When Is It Appropriate To Use The Next Statement In R
A data scientist will use next to skip an iteration in a loop. As an example:
This code iterates through a range of numbers from 1 to 20 and prints the values. I dont want to print 15, though, so Ive used the next statement to skip that iteration and move on to other values. The output would print 1-14 and 16-20.
Python Coding Interview Question #1: Customer Revenue In March
The last question is by Meta/Facebook:
Calculate the total revenue from each customer in March 2019. Include only customers who were active in March 2019.
Output the revenue along with the customer id and sort the results based on the revenue in descending order.
Link to the question:
Youll need to_datetime on the column order_date. Then extract March and the year 2019 from the same column. Finally, group by the cust_id and sum the column total_order_cost, which will be the revenue youre looking for. Use the sort_values to sort the output according to revenue in descending order.
Q3 What Is Acid Property In A Database
ACID is an acronym for Atomicity, Consistency, Isolation, and Durability. This property is used in the databases to ensure whether the data transactions are processed reliably in the system or not. If you have to define each of these terms, then you can refer below.
- Atomicity: Refers to the transactions which are either completely successful or failed. Here a transaction refers to a single operation. So, even if a single transaction fails, then the entire transaction fails and the database state is left unchanged.
- Consistency: This feature makes sure that the data must meet all the validation rules. So, this basically makes sure that the transaction never leaves the database without completing its state.
- Isolation: Isolation keeps transactions separated from each other until theyre finished. So basically each and every transaction is independent.
- Durability: Durability makes sure that your committed transaction is never lost. So, this guarantees that the database will keep track of pending changes in such a way that even if there is a power loss, crash or any sort of error the server can recover from an abnormal termination.
Read Also: Social Media Manager Interview Assignment
Can You Tell Me What Data Cleansing Means And How You Practice This
The goal of this question is to assess a candidate’s ability to detect and remove any data inconsistencies or errors. You can also gauge their confidence and communication skills. Sometimes an analyst must discuss a project directly with the client, and they should possess professional communication skills. What to look for in an answer:
- Systematic approach
- Attention to detail and accuracy
- Critical thinking skills
“The term data cleansing refers to the process of locating and correcting inaccurate or corrupt data. I employ several practices for improved data quality. The first is breaking up large chunks into smaller datasets before cleaning. The second is to track data cleansing operations to allow easy removal or addition from datasets. I also create scripts to handle frequent cleaning tasks, which saves time and improves accuracy.”
Prepare For Your Data Analyst Interview
Because of its wide range of applications in data science and analytics, you may also be questioned about your knowledge of Python. Use this list of practice Python interview questions to refamiliarize yourself with the language’s key components and concepts.
If you need a more in-depth refresher of the skills and knowledge you’ll need as a data analyst, check out our Data Scientist: Analytics Specialist career path. We’ll cover everything you need to know, help you build a portfolio, and even give you some more tips on interviewing.
Or, if you’re starting fresh, check out our data science career guide. We’ll help you find the right path toward your future career in the field.
Recommended Reading: Microservices Design Patterns Interview Questions
Q220 What Are Namespaces In Python
A namespace is a naming system that is used to ensure that every object has a unique name. It is like space is assigned to every variable which is mapped to the object. So, when we call out this variable, this assigned space or container is searched and hence the corresponding object as well. Python maintains a dictionary for this purpose.
Data Analyst Interview Questions: Common Questions And Answers
Congratulations! You’ve landed an interview for a data analyst position at the perfect company. You’re only inches away from your dream career, and all you need to do to seal the deal is answer a few questions.
Of course, it’s best to practice first. To make sure you’re well-prepared for your upcoming interview, we’ll walk you through some common data analyst interview questions. Read on for a mix of technical and behavioral questions that’ll help refresh your understanding of data analytics’ fundamentals and key principles.
Also Check: How Does A Job Interview Go
Data Mining Vs Data Profiling: What Is The Difference
Data mining involves processing data to find patterns that were not immediately emergent in it. The focus is on analyzing the dataset and detecting dependencies and correlations within it.
Data profiling, on the other hand, implies identifying the attributes of the data in a dataset. That includes attributes such as datatype, distributions, and functional dependencies.
Data Analysis Process Questions:
What is data cleaning, and how do you do it?
Data cleaning takes up a large part of your work hours as a data analyst. Here is a chance to show the interviewer how you handle the process, including missing data, duplicates, outliers and more. Be sure to explain why it is important, and how you have dealt with it in past projects.
How do you communicate technical concepts to a non-technical audience?
Much of data analysis involves ordering your findings into a narrative, and clearly explaining it to both technical and non-technical audiences. This is where your soft skills come in: communication and storytelling. Give examples of how youve drawn insights from data and communicated those to audiences. These might include presentations to shareholders or written communication within your portfolio.
How would you go about measuring the performance of our company?
When an interviewer offers up a question about the company, this is an opportunity to show your research into their work and how you align with them. Consider how your analysis skills can bring insights specific to this company in particular, with their problems and goals in mind.
What Are The Disadvantages Of R
Just as you should know what R does well, you should understand its failings.
Memory and performance. In comparison to Python, R is often said to be the lesser language in terms of memory and performance. This is disputable, and many think its no longer relevant as 64-bit systems dominate the marketplace.
Open source. Being open source has its disadvantages as well as its advantages. For one, theres no governing body managing R, so theres no single source for support or quality control. This also means that sometimes the packages developed for R are not the highest quality.
Security. R was not built with security in mind, so it must rely on external resources to mind these gaps.
In Microsoft Excel How Can You Treat A Numeric Value As Text
This question tests your understanding of scripting software like Microsoft Excel. It is an important tool for data analysts, as it helps create clear and polished charts that aid in data visualisation.
Example:”For treating a numeric value as text in Excel, precede the numeric value with an apostrophe symbol. For example, ‘2340, Excel would consider 2340 as a text and would skip it during the analysis and calculation.”
Don’t Miss: What To Include In An Interview Thank You Note
During Which Stage Of The Hiring Process Should You Use Data Analyst Interview Questions
You should use these data analyst interview questions only once you have sourced candidates, requested that they complete a skills assessment, and received the results of the tests.
The interview process should always follow skills testing. This approach reduces time-to-hire and ensures that you take the best data analyst candidates forward to the interview rounds.
Python Coding Interview Question #1: Number Of Comments Per User In Past 30 Days
Heres a question by Meta/Facebook:
Return the total number of comments received for each user in the last 30 days. Don’t output users who haven’t received any comment in the defined time period. Assume today is 2020-02-10.
Link to the question:
You can find data in the table fb_comments_count:
Have a look at the solution, and then well explain it below:
import pandas as pdfrom datetime import timedeltaresult = fb_comments_count > = pd.to_datetime - timedelta) & )].groupby.sum.reset_index
To find the comments not older than thirty days from 2020-02-10, you first need to convert this date to datetime using the to_datetime function. To get the latest date of the comments youre interested in, subtract 30 days from today using the timedelta function. All the comments youre interested in have date equal to or greater than this difference. Also, you want to exclude all the comments that are posted after 2020-02-10. Thats why theres a second condition. Finally, group by the user_id and use the sum function to get the comments per user.
If you did everything right, youd get this output:
Write Code To Accomplish A Task
In just about any interview for a position that involves coding, companies will ask you to accomplish a specific task by actually writing code. Facebook and Google both do as much. Because its difficult to predict what task an interviewer will set you to, just be prepared to write whiteboard code on the fly.
What Is The Difference Between Treemaps And Heatmaps In Tableau
Treemaps are used to display data in nested rectangles.
Heat maps can visualize measures against dimensions with the help of colors and size to differentiate one or more dimensions and up to two measures.
You use dimensions to define the structure of the treemap, and measures to define the size or color of the individual rectangles.
The layout is like a text table with variations in values encoded as colors.
Treemaps are a relatively simple data visualization that can provide insight in a visually attractive format.
In the heatmap, you can quickly see a wide array of information.
You May Like: How To Give Introduction In Interview
Top Strategies Companies Use To Recruit Data Analyst
There is no one-size-fits-all answer to this question, as different companies will have different strategies for recruiting data analysts. However, some common strategies that companies use to recruit data analysts include posting job openings on online job boards, attending job fairs, conducting interviews, and using employee referral programs. Additionally, many companies also use social media to reach out to potential candidates and promote open positions.
Before you continue
Data Analyst Interview Questions From Top Companies
Data analysis is a rapidly growing field, and companies are increasingly looking to hire the best talent to handle big data. Here are a few examples of interview questions for Data Analysts from some of the top tech companies :
Here are some common tech interview questions for Data Analysts:
|Please explain the difference between clustered and non-clustered indexes in SQL?|
|What data cleansing techniques do you use?|
|How do you define data normalization and non-normalization?|
|Please explain what cascading referential integrity means.|
|Whats your time management approach for solo projects?|
|Are there specific functions in SQL that you use the most?|
|Please provide an example of a situation in which you automated an otherwise manual process.|
|Do you recall a time when you expected one thing when you began an analysis, but then got results you didnt anticipate?|
Don’t Miss: How To Prepare For Tech Interview
Python Coding Interview Questions You Must Know For Data Science
Solving the Python coding interview questions is the best way to get ready for an interview. Thats why well lead you through 15 examples and five concepts these questions cover.
Knowing Python is one of the crucial skills every data scientist should hone. And its not without reason. Pythons ability, combined with Pandas library, to manipulate and analyze data in a number of different ways makes it an ideal tool for a data science job.
It comes as no surprise that all the companies looking for data scientists will test their Python skills on a job interview.
Well have a look at what technical concepts, along with Python/Pandas functions, you should be familiar with to land a data science job.
These are the five topics well talk about:
- Aggregation, Grouping, and Ordering Data
- Text Manipulation
- Datetime Manipulation
It goes without saying that these concepts are rarely tested separately, so by solving one question youll have to showcase your knowledge of multiple Python topics.
Q10 What Are The Differences Between The Sum Function And Using + Operator
The SUM function returns the sum of non-missing arguments whereas + operator returns a missing value if any of the arguments are missing. Consider the following example.
data exampledata1 input a b c cards 44 4 434 3 434 3 4. 1 224 . 444 4 .25 3 1 run data exampledata2 set exampledata1 x = sum y=a+b+c run
In the output, the value of y is missing for 4th, 5th, and 6th observation as we have used the + operator to calculate the value of y.
x y52 5241 4141 413 .28 .48 .29 29
If you wish to know more questions on SAS, then refer a full-fledged article on SAS Interview Questions.
Now, let us move on to the next set of questions which is the SQL Interview Questions.
Don’t Miss: Google Cloud Platform Interview Questions
What Is The Most Challenging Project You Encountered On Your Learning Journey
Recruiters ask this question to understand your problem-solving approach and ability to take the initiative on projects.
Answer by throwing back to a specific project that you worked on, starting with the goal of the project and its business context. Then talk about what problems emerged that made it challenging. Most importantly, talk about how you solved those problems, including details about both your own contributions as well as how you rallied your team around you.
Q33 What Is The Difference Between Lists And Arrays
An array is a data structure that contains a group of elements where the elements are of the same data type, e.g., integer, string. The array elements share the same variable name, but each element has its own unique index number or key. The purpose is to organize the data so that the related set of values can be easily sorted or searched.
Recommended Reading: What Are Good Questions To Ask Your Interviewer
Explain Cluster Analysis And Its Characteristics
A process in which we define an object without labelling it is known as cluster analysis. It uses data mining to group various similar objects into a single cluster just like in discriminant analysis. Its applications include pattern recognition, information analysis, image analysis, machine learning, computer graphics, and various other fields. Cluster analysis is a task that is conducted using several other algorithms that are different from each other in many ways and thus creating a cluster. The following are some of the characteristics of cluster analysis: Cluster Analysis is highly scalable. It can deal with a different set of attributes. It shows high dimensionality, Interpretability. It is useful in many fields including machine learning and information gathering.
Q35 What Is The Difference Between Merge Join And Concatenate
Merge is used to merge the data frames using the unique column identifier. By default, the merge happens on an inner that is the intersection of all the elements. Syntax: pd.merge
Join is used to join the data frames using the unique index. The left join is the default which means it takes all the exclusive ids of the data frame that exists on the left table. It will return all the indexes on the left side of the table and NaN for the corresponding values that dont exist on the right table. Syntax: df1.join
Concatenate: It joins the data frames basically either by rows or columns. Syntax: pd.concat
Read Also: What Language To Use For Coding Interviews
What Happens When The Split Method Is Used For Splitting Numpy Arrays
1. np.split : Equally splits arrays into multiple sub-arrays. It raises Value Error when the split cannot be equal.
- array – array that needs to be split
- If we give an integer X, X equal sub-arrays are obtained after dividing the array. If the split is not possible, ValueError is raised.
import numpy as npa = np.arangesplit_arr = np.splitsplit_arr
- If we give a 1-D sorted array then the entries would represent where the array would be split along the axis. For instance if we provide and axis as 0, then the result would be, arr, arr]
- If the provided index exceeds the array dimension along the given axis, then an empty subarray will be returned.
), array, array, array, array, array, array]
The output would be:
import numpy as npa = np.arangesplit_arr = np.splitsplit_arr
- axis – Along what axis the array has to be split. By default, the value is 0
Should Data Analyst Interviews Be Technical
While the short answer is yes, Data Analyst interviews should focus on technical and soft skills.
Its essential to shortlist candidates using job-specific skills tests to pinpoint and shortlist candidates who have the technical knowledge and cultural fit for the role. Job-specific assessments, like this Data Scientist template from Toggl Hire, are ready-made and combine the questions to assess skills and personality traits needed for the position.
This screening method not only helps to continue with candidates with the required skill set for the Job via a data-driven approach but also opens the way to asses candidates practical skills in the following steps, such as in-depth Data Analyst interviews.
Most recruiters and hiring managers later assess communication and presentation skills with video interviews.
They then conduct take-home assignments with their finalists for this role to see candidates practical skills in action, both for soft and technical skills required for Data Analysts. Homework assignments take the guesswork from the equation when selecting the right candidate for the role.
Recommended Reading: Questions For Data Engineer Interview