Have you earned any certifications to boost your career opportunities as a Data Analyst?

Hiring managers appreciate a candidate who is serious about advancing their career options through additional qualifications. Certificates prove that you have put in the effort to master new skills and knowledge of the latest analytical tools and subjects. While answering the question, list the certificates you have acquired and briefly explain how they’ve helped you boost your data analyst career. If you haven’t earned any certifications so far, make sure you mention the ones you’d like to work towards and why.

Example
“I’m always looking for ways to upgrade my analytics skillset. This is why I recently earned a certification in Customer Analytics in Python. The training and requirements to finish it really helped me sharpen my skills in analyzing customer data and predicting the purchase behavior of clients.”

What is an N-gram?

An n-gram is a connected sequence of n items in a given text or speech. Precisely, an N-gram is a probabilistic language model used to predict the next item in a particular sequence, as in (n-1).

Do you have any idea about the job profile of a data analyst?

Yes, I have a fair idea of the job responsibilities of a data analyst. Their primary responsibilities are –

To work in collaboration with IT, management and/or data scientist teams to determine organizational goals
To dig data from primary and secondary sources
To clean the data and discard irrelevant information
To perform data analysis and interpret results using standard statistical methodologies
To highlight changing trends, correlations and patterns in complicated data sets
To strategize process improvement
To ensure clear data visualizations for management

What is the difference between univariate, bivariate and multivariate analysis?

The differences between univariate, bivariate and multivariate analysis are as follows:

Univariate: A descriptive statistical technique that can be differentiated based on the count of variables involved at a given instance of time.
Bivariate: This analysis is used to find the difference between two variables at a time.
Multivariate: The study of more than two variables is nothing but multivariate analysis. This analysis is used to understand the effect of variables on the responses.

What does “Data Cleansing” mean? What are the best ways to practice this?

If you are sitting for a data analyst job, this is one of the most frequently asked data analyst interview questions.
Data cleansing primarily refers to the process of detecting and removing errors and inconsistencies from the data to improve data quality.
The best ways to clean data are:

Segregating data, according to their respective attributes.
Breaking large chunks of data into small datasets and then cleaning them.
Analyzing the statistics of each data column.
Creating a set of utility functions or scripts for dealing with common cleaning tasks.
Keeping track of all the data cleansing operations to facilitate easy addition or removal from the datasets, if required.

What are the two main methods two detect outliers?

Box plot method: if the value is higher or lesser than 1.5*IQR (inter quartile range) above the upper quartile (Q3) or below the lower quartile (Q1) respectively, then it is considered an outlier.

Standard deviation method: if value higher or lower than mean ± (3*standard deviation), then it is considered an outlier.

Can you mention a few problems that data analyst usually encounter while performing the analysis?

The following are a few problems that are usually encountered while performing data analysis.

Presence of Duplicate entries and spelling mistakes, reduce data quality.
If you are extracting data from a poor source, then this could be a problem as you would have to spend a lot of time cleaning the data.
When you extract data from sources, the data may vary in representation. Now, when you combine data from these sources, it may happen that the variation in representation could result in a delay.
Lastly, if there is incomplete data, then that could be a problem to perform analysis of data.

What are some of the statistical methods that are useful for data-analyst?

Statistical methods that are useful for data scientist are

Bayesian method
Markov process
Spatial and cluster processes
Rank statistics, percentile, outliers detection
Imputation techniques, etc.
Simplex algorithm
Mathematical optimization

Mention what is the responsibility of a Data analyst?

Responsibility of a Data analyst include,

Provide support to all data analysis and coordinate with customers and staffs
Resolve business associated issues for clients and performing audit on data
Analyze results and interpret data using statistical techniques and provide ongoing reports
Prioritize business needs and work closely with management and information needs

Identify new process or areas for improvement opportunities
Analyze, identify and interpret trends or patterns in complex data sets
Acquire data from primary or secondary data sources and maintain databases/data systems
Filter and “clean” data, and review computer reports
Determine performance indicators to locate and correct code problems
Securing database by developing access system by determining user level of access