What is the Metadata?
Metadata refers to the detailed information about the data system and its contents. It helps to define the type of data or information that will be sorted.
Do you have any questions?
At the close of the interview, most interviewers ask whether you have any questions about the job or company. It’s always a good idea to have a few ready so that you show you’ve prepared for the interview and have thought about some things relative to the company or to the role that you would like to explore further.
Questions about the role: This is a unique opportunity to learn more about what you’ll do, if it hasn’t already been thoroughly covered in the earlier part of the interview. For example:
• Can you share more about the day-to-day responsibilities of this position? What’s a typical day like?
Questions about the company or the interviewer: This is also a good opportunity to get a sense of company culture and how the company is doing.
• What’s the company organization and culture like?
It’s important to be prepared to respond effectively to the interview questions that employers typically ask at job interviews. Since these questions are so common, hiring managers and interviewers will expect you to be able to answer them smoothly and without hesitation.
You don’t need to memorize your answers to the point you sound like a robot, but do think about what you’re going to say so you’re not put on the spot during the job interview. Practice with a friend so you’re familiar and comfortable with the questions. Good luck!
Explain what is collaborative filtering?
Collaborative filtering is a simple algorithm to create a recommendation system based on user behavioral data. The most important components of collaborative filtering are users- items- interest.
A good example of collaborative filtering is when you see a statement like “recommended for you” on online shopping sites that’s pops out based on your browsing history.
Explain “Normal Distribution.”
One of the popular data analyst interview questions. Normal distribution, better known as the Bell Curve or Gaussian curve, refers to a probability function that describes and measures how the values of a variable are distributed, that is, how they differ in their means and their standard deviations. In the curve, the distribution is symmetric. While most of the observations cluster around the central peak, probabilities for the values steer further away from the mean, tapering off equally in both directions.
What is the difference between data mining and data profiling? (Maestro Technologies)
Data mining is a process in which you identify patterns, anomalies, and correlations in large data sets to predict outcomes. On the other hand, data profiling lets analysts monitor and cleanse data.
Whereas data mining is concerned with collecting knowledge from data, data profiling is concerned primarily with evaluating the quality of data.
Have you ever used both quantitative and qualitative data within the same project?
To conduct a meaningful analysis, data analysts must use both the quantitative and qualitative data available to them. In surveys, there are both quantitative and qualitative questions, so merging those 2 types of data presents no challenge whatsoever. In other cases, though, a data analyst must use creativity to find matching qualitative data. That said, when answering this question, talk about the project where the most creative thinking was required.
Example
“In my experience, I’ve performed a few analyses where I had qualitative survey data at my disposal. However, I realized I can actually enhance the validity of my recommendations by also implementing valuable data from external survey sources. So, for a product development project, I used qualitative data provided by our distributors, and it yielded great results.”
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.
Example:
- data exampledata1;
- input a b c;
- cards;
- 44 4 4
- 34 3 4
- 34 3 4
- . 1 2
- 24 . 4
- 44 4 .
- 25 3 1
- ;
- run;
- data exampledata2;
- set exampledata1;
- x = sum(a,b,c);
- 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 y
52 52
41 41
41 41
3 .
28 .
48 .
29 29
List out some common problems faced by data analyst?
Some of the common problems faced by data analyst are
- Common misspelling
- Duplicate entries
- Missing values
- Illegal values
- Varying value representations
- Identifying overlapping data
What is kmeans algorithm?
Kmeans algorithm partitions a data set into clusters such that a cluster formed is homogeneous and the points in each cluster are close to each other. The algorithm tries to maintain enough separation between these clusters. Due to the unsupervised nature, the clusters have no labels.
What Are Some Issues That Data Analysts Typically Come Across?
All jobs have their challenges, and your interviewer not only wants to test your knowledge on these common issues but also know that you can easily find the right solutions when available. In your answer, you can address some common issues, such as having a data file that’s poorly formatted or having incomplete data.