How Have You Handled Data Inconsistencies In The Past
Identifying and handling inconsistencies is an essential concept for an analyst to understand because they spend much of their time cleaning and processing data which may contain inconsistencies. This question highlights a candidate’s understanding and experience. What to look for in an answer:
- Logical thought process
- Attention to detail
“At my last position, I used a central semantic storage approach to prevent data inconsistencies. This approach helps create a central area for the data that I used as a reference when processing data. I think preventive measures like this are best for handling inconsistencies, but if an inconsistency still occurs I use a primary key to link to a table where I can re-enter the correct data.”
Whats Your Knowledge Of Statistics And How Have You Used It In Your Work As A Data Analyst
How to Answer
Data analysts should have basic statistics knowledge and experience. That means you should be comfortable with calculating mean, median and mode, as well as conducting significance testing. In addition, as a data analyst, you must be able to interpret the above in connection to the business. If a higher level of statistics is required, it will be listed in the job description.
In my line of work, Ive used basic statistics mostly calculated the mean and standard variances, as well as significance testing. The latter helped me determine the statistical significance of measurement differences between two populations for a project. Ive also determined the relationship between 2 variables in a data set, working with correlation coefficients.
Q10 Can You Tell How To Embed Views Onto Web Pages
You can embed interactive Tableau views and dashboards into web pages, blogs, wiki pages, web applications, and intranet portals. Embedded views update as the underlying data changes, or as their workbooks are updated on Tableau Server. Embedded views follow the same licensing and permission restrictions used on Tableau Server. That is, to see a Tableau view thats embedded in a web page, the person accessing the view must also have an account on Tableau Server.
Alternatively, if your organization uses a core-based license on Tableau Server, a Guest account is available. This allows people in your organization to view and interact with Tableau views embedded in web pages without having to sign in to the server. Contact your server or site administrator to find out if the Guest user is enabled for the site you publish to.
You can do the following to embed views and adjust their default appearance:
- Get the embed code provided with a view: The Share button at the top of each view includes embedded code that you can copy and paste into your webpage.
- Customize the embed code: You can customize the embed code using parameters that control the toolbar, tabs, and more. For more information, see Parameters for Embed Code.
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Technical Data Analyst Interview Questions
Technical data analyst interview questions are focused on assessing your proficiency in analytical software, visualization tools, and scripting languages, such as SQL and Python. Depending on the specifics of the job, you might be requested to answer some more advanced statistical questions, too. Here are some real-world examples:
What Skills And Education Do You Need To Become A Data Analyst
When it comes to entering the field as an entry-level data analyst, youll find that technical skills are what are most important to an employer. Most data analysts will also have bachelors degrees in fields likemathematics,statistics, orcomputer science, and the best-paid analysts will have masters and doctorate degrees. However, entry-level data analysts that have a strong technical background will generally stand a better chance of being hired than those who dont.
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Q8 What Are Different Types Of Hypothesis Testing
The different types of hypothesis testing are as follows:
- T-test: T-test is used when the standard deviation is unknown and the sample size is comparatively small.
- Chi-Square Test for Independence: These tests are used to find out the significance of the association between categorical variables in the population sample.
- Analysis of Variance : This kind of hypothesis testing is used to analyze differences between the means in various groups. This test is often used similarly to a T-test but, is used for more than two groups.
- Welchs T-test: This test is used to find out the test for equality of means between two population samples.
Q1 What Is Interleaving In Sas
Interleaving in SAS means combining individual sorted SAS data sets into one sorted data set. You can interleave data sets using a SET statement along with a BY statement.
In the example that you can see below, the data sets are sorted by the variable Age.
Fig 9: Example for Interleaving in SAS Data Analyst Interview Questions
We can sort and then join the data sets on Age by writing the following query:
data combined set Data1, Data2 by Age run
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Advanced Data Analyst Interview Questions
The more you advance in your career, the more recruiters expect you to know about the field of data analysis. That includes not just technical know-how, but an understanding of where data fits into organizational goals and managing teams. Here are some of the interview questions you can expect as a senior data analyst.
Q8 How Does Proc Sql Work
PROC SQL is nothing but a simultaneous process for all the observations. The following steps occur when a PROC SQL gets executed:
- SAS scans each and every statement in the SQL procedure and checks the syntax errors.
- The SQL optimizer scans the query inside the statement. So, the SQL optimizer basically decides how the SQL query should be executed in order to minimize the runtime.
- If there are any tables in the FROM statement, then they are loaded into the data engine where they can then be accessed in the memory.
- Codes and Calculations are executed.
- The Final Table is created in the memory.
- The Final Table is sent to the output table described in the SQL statement.
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Why Did You Opt For A Data Analytics Career
This is your chance to slip into storytelling mode a little bit. Recruiters like when you can talk passionately about the field youre working in and have personal reasons for why you want to work in it. Describe how you got interested in data analytics and the reasons for wanting to work in the field.
As much as possible, stay away from generic reasons for being interested in data science. Go into your own journey: how you heard about it, the resources you used to study different aspects of the field, and the work that you have done.
Data Analyst Interview Questions And Answers On Technical Skills
Q1. What are the various methods to remove outliers in a dataset?
Outliers can be removed in the following ways:-
- Giving the option to filter out the data
- Based on the distribution of data, replacing the outliers with mean, median, and mode
- Treating them based on post-test analysis
Q2. What are foreign key constraints in a data warehouse?
This is a part of basic SQL data analyst interview questions and answers asked in an interview. Using foreign key constraints is very important and a good practice while working on databases and using data warehouse. A foreign key constraint helps in understanding that the key only contains values that are in the primary key. This helps in data security and as well as joining of proper data.
Q3. How can you create a heat map in excel?
It is a one of the common excel interview questions for data analyst. The same answer applies to a google spreadsheet as well. Heat maps in excel can be created using conditional formatting option. One can customize the heat map based on basic statistics as well.
Q4. Explain how a map function works while using python for data analysis
It is a one of the common python interview questions for data analyst. Map Function is a very important function and is widely used by developers. This function returns an object which is an iterator after using it on items like list and tuples.
Q5. While using Python, what Object Relational Mapping have you used?
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Tell The Minimum Number Of Balls To Be Picked Up In This Process Of Labeling The Jars
If you notice the condition in the question, you will observe that there is a circular misplacement. By which I mean that, if Black is wrongly labeled as Black, Black cannot be labeled as White. So, it must be named as Back + White. If you consider that all the 3 jars are wrongly placed, that is, Black + White jar contains either the Black balls or the White balls, but not the both. Now, just assume you pick one ball from the Black + White jar and let us assume it to be a Black ball. So, obviously, you will name the jar as Black. However, the jar labeled Black cannot have Black + White. Thus, the third jar left in the process should be labeled Black + White. So, if you just pick up one ball, you can correctly label the jars.
Learn About Google’s Culture
Most candidates fail to do this. But before investing tens of hours preparing for an interview at Google, you should take some time to make sure it’s actually the right company for you.
Google is prestigious and it’s therefore tempting to assume that you should apply, without considering things more carefully. However, it’s important to remember that prestige alone won’t make you happy in your day-to-day work. What will make you happy is what youll actually be doing as well as the people you’ll be working with.
If you know data scientists who work at Google or used to work there, talk to them to understand what the culture is like. In addition, we would recommend reading the following resources:
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Explain Data Cleaning In Brief
Data Cleaning is also called Data Wrangling. As the name suggests, it is a structured way of finding erroneous content in data and safely removing them to ensure that the data is of the utmost quality. Here are some of the ways in data cleaning:
- Removing a data block entirely
- Finding ways to fill black data in, without causing redundancies
- Replacing data with its mean or median values
- Making use of placeholders for empty spaces
Job Description Of Data Scientist
Before applying though, you should definitely have a look at the job description and what kind of work is expected, this is mainly for you to understand if you will enjoy working there. The Google data science role isprimarily an analytics role that is focused on metrics and experimentation. This is distinctly different from the machine learning and product analyst roles that also exist at Google that focus more on the engineering and product side respectively. With the team, the role changes a bit, however, its more on the analytical side in general.
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Have You Earned Any Sort Of Certifications To Boost Your Opportunities As A Data Analyst Aspirant
As always, interviewers look for candidates who are serious about advancing their career options by making use of additional tools like certifications. Certificates are strong proof that you have put in all the efforts to learn new skills, master them, and put them to use to the best of your capacity. List the certifications, if you have any, and do talk about them in brief, explaining what all you learned from the program and how its been helpful to you so far.
Can You Break Down The Process You Use When Starting A New Project
This question can help you assess a candidate’s organisational skills and ability to anticipate challenges in a new project. You can also see their leadership or work styles and ensure they are compatible with your company. What to look for an answer:
- Logical thought process
- Timely and efficient
“My first step is always taking some time to review the project, so I’m able to define the objectives or problem. If I have any issues figuring that part out, I will contact the client early on. Next, I find out how reliable the data is and where it originates from. I think about the best methods for modeling it and whether the deadline is realistic for the task at hand. After that, I carefully process the data and cross-reference it to a database to ensure accuracy.”
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What Is The Interview Process For A Data Scientist At Google Like
Googles data scientists report filling out an online application before going through a phone screening, a technical video conference screening, and then being invited onsite for an interview loop.
- The phone screener. Similar to technology companies like Facebook, Amazon, and Apple, the Google data science interview begins with a phone screening in which a recruiter asks basic get-to-know-you questions about former experience and why you want to work at Google.
- The technical screener. The technical screen usually takes place over video conference with a data scientist and is an opportunity for Google to assess your technical capabilities. During this stage, you might be asked questions about A/B testing, statistics, experimental design, and coding questions where you can show that you know how to code in Python and SQL. Some technical questions that have been asked in the past include: For a sample size of N, the margin of error is 3. How many more samples do we need for the margin to hit 0.3? and Write a function to reverse a string.
- The Googleyness screener. Googles onsite interview typically consists of a loop of five interviews with people ranging from product managers to data scientists and business executives. In addition to being asked both technical and situational questions, this final round will assess your leadership qualities, how you navigate workplace ambiguity, and whether youll be a culture fit.
What Does An Entry
As an entry-level data analyst, you will collect, clean, and interpret datasets to answer a question or solve a problem. Data analysts work in all sectors and industriesa data analyst who works in new product development for a large corporation might parse big data on customer preferences to determine what features of a certain product consumers find most valuable. A data analyst working for a health research organization might analyze data on a particular disease to see how it affects a certain population segment.
There are different types of data analysis. Descriptive analysis finds out what happened, diagnostic analysis looks at why it happened,predictive analytics forms projections for the future, and prescriptive analysis focuses on what actions to take.
The duties of the entry-level data analyst are, broadly, the following:
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What Tools Or Software Do You Prefer Using In The Various Phases Of Data Analysis And Why
How to Answer
Although you might think you should have experience with as many tools as possible to ace this question, this is not the case. Each company uses specific data analysis tools, so its normal that your expertise is limited to those. Of course, if you have worked for a large number of companies, youre bound to have exposure to a wider variety of analytical software. That said, the interviewer would like to know which tools you feel comfortable with, rather than the number of tools youve utilized.
When it comes to data analysis tools, I can say Im a traditionalist. Thats why, I find Microsoft Excel and Microsoft Access most useful. I feel truly comfortable working with those, and theyre available in almost every company out there. Moreover, you can achieve great results with them with the right training.
Google Analytics Interview Questions
This first part covers basic Google analytics Interview Questions and Answers.
Q1. What are Google Analytics and its key benefits?
Answer:Google Analytics is a web analytics tool by Google. This helps understand the data traffic on a website so that the website could be optimized and visitors experience could be enhanced. Statistics of website traffic will be presented in the form of a scorecard, tables and other graphs, which will give you actionable insights.
Key benefits are:
Q2. Role of KPI in Google Analytics?
Answer:This is the common question asked in an interview. KPI stands for Key Performance Indicator. It helps in tracking the important things to a business. E.g., No. of visits, no. of clicks, sessions, returning users, first-time visitors, bounce rate, etc. KPI helps you track your business SLAs as well.
Q3. What is a Cohort in Google Analytics?
Answer:Cohort refers to customers who share the same characteristics as they have the same bought the same kind of products they have the same purchase date, etc. It helps an organization to analyze group-wise behaviour by their metrics and revenue.
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The Rise In Popularity And Demand For Data Science Courses
analysis has predicted that the market size of the data science platform will reach USD 140.9 billion by 2024, at a Compound Annual Growth Rate of 30% during the forecast period.
The demand for Data Science is primarily a result of businesses worldwide trying to remain competitive by increasingly using digital technologies. On the other hand, there is a huge demand for Data Scientists because there are not enough skilled professionals to fill the vacancies created by these businesses. This short supply, in turn, has made Data Science one of the highest-paying jobs in the world. Hence, Data Science aspirants are looking for the best Data Scientist courses.
The role of a Data Science professional is to essentially help businesses make informed decisions and solve critical problems through insights generated by interpreting and managing large, complex sets of data.
Intellipaat has collaborated with several top-rated institutions to bring you Data Science courses tailored for anyone who wishes to pursue this career. We have certification programs like Advanced Certification in Data Science and AI by IIT Madras, PG certification in Data Science and Machine Learning by MNIT, Jaipur, Masters in Data Science online program, Data Science online course, Big Data and Data Science Masters course, and the MCA degree program with a specialization in Data Science by Jain University.