Data Analyst Road Map

 

  Data Analyst Interview Questions and Answers PART-1

1.What Is Data Visualization? How Many Types of Visualization Are There?

Data visualization is the practice of representing data and data-based insights in graphical form. Visualization makes it easy for viewers to quickly glean the trends and outliers in a dataset. 

There are several types of data visualizations, including: 

• Pie charts 
• Column charts
• Bar graphs
• Scatter plots
• Heat maps
• Line graphs
• Bullet graphs
• Waterfall charts

2.What Is a Hashtable?

A hashtable is a data structure that stores data in an array format using associative logic. The use of arrays means that every value is given its own index value. This makes accessing the data easy. 

3.How Would You Define a Good Data Model?

A good data model exhibits the following: 

• Predictability: The data model should work in ways that are predictable so that its performance outcomes are always dependable. 
• Scalability: The data model’s performance shouldn’t become hampered when it is fed increasingly large datasets.
• Adaptability: It should be easy for the data model to respond to changing business scenarios and goals. 
• Results-oriented: The organization that you work for or its clients should be able to derive profitable insights using the model. 

4.What Is Time Series Analysis?

Time Series Analysis is a data analysis approach that analyzes a dataset over certain intervals of time. It can be especially valuable in areas where tracking data over time can unearth valuable insights. For example, a time series analysis of COVID-19 can help us see trends in the way the disease has spread.

5.What Is the Difference Between Time Series Analysis and Time Series Forecasting?

Time series analysis simply studies data points collected over a period of time looking for insights that can be unearthed from it. Time series forecasting, on the other hand, involves making predictions informed by data studied over a period of time. 

6.What Is Clustering? List the Main Properties of Clustering Algorithms.

Clustering is the technique of identifying groups or categories within a dataset and placing data values into those groups, thus creating clusters. 

Clustering algorithms have the following properties: 

• Iterative
• Hard or soft 
• Disjunctive 
• Flat or hierarchical 

7.What Is Linear Regression?

Linear regression is a statistical method used to find out how two variables are related to each other. One of the variables is the dependent variable and the other one is the explanatory variable. The process used to establish this relationship involves fitting a linear equation to the dataset. 

8.Explain Kmeans Clustering.

Analysts use K-means clustering to partition observations into k non-overlapping sub-groups called clusters. It is a popular technique for cluster analysis in data mining.

9.Explain Data Warehousing.

A data warehouse is a data storage system that collects data from various disparate sources and stores them in a way that makes it easy to produce important business insights. Data warehousing is the process of identifying heterogeneous data sources, sourcing data, cleaning it, and transforming it into a manageable form for storage in a data warehouse. 

10.Name the Statistical Methods That Are Highly Beneficial for Data Analysts.

Some of the most widely used statistical methods in data analysis are as follows: 

• Cluster analysis 
• Regression 
• Bayesian approaches 
• Markov chains 
• Imputation