In an interview, these questions are more likely to appear early in the process and cover data analysis at a high level.
1. Mention the differences between Data Mining and Data Profiling?
| Data Mining | Data Profiting |
| Data mining is the process of discovering relevant information that has not yet been identified before. | Data profiling is done to evaluate a dataset for its uniqueness, logic, and consistency. |
| In data mining, raw data is converted into valuable information. | It cannot identify inaccurate or incorrect data values. |
2. Define the term ‘Data Wrangling in Data Analytics.
Data Wrangling is the process wherein raw data is cleaned, structured, and enriched into a desired usable format for better decision making. It involves discovering, structuring, cleaning, enriching, validating, and analyzing data. This process can turn and map out large amounts of data extracted from various sources into a more useful format. Techniques such as merging, grouping, concatenating, joining, and sorting are used to analyze the data. Thereafter it gets ready to be used with another dataset.
3. What are the various steps involved in any analytics project?
This is one of the most basic data analyst interview questions. The various steps involved in any common analytics projects are as follows:
Understanding the Problem
Understand the business problem, define the organizational goals, and plan for a lucrative solution.
Collecting Data
Gather the right data from various sources and other information based on your priorities.
Cleaning Data
Clean the data to remove unwanted, redundant, and missing values, and make it ready for analysis.
Exploring and Analyzing Data
Use data visualization and business intelligence tools, data mining techniques, and predictive modeling to analyze data.
Interpreting the Results
Interpret the results to find out hidden patterns, future trends, and gain insights.
. What are the common problems that data analysts encounter during analysis?
The common problems steps involved in any analytics project are:
- Handling duplicate
- Collecting the meaningful right data and the right time
- Handling data purging and storage problems
- Making data secure and dealing with compliance issues
5. Which are the technical tools that you have used for analysis and presentation purposes?
As a data analyst, you are expected to know the tools mentioned below for analysis and presentation purposes. Some of the popular tools you should know are:
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.
6. What are the best methods for data cleaning?
- Create a data cleaning plan by understanding where the common errors take place and keep all the communications open.
- Before working with the data, identify and remove the duplicates. This will lead to an easy and effective data analysis process.
- Focus on the accuracy of the data. Set cross-field validation, maintain the value types of data, and provide mandatory constraints.
- Normalize the data at the entry point so that it is less chaotic. You will be able to ensure that all information is standardized, leading to fewer errors on entry.
7. What is the significance of Exploratory Data Analysis (EDA)?
- Exploratory data analysis (EDA) helps to understand the data better.
- It helps you obtain confidence in your data to a point where you’re ready to engage a machine learning algorithm.
- It allows you to refine your selection of feature variables that will be used later for model building.
- You can discover hidden trends and insights from the data.