2. Define the term ‘Data Wrangling in Data Analytics.

3. What are the various steps involved in any analytics project?

4. What are the common problems that data analysts encounter during analysis?

5. Which are the technical tools that you have used for analysis and presentation purposes?

6. What are the best methods for data cleaning?

7.What is the significance of Exploratory Data Analysis (EDA)?

8. Explain descriptive, predictive, and prescriptive analytics.

9. What are the different types of sampling techniques used by data analysts?

10. Describe univariate, bivariate, and multivariate analysis.

11. What are your strengths and weaknesses as a data analyst?

12. What are the ethical considerations of data analysis?

13. What are some common data visualization tools you have used?

14. How can you handle missing values in a dataset?

15. Explain the term Normal Distribution.

16. What is Time Series analysis?

17. How is Overfitting different from Underfitting?

18. How do you treat outliers in a dataset? 

19. What are the different types of Hypothesis testing?

20. Explain the Type I and Type II errors in Statistics?

21. How would you handle missing data in a dataset?

22. Explain the concept of outlier detection and how you would identify outliers in a dataset.

23. In Microsoft Excel, a numeric value can be treated as a text value if it precedes with what?

24.What is the difference between COUNT,COUNTA,COUNTBLANK And COUNTIF in Excel?

25.How do you make a dropdown list in MS Excel?

26. Can you provide a dynamic range in “Data Source” for a Pivot table?

27. What is the function to find the day of the week for a particular date value?

28. How does the AND() function work in Excel?

29. Explain how VLOOKUP works in Excel?

30. What function would you use to get the current date and time in Excel?

31. Using the below sales table, calculate the total quantity sold by sales representatives whose name starts with A, and the cost of each item they have sold is greater than 10.

32.Using the data given below, create a pivot table to find the total sales made by each sales representative for each item. Display the sales as % of the grand total.

33. How do you subset or filter data in SQL?

34. What is the difference between a WHERE clause and a HAVING clause in SQL?

35.Is the below SQL query correct? If not, how will you rectify it?

36. How are Union, Intersect, and Except used in SQL?

37.What is a Subquery in SQL?

38. From the product_price table, write an SQL query to find the total and average market price for each currency where the average market price is greater than 100, and the currency is in INR or AUD

39.From the product_price table, write an SQL query to find the total and average market price for each currency where the average market price is greater than 100, and the currency is in INR or AUD.

40.Using the product and sales order detail table, find the products with total units sold greater than 1.5 million.

41.  How do you write a stored procedure in SQL?

42.Write an SQL stored procedure to find the total even number between two users given numbers.

43.How is joining different from blending in Tableau?

44.What do you understand by LOD in Tableau?

45.Can you discuss the process of feature selection and its importance in data analysis?

46. What are the different connection types in Tableau Software?

47. What are the different joins that Tableau provides?

48.What is a Gantt Chart in Tableau?

49.Using the Sample Superstore dataset, create a view in Tableau to analyze the sales, profit, and quantity sold across different subcategories of items present under each category.

50.Create a dual-axis chart in Tableau to present Sales and Profit across different years using the Sample Superstore dataset.

51.Design a view in Tableau to show State-wise Sales and Profit using the Sample Superstore dataset.

Data Analyst Interview Question And Answer

1. Data Mining vs. Data Profiling:

Ans.

• Data Mining: It is the process of discovering patterns, correlations, and trends within large datasets. The goal is to extract useful information and knowledge from the data. Data mining involves various techniques such as clustering, classification, regression, and association rule mining.
• Data Profiling: It is the process of examining and summarizing the content, structure, and quality of available data. The main objective is to understand the characteristics of the data, identify anomalies, and assess its fitness for a specific purpose. Data profiling helps in preparing data for further analysis.

2.Data Wrangling in Data Analytics:

Ans,

• Data wrangling (or data munging): It refers to the process of cleaning, structuring, and enriching raw data into a desired format for better decision making in less time. This involves cleaning and handling missing data, transforming variables, and combining datasets. It is a crucial step in the data preparation phase of analytics.

3.Steps in an Analytics Project:

Ans.

• Define Objectives: Clearly state the goals of the analytics project.
• Data Collection: Gather relevant data from various sources.
• Data Cleaning and Wrangling: Preprocess the data to make it suitable for analysis.
• Exploratory Data Analysis (EDA): Understand the patterns and relationships in the data.
• Modeling: Apply statistical or machine learning models for analysis.
• Evaluation: Assess the performance of the models.
• Deployment: Implement the findings or models in a real-world setting.
• Communication: Present results to stakeholders.

4.Common Problems in Data Analysis:

Ans.

• Data Quality Issues: Incomplete, inaccurate, or inconsistent data.
• Lack of Domain Knowledge: Difficulty in interpreting results without understanding the context.
• Overfitting: Models too closely fit the training data and perform poorly on new data.
• Bias: Unintentional skewing of results due to biased data or models.
• Data Security and Privacy Concerns: Handling sensitive information responsibly.

5.Technical Tools for Analysis and Presentation:

Ans.

• Analysis: Python (pandas, NumPy, scikit-learn), R, SQL, Jupyter Notebooks.
• Visualization: Tableau, Power BI, Matplotlib, Seaborn.

6.Methods for Data Cleaning:

Ans.

• Handling Missing Data: Imputation or removal of missing values.
• Handling Outliers: Identifying and addressing data points significantly different from the norm.
• Standardization and Normalization: Ensuring consistent units and scales.

7.Significance of Exploratory Data Analysis (EDA):

Ans.

• Identifying Patterns: EDA helps in discovering patterns, relationships, and trends in the data.
• Assessing Assumptions: It allows for the validation of assumptions made during the initial stages of analysis.
• Outlier Detection: EDA helps in identifying and handling outliers.
• Data Quality Check: EDA is crucial for understanding the quality and integrity of the data.

8.Descriptive, Predictive, and Prescriptive Analytics:

Ans.

• Descriptive Analytics: Involves summarizing and interpreting historical data to describe what has happened.
• Predictive Analytics: Involves forecasting future trends based on historical data and modeling.
• Prescriptive Analytics: Recommends actions to optimize outcomes based on predictions.

9.Sampling Techniques Used by Data Analysts:

Ans.

• Simple Random Sampling: Each item in the population has an equal chance of being included.
• Stratified Sampling: Dividing the population into subgroups and then sampling from each subgroup.
• Cluster Sampling: Dividing the population into clusters and randomly selecting entire clusters.

10.Univariate, Bivariate, and Multivariate Analysis:

Ans.

• Univariate Analysis: Involves the analysis of a single variable, describing its distribution and characteristics.
• Bivariate Analysis: Examines the relationship between two variables.
• Multivariate Analysis: Analyzes the relationship between three or more variables simultaneously.

11.Strengths and Weaknesses as a Data Analyst:

Ans.

• Strengths: Strong analytical skills, attention to detail, proficiency in relevant tools, effective communication.
• Weaknesses: Potential areas for improvement, such as learning advanced statistical methods or gaining more experience in a specific domain.

12.Ethical Considerations of Data Analysis:

Ans.

• Privacy: Safeguarding individuals’ privacy and ensuring responsible data handling.
• Bias: Addressing and mitigating biases in data, algorithms, and interpretations.
• Transparency: Clearly communicating methods, assumptions, and limitations.
• Consent: Ensuring informed consent when collecting and using data from individuals.
• Data Security: Protecting data from unauthorized access or breaches.

Hridhya Manoj

Hello, I’m Hridhya Manoj. I’m passionate about technology and its ever-evolving landscape. With a deep love for writing and a curious mind, I enjoy translating complex concepts into understandable, engaging content. Let’s explore the world of tech together