In the domain of data science, you’ll get a wide range of different career options to choose from. If you take an interest in data cleaning and data exploration and want to work as a data analyst, here are some interview questions that are frequently asked with their answers to get you job-ready.
Key job responsibilities
As a data analyst, what will be your key job responsibilities?
As a data analyst, some of my key job responsibilities will be
- Data cleaning where I’ll remove or fix incomplete, duplicate, corrupted, or erroneous data within a dataset.
- Data exploration and interpretation where I’ll explore massive data sets to find out initial attributes, patterns, and points of interest and analyze these results.
- To provide support for each phase of data analysis, and analyze complex datasets to identify the hidden patterns in them and extract insights for decision-making.
- To keep the databases secured.
Statistical methodologies used by data analysts
Which statistical methodologies are used by data analysts?
To perform data analysis, several statistical techniques can be used. However, some of the significant ones are:
- Cluster analysis
- Markov process
- Rank statistics
- Bayesian methodologies
- Imputation techniques
The best tools for data analysis
What are the best tools for data analysis?
For data analysis, some of the most useful tools are:
- Tableau
- Google Search Operators
- RapidMiner
- Google Fusion Tables
- NodeXL
- KNIME
- OpenRefine
- Solver
The criteria that define a good data model
What are the criteria that define a good data model?
A data model is good if
- it’s intuitive.
- data in it can be easily consumed.
- data changes in it are scalable.
- it’s responsive and adaptive to changes, which would make it capable of supporting new or growing business needs.
Feature engineering for data analytics
How can feature engineering make data analytics more powerful?
Data that I’m given or which I gather may not always be adequate for designing a good machine learning model. This is where feature engineering can help. It can prepare suitable input datasets that are compatible with the requirements of the machine learning algorithm, and help in boosting the machine learning models’ performance. The domain of feature engineering involves different tasks, such as:
- Feature transformation where new features are built from existing features.
- Feature generation (or feature extraction) that involves creating new features via domain-specific or generic automatic feature generation methods; these new features aren’t usually the result of feature transformation.
- Feature selection, where a small set of features are chosen from an extremely big pool of features; with the decreased feature set size, it becomes computationally viable to use specific data analytic and machine learning algorithms.
- Automatic feature engineering, which is a generic method for automatically producing a huge number of features and choosing an effective subset of the produced features.
- Feature analysis and evaluation where the efficacy of features and feature sets is assessed.
Conclusion
Conclusion
These interview questions for data analysts are selected from a vast pool of probable and frequently asked questions. Thus, knowing their answers would surely help you a lot in landing your dream job where you can have fun with data cleaning, data exploration, and much more.
