Updated April 29, 2023
Introduction to Data Analytics Interview Questions
So you have finally found your dream job in Data Analytics but are wondering how to crack the 2023 Data Analytics interview and what the probable Data Analytics Interview Questions could be. Every Data Analytics interview and the job scope are different too. Keeping this in mind, we have designed the most common Data Analytics Interview Questions and answers to help you get success in your Data Analytics interview.
Below are the Top 2023 Data Analytics Interview Questions primarily asked in an interview. These are divided into two parts.
Part 1 – Data Analytics Interview Questions and Answers (Basic)
Below are the basic interview questions and answers:
Q1. What is the difference between Data Mining and Data Analysis?
Answer:
| Data Mining | Data Analysis |
| A hypothesis is not required for Data Mining. | Data analysis begins with a hypothesis. |
| Data Mining demands clean and well-documented data. | Data analysis involves data cleaning. |
| The results of data mining are not always easy to interpret. | Data analysts interpret the results and present them to the stakeholders. |
| Data mining algorithms automatically develop equations. | Data analysts have to develop their equations. |
Q2. Mention what are the various steps in an analytics project.
Answer:
Data analytics involves collecting, cleansing, transforming, and modeling data to gain valuable insights and support better organizational decision-making.
The steps involved in the data analysis process are as follows:
- Data Exploration: Having explored the business problem, a data analyst has to analyze the root cause of the problem.
- Data Preparation: In this step of the data analysis process, we find data anomalies like missing values within the data.
- Data Modelling: The modeling step begins after the data has been prepared. Modeling is an iterative process wherein the model runs repeatedly for improvements. Data modeling ensures the best possible result for a business problem.
- Validation: In this step, the model is provided by the client, and the model developed by the data analyst are validated against each other to find out if the developed model will meet the business requirements.
- Implementation of the Model and Tracking: In this final step of the data analysis, model implementation is done, and after that, tracking is done to ensure that the model is implemented correctly or not.
Q3. What is the responsibility of a Data Analyst?
Answer:
- Resolve business-associated issues for clients and perform data audit operations.
- Interpret data using statistical techniques.
- Identify areas for improvement opportunities.
- Analyze, identify, and interpret trends or patterns in complex data sets.
- Acquire data from primary or secondary data sources.
- Maintain databases/data systems.
- Locate and correct code problems using performance indicators.
- Securing database by developing access system.
Q4. What is Hash Table Collisions? How is it Avoided?
Answer:
A hash table collision happens when two different keys hash to the same value. There are many techniques to avoid hash table collision; here, we list two.
- Separate Chaining: It uses the data structure that hashes to the same slot to store multiple items.
- Open Addressing: It searches for other slots using a second function and store item in the first empty slot.
Q5. List some best tools that can be useful for data analysis.
Answer:
- Tableau
- RapidMiner
- OpenRefine
- KNIME
- Google Search Operators
- Solver
- NodeXL
- io
- Wolfram Alpha’s
- Google Fusion Tables
Q6. What is the difference between data mining and data profiling?
Answer:
The difference between data mining and data profiling is as follows:
- Data profiling: It targets the instant analysis of individual attributes like price vary, special price and frequency, the incidence of null values, data type, length, etc.
- Data mining: It focuses on dependencies, sequence discovery, relation holding between several attributes, cluster analysis, detection of unusual records, etc.
Part 2 – Data Analytics Interview Questions and Answers (Advanced)
Below are the advanced interview questions and answers:
Q7. Explain K-mean Algorithm and Hierarchical Clustering Algorithm.
Answer:
- K-Mean Algorithm: K mean is a famous partitioning method. In the K-mean algorithm, the clusters are spherical, i.e. the data points in a cluster are centered on that cluster. Also, the variance of the clusters is similar, i.e., each data point belongs to the closest cluster.
- Hierarchical Clustering Algorithm: Hierarchical clustering algorithm combines and divides existing groups and creates a hierarchical structure to show the order in which groups are divided.
Q8. What is data cleansing? Mention a few best practices you must follow while doing data cleansing.
Answer:
Sorting the information required for data analysis from a given dataset is essential. Data cleaning is a crucial step wherein data is inspected to find anomalies, remove repetitive and incorrect information, etc. Data cleansing does not involve removing any existing information from the database; it just enhances the data quality for analysis.
Some of the best practices for data cleansing include:
- Developing a data quality plan to identify where maximum data quality errors occur so that you can assess the root cause and plan according to that.
- Follow a customary method of substantiating the necessary information before it’s entered into the information.
- Identify any duplicate data and validate the accuracy of the data, as this will save a lot of time during analysis.
- Tracking all the improvement operations performed on the information is incredibly necessary so that you repeat or take away any operations as required.
Q9. What are some of the statistical methods that are useful for data-analyst?
Answer:
Statistical methods that are useful for a data scientist are:
- Bayesian method
- Markov process
- Spatial and cluster processes
- Rank statistics, percentile, outlier’s detection
- Imputation techniques, etc
- Simplex algorithm
- Mathematical optimization
Q10. Explain what imputation is. List out different types of imputation techniques. Which imputation method is more favorable?
Answer:
During imputation, we tend to replace missing information with substituted values.
The kinds of imputation techniques involve are:
- Single Imputation: Single imputation denotes that a value replaces the missing value. In this method, the sample size is retrieved.
- Hot-deck imputation: A missing value is imputed from a randomly selected similar record by using a punch card
- Cold deck imputation: It works like hot-deck imputation but is a little more advanced and chooses donors from other datasets.
- Mean imputation: It involves replacing the missing value with the predicted values of other variables.
- Regression imputation: It involves replacing the missing value with the predicted values of a particular value depending on other variables.
- Stochastic regression: It is the same as regression imputation but adds the common regression variance to the imputation.
- Multiple imputation: Unlike single imputation, multiple imputations estimate the values multiple times.
Although single imputation is widely used, it does not reflect the uncertainty created by missing data at random. So, multiple imputations are more favorable than single imputations in case of data missing at random.
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