a few statistical methods available for a NumPy array.

np.means, np.cumsum, np.sum,

5) What are boolean arrays? Write a code to create a boolean array using the NumPy library.

A boolean array is an array whose elements are of the boolean data type. A vital point to remember is that for boolean arrays, Python keywords and and or do not work.

Barr = np.array

6) What is Fancy Indexing?

IN NumPy, one can use an integer list to describe the indexing of NumPy arrays. For example, Array] for an array of dimensions 4×4 will print the rows in the order specified by the list.

7) What is NaT in Pythons Pandas library?

NaT stands for Not a Time. It is the NA value for timestamp data

8) What is Broadcasting for NumPy arrays?

This can be represented by the following image:

Five Excel Interview Questions For Data Analysts

Data Science Interview Questions and Answers (With images)

Here are five more questions specific to Excel that you might be asked during your interview:

1. What is a VLOOKUP, and what are its limitations?

2. What is a pivot table, and how do you make one?

3. How do you find and remove duplicate data?

4. What are INDEX and MATCH functions, and how do they work together?

5. Whatâs the difference between a function and a formula?

Need a quick refresher before your interview? Get a hands-on walkthrough of important functions and techniques in under 90 minutes with the Problem Solving Using Microsoft Excel.

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Python Coding Interview Question #1: Business Name Lengths

The next question is by the City of San Francisco:

Find the number of words in each business name. Avoid counting special symbols as words . Output the business name and its count of words.

Link to the question:

When answering the question, you should first find only distinct businesses using the drop_duplicates function. Then use the replace function to replace all the special symbols with blank, so you dont count them later. Use the split function to split the text into a list, and then use the len function to count the number of words.

What Is The Difference Between Variance And Covariance In Data Analysis

Variance and covariance are both statistical terms. Variance indicates how distant two numbers or quantities are concerning the mean value. So, it only specifies the magnitude of the relationship between the two quantities. It means how much the data is spread around the mean.

On the other hand, covariance is used to specify how two random variables will change together. So, we can say that covariance provides both the direction and magnitude of how two quantities vary concerning each other.

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How Can You Replace String Space With A Given Character In Python

It is a simple string manipulation challenge. You have to replace the space with a specific character.

Example 1: a user has provided the string l vey u and the character o, and the output will be loveyou.

Example 2: a user has provided the string D t C mpBl ckFrid yS le and the character a, and the output will be DataCampBlackFridaySale.

In the `str_replace` function, we will loop over each letter of the string and check if it is space or not. If it consists of space, we will replace it with the specific character provided by the user. Finally, we will be returning the modified string.

def str_replace:    result = ''    for i in text:             if i == ' ':                 i = ch              result += i     return resulttext = "D t C mpBl ckFrid yS le"ch = "a"str_replace# 'DataCampBlackFridaySale'

What Is Slicing In Python

Data Analysis Interview Questions & Answers Using Python | #2 (Amazon)

Slicing is a process used to select a range of elements from sequence data type like list, string and tuple. Slicing is beneficial and easy to extract out the elements. It requires a : which separates the start index and end index of the field. All the data sequence types List or tuple allows us to use slicing to get the needed elements. Although we can get elements by specifying an index, we get only a single element whereas using slicing we can get a group or appropriate range of needed elements.

Syntax:

List_name

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What Is The Process Of Data Analysis

Data analysis is the process of collecting, cleansing, interpreting, transforming, and modeling data to gather insights and generate reports to gain business profits. Refer to the image below to know the various steps involved in the process.

  • Collect Data: The data is collected from various sources and stored to be cleaned and prepared. In this step, all the missing values and outliers are removed.
  • Analyse Data: Once the data is ready, the next step is to analyze the data. A model is run repeatedly for improvements. Then, the model is validated to check whether it meets the business requirements.
  • Create Reports: Finally, the model is implemented, and then reports thus generated are passed onto the stakeholders.

What Is Hypothesis Testing Why Is It Needed And List Some Of The Statistical Tests

Hypothesis testing is the process in which statistical tests are used to check whether or not a hypothesis is true, using data. Based on hypothetical testing, we choose to accept or reject a hypothesis. It is needed to check whether the event is the result of a significant occurrence or merely of chance, hypothesis testing must be applied.

Some of the statistical tests are:

T-test: It is used to compare the means of two populations that is whether the given mean is significantly different from the sample mean or not. It can also be used to ascertain whether the regression line has a slope different from zero.

F-Test: It is used to determine the equality of the variances of the two normal populations. It can be also used to check if the data conforms to the regression model. In the Multiple Linear Regression model, examines the overall validity of the model or determines whether any of the independent variables is having a linear relationship with the dependent variable.

Chi-Square: It is used to check whether there is any statistically significant difference between the observed distribution and theoretical distribution.

ANOVA: It tests the equality of two or more population means by examining the variances of samples that are taken. ANOVA tests the general rather than specific differences among means.

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What Are The Typical Characteristics Of Elements In A List Vs In A Dictionary

Elements in both Python lists and dictionaries are mutable and dynamic. They can both be nested and are both able to be indexed. However, it is important to note that the index for a Python dictionary is referenced with a letter. This index is actually called a key. A list index is referenced with a numerical value, with the first element in the list having a value of 0 and the second element having a value of 1, and so on. As of Python version 3.7, a Python dictionary is no longer an unordered collection. In versions 3.6 and before, they were considered to be unordered collections as the items could not be referred to with an index.

Python Coding Interview Question #1: Positions Of Letter ‘a’

Interviewing for a senior data analyst position at a startup. What kind ...

This question by Amazon asks you to:

Find the position of the letter ‘a’ in the first name of the worker ‘Amitah’. Use 1-based indexing, e.g. position of the second letter is 2.

Link to the question:

There are two main concepts in the solution. The first is filtering the worker Amitah using the == operator. The second one is using the find function on a string to get the position of the letter a.

As a data scientist, youll be working with dates a lot. Depending on the data available, you could be asked to convert data to datetime, extract a certain period of time , or manipulate datetime in any other way thats suitable.

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