Today data is the most important thing for any business and taking data driven decisions makes them stay on the correct path. Businesses are more inclined towards data these days and there is a need for someone who can analyse the data accurately. Data analytics is widely used in the 21st century. There comes the job for data analyst. A data analyst collects and processes data. He or she looks at big sets of data to figure out the meaning. If you want to apply for the data analyst post then you should prepare some important data analyst interview questions. In this blog post we will tell you about **data analyst technical interview questions and answers, data analyst interview questions for freshers and data analyst interview questions.**

## Basic Data Analyst Interview Questions

### What is the difference between data mining and data profiling?

Data Mining | Data profiling |

It means that discovering information that is new | It is done to evaluate the dataset based on logic, uniqueness and consistency |

Raw data is converted into valuable information | It can’t identify incorrect data |

### What are some common problems faced by data analysts during the analysis?

- Handling duplicates
- Facing storage problems
- Creating secure data and dealing with other issues
- Collecting the meaningful data at correct time

### What are some technical tools that are used for data analysis and presentation?

MS SQL Server, MySQL

For working with data stored in relational databases

MS Excel, Tableau

For creating reports and dashboards

Python, R, SPSS

For statistical analysis, data modeling, and exploratory analysis

MS PowerPoint

For presentation, displaying the final results and important conclusions

### What do you mean by Data wrangling in Data analytics?

Data Wrangling is the process of cleaning, organizing, and enhancing raw data so that it can be used in a way that helps people make better decisions. It involves finding, organizing, cleaning, enriching, validating, and analyzing data. This process can take a lot of data from different sources and map it out in a way that is easier to use.

### Best methods for data cleaning?

- Make a plan for data cleaning by figuring out where most mistakes happen and making sure all lines of communication are open.
- Find and get rid of the duplicates before you start working with the data. This will make it easy and useful to look at the data.
- Pay attention to how accurate the data is. Set cross-field validation, keep track of the types of data values, and add required constraints.
- Normalize the data as it comes in to make it less chaotic. You will be able to make sure that all the information is the same, which will make it easier to enter with fewer mistakes.

### Tell me about different types of sampling techniques used by data analysts?

Sampling is a statistical method for choosing a subset of data from a whole set of data (a population) to estimate what the whole population is like.

- Simple random sampling
- Systematic sampling
- Cluster sampling
- Stratified sampling
- Judgmental or purposive sampling

### What do you mean by univariate, bivariate, and multivariate analysis?

- Univariate analysis is the easiest and simplest form of data analysis where only one variable data is analyzed.

For example: studying the heights of cricketers in the world cup.

- Bivariate analysis involves the analysis of two variables to find relations between variables.

For example: Using the weather to figure out how much ice cream is sold.

Correlation coefficients, Linear regression, Logistic regression, Scatter plots, and Box plots can be used to explain the bivariate analysis.

- Multivariate analysis looks at the relationships between three or more variables to figure out how each one affects the others.

Example: Figuring out how much money was made and spent.

Multiple regression, factor analysis, classification and regression trees, cluster analysis, principal component analysis, dual-axis charts, etc. can all be used to do multivariate analysis.

### What are the steps involved in an analytics project?

- Understanding the problem
- Collection of data
- Cleaning of data
- Analyzing the data
- Interpreting the results

### What do you mean by time series analysis?

Time Series analysis is a statistical method that looks at the order of a variable’s values at evenly spaced points in time.

Data for a time series are collected at times close together. So, there is a link between what was seen and what was said. This is what makes cross-sectional data different from time-series data.

### What is the importance of exploratory data analysis (EDA)?

- It helps to understand the data better
- You can find hidden insights and trends from the data
- It gives you enough confidence in your data to be ready to use an algorithm for machine learning.
- It lets you narrow down your choice of feature variables that will later be used to build a model.

Read More: A List of Top Big Data Engineer Skills One Must Possess

## Some Data Analyst Interview Questions for Fresher

- What are the most commonly seen patterns that are missing?
- What should be done with data that might be wrong or is missing?
- How do you deal with problems that have more than one cause?
- What kinds of tools are used in Big Data?
- What do you mean by Map Reduce?

## Data Analyst Interview Questions and Answers For Experienced

### What do you mean by KNN imputation method?

In KNN imputation, the missing attribute values are filled in by using the values of the attributes that are most similar to the attribute whose values are missing. Using a distance function, the similarity of two attributes can be measured.

### What do you understand about the K-mean algorithm?

K-mean is a famous partitioning method.

In the K-mean algorithm,

- The clusters are round, and each data point in a cluster is in the middle of that cluster.
- The clusters have the same variance or spread: Each point of data belongs to the group that is closest to it.

### Explain the term correlogram analysis?

In geography, a correlogram analysis is the most common type of spatial analysis. It is made up of a list of estimated autocorrelation coefficients that were calculated for different spatial relationships. It can be used to make a correlogram for data that is based on distance, when the raw data is given in terms of distance instead of values at each point.

### Explain the term n-gram?

An n-gram is a set of n items that come together in a row from a set of text or speech. It is a type of probabilistic language model for figuring out what will come next in a sequence (n-

### What do you mean by LOD in tableau?

LOD stands for “Level of Detail” in Tableau. It is an expression that is used at the data sourcing level to run complex queries with many dimensions. Using the LOD expression, you can find duplicate values, align the axes of a chart, and make bins from the data that has been gathered.

## Conclusion

To do your best in the **data analyst interviews** you need to prepare well with these questions. It will increase your chances of getting hired. As there are a lot of job openings for data analysts today with an increase in dependence on data analytics and data driven decisions. We hope this post was helpful to you.