technical Interview

Question 1

Explain how you would estimate …

Answer 1

Let’s use the example of estimating the best month to offer a discount on shoes. First, I would collect historical sales data for shoes, looking at trends over time. I’d analyze seasonal patterns, taking into account key factors such as holidays, promotions, or economic conditions that may have influenced sales in the past. Next, I’d run a time-series analysis to predict future trends. Additionally, I’d consider customer behavior data and external market factors, such as competitor promotions or weather conditions that might affect sales. Based on the data insights, I’d estimate the optimal time to offer discounts to maximize sales and profitability. At Daikin, this approach aligns with making data-driven decisions to optimize business strategies.

Question 2

What is your process for cleaning data?

Answer 2

My data cleaning process involves several key steps:
1. Remove Duplicates: First, I check for and remove any duplicate entries.
2. Handle Missing Data: I assess missing values and decide whether to impute them using techniques like mean imputation for continuous data or KNN imputation, or if I should remove those rows if they are not significant.
3. Standardize Formats: I ensure consistency in formats (dates, currencies, units).
4. Detect and Handle Outliers: I identify outliers using statistical techniques and decide whether they are valid or should be excluded from the analysis.
5. Data Validation: I validate the data by running summary statistics to check for accuracy and consistency.

Question 3

What data analytics software are you familiar with?

Answer 3

I am proficient in Python, R, SQL, Power BI, and Tableau. These tools allow me to perform a wide range of data analysis tasks, from cleaning and visualizing data to building predictive models and automating workflows. At Daikin, I would leverage these tools to drive insights and inform business decisions.

Question 4

What scripting languages are you trained in?

Answer 4

I am proficient in Python, SQL, and Unix/Linux scripting. These languages have been integral to automating data collection, cleaning processes, and running simulations in previous roles. For instance, I wrote a Python script that automated chip design simulations during my internship at Arm, which reduced processing time by 40%. At Daikin, I would use these skills to streamline data processes and improve efficiency.

Question 5

What statistical methods have you used in data analysis?

Answer 5

I’ve used a variety of statistical methods, including regression analysis, hypothesis testing, clustering, and classification techniques. For example, in my medical expenses project, I used KNN, ensemble methods, and regression models to predict costs, improving prediction accuracy by 30%. I’ve also worked with hypothesis testing in SQL to validate assumptions.

Question 6

How have you used Excel for data analysis in the past?

Answer 6

I’ve used Excel extensively for tasks such as data cleaning, creating pivot tables, generating charts, and running basic statistical analyses. During my internship at Rice University, I regularly entered experimental data into Excel and used it to analyze trends and present insights. I also combined Excel with Python scripts to automate data reporting processes. Excel is a versatile tool that I can use to quickly analyze data and present findings to non-technical stakeholders at Daikin.

Question 7

Normal distribution

Answer 7

Definition: A normal distribution is a bell-shaped curve where most of the data points cluster around the mean, with symmetrical tails on either side.
Example: Heights of adult men in a population typically follow a normal distribution. Most men will have a height around the average, with fewer men being very short or very tall.

Question 8

Data wrangling

Answer 8

Definition:* Data wrangling is the process of cleaning, organizing, and transforming raw data into a format suitable for analysis.
Example: In a marketing dataset, data wrangling might involve correcting inconsistent date formats, filling in missing values, and merging multiple tables from different sources into one.

Question 9

KNN imputation method

Answer 9

KNN (K-Nearest Neighbors) imputation is a technique for filling in missing data by using the values of the nearest neighbors (based on similarity) to predict the missing values.
Example: In a dataset where income data is missing for some individuals, KNN imputation might use similar individuals’ incomes (based on age, job type, etc.) to fill in the missing values.

Question 10

*Clustering

Answer 10

Definition: Clustering is an unsupervised machine learning technique that groups data points into clusters based on their similarities.
Example: In customer segmentation, clustering could be used to group customers based on purchasing behavior, enabling businesses to tailor marketing strategies to each group.

Question 11

*Outlier:

Answer 11

Definition:* An outlier is a data point that differs significantly from the rest of the dataset.
Example: In a dataset of employee ages, if most employees are aged 20-50 and one employee is 95 years old, that 95-year-old would be considered an outlier.

Question 12

N grams

Answer 12

Definition:* N-grams are contiguous sequences of ‘n’ items from a

given sample of text or speech, commonly used in natural language processing (NLP).
Example: For a bigram (2-gram) analysis of the sentence “I love data science,” the bigrams would be “I love,” “love data,” and “data science.” This helps in analyzing common word pairings.

Question 13

Statistical model

Answer 13

Definition:* A statistical model is a mathematical framework that represents relationships between variables to make predictions or understand patterns.
Example: A linear regression model predicting house prices based on variables like the number of bedrooms, square footage, and location is a common type of statistical model.

Question 14

Data mining vs. data profiling

Answer 14

Data mining is the process of discovering patterns and relationships in large datasets, often for predictive analysis.
Example: Using data mining to identify purchasing trends in customer transaction data.
Data profiling involves assessing the quality and structure of data—such as its completeness, consistency, and accuracy—before analysis.
Example: Profiling a dataset to check for missing values, inconsistencies, and outliers before analysis.

Question 15

Quantitative vs. qualitative data

Answer 15

Quantitative data is numerical and can be measured.
Example: Revenue figures, sales quantities, or customer age.
Qualitative data is descriptive and cannot be easily measured.
Example: Customer feedback comments or interview responses.

Question 16

*Variance vs. covariance

Answer 16

Variance measures the spread of data points in a dataset from the mean.
Example: A high variance in employee salaries means salaries vary widely.
Covariance measures how two variables change together.
Example: Positive covariance between marketing spend and sales might indicate that as marketing spend increases, so do sales.

Question 17

Univariate vs. bivariate vs. multivariate analysis:

Answer 17

Univariate analysis* involves analyzing one variable at a time.
Example: Calculating the mean salary of employees.
Bivariate analysis examines the relationship between two variables.
Example: Analyzing the correlation between marketing spend and sales.
Multivariate analysis looks at more than two variables at once.
Example: Analyzing the impact of marketing spend, season, and customer demographics on sales.

Question 18

Clustered vs. non-clustered index:

Answer 18

Clustered index* sorts and stores the data rows in the table based on the index key values.
Example: A table of customers sorted by customer ID (clustered index) to improve search efficiency.
Non-clustered index creates a separate structure for the index that points to the data in the table.
Example: A table with a non-clustered index on customer names for faster searching by name.

Question 19

*1-sample T-test vs. 2-sample T-test in SQL

Answer 19

A 1-sample T-test compares the mean of a sample to a known value or population mean.
Example: Testing if the average salary of employees at Daikin is significantly different from the national average.
A 2-sample T-test compares the means of two independent groups to see if there’s a significant difference.
Example: Comparing the average sales figures between two regions to see if one region performs better than the other.

Question 20

Joining vs. blending in Tableau:

Answer 20

Joining combines data from two or more tables within the same data source based on a common field.
Example: Joining customer data with sales data based on customer ID.
Blending combines data from different sources that don’t necessarily have a common field.
Example: Blending customer satisfaction survey data with financial sales data, even if there’s no direct common field between the two datasets.

Question 21

Can you explain how you would approach building a predictive model for analyzing marketing campaign performance using the data you have?

Answer 21

“First, I would collect all relevant data needed for the project, utilizing sources such as Kaggle, internal databases, and third-party providers like market research tools. I would ensure data integrity by verifying the quality and consistency of these data sources. Once collected, I would organize the data into a single dataset using tools like Python, SQL, or Tableau. The data organization process would include merging multiple datasets and ensuring that the data structure aligns with the needs of the analysis.

The next step is data cleaning. This includes handling missing data—if the missing data is insignificant, I would remove those records. For significant gaps, I would use strategies like replacing missing values with the mean for continuous variables or the most frequent value for categorical variables. I prioritize data accuracy to maintain the highest standards of integrity.

Following that, I would conduct Exploratory Data Analysis (EDA). This phase is crucial for understanding data patterns, identifying correlations, and generating visual insights through tools like Power BI or Tableau. I would identify the target variable, look for potential correlations, and examine summary statistics. This helps form a hypothesis about the factors driving marketing performance, keeping customer impact at the forefront.

Based on the insights from EDA, I would then choose the most appropriate predictive model, such as regression for continuous outcomes or classification models for binary outcomes, or I might explore clustering if segmenting customers is required. The goal is continuous improvement, iterating on model performance to drive the best outcomes.

Finally, after building the predictive model, I would validate it using techniques like cross-validation to ensure accuracy and scalability. By continually refining the model and staying on top of the latest innovations in data science, I can provide valuable insights that align with Daikin’s goal of optimizing the customer experience and delivering long-term business value.”

Question 22

How would you approach improving the efficiency of an existing SQL database, especially when managing large volumes of data?

Answer 22

“To improve the efficiency of an existing SQL database, especially when managing large volumes of data, I would take a multi-step approach:

1. Indexing Optimization: I would review and optimize indexes to ensure that the most frequently queried columns are indexed properly. This reduces the time it takes to retrieve data, speeding up queries significantly. However, I would be careful to balance this by not over-indexing, which can slow down data inserts and updates.
2. Query Optimization: I would analyze slow-running queries using tools like SQL Profiler or EXPLAIN plans. By identifying inefficient queries, I can rewrite them to be more efficient—such as by avoiding SELECT * statements, reducing joins, or simplifying complex subqueries.
3. ** ery performance by limiting the amount of data scanned for each query.
4. Implementing Caching: I would set up query caching to reduce the load on the database by storing the results of frequent queries temporarily. This way, repetitive queries can retrieve results from cache instead of recalculating the data.
5. Regular Maintenance: Regular maintenance activities such as database vacuuming, defragmenting, and updating statistics help ensure the database performs optimally as it grows. This reflects Daikin’s focus on continuous improvement and maintaining a high standard of quality.
6. Scaling the Infrastructure: If necessary, I would look into horizontal scaling by distributing data across multiple servers or using sharding to ensure efficient access to data as it grows. Alternatively, vertical scaling (adding more resources to the server) could be a solution for improving performance.
7. Monitoring and Metrics: Finally, I would set up automated monitoring systems to track the performance of the database and identify bottlenecks early. This way, we can innovate by introducing new technologies or approaches, like cloud-based solutions such as AWS RDS or Snowflake, to ensure long-term scalability and performance.”

Question 23

How would you handle integrating third-party data into an existing data warehouse while ensuring data quality and consistency?*

Answer 23

When integrating third-party data into an existing data warehouse, ensuring data quality and consistency is crucial. I would follow a structured approach:

1. Data Validation: First, I would validate the incoming data to ensure it meets our quality standards before integration. This involves checking for missing values, duplicates, and any outliers that could affect the overall data quality. I would collaborate with the third-party provider to clarify any inconsistencies or discrepancies in the data.
2. Data Transformation and Standardization: I would ensure the third-party data is transformed into the correct format to match the schema of the existing data warehouse. This step involves mapping the incoming data fields to the appropriate tables and columns, while standardizing units, formats, and naming conventions to maintain consistency across all datasets. Using ETL (Extract, Transform, Load) tools like AWS Glue or Alteryx can automate this process and reduce the chance of human error.
3. Data Cleansing: During the integration process, I would perform data cleansing to eliminate any errors, fill missing values, and ensure data accuracy. For example, I could use imputation techniques for missing data and remove any erroneous entries. My priority is maintaining the integrity of the data, as clean and accurate data leads to reliable insights.
4. Data Integration and Testing: After cleansing, I would integrate the data into the warehouse, ensuring that it merges seamlessly with the existing datasets. To maintain data quality, I would conduct thorough testing to ensure that the data integrates as expected and that queries return accurate and consistent results.
5. Monitoring and Continuous Improvement: Once the data is integrated, I would set up automated monitoring to regularly check for data quality issues. This step aligns with Daikin’s focus on continuous improvement, allowing us to identify and resolve any future data discrepancies quickly.

By following this process, I ensure that third-party data is integrated seamlessly while maintaining high data quality and consistency. This approach reflects Daikin’s commitment to integrity in managing data and delivering accurate, actionable insights to stakeholders.”

Question 24

Can you explain how you would use Python to automate a repetitive data analysis task?*

Answer 24

My first step would be to fully understand the repetitive task by breaking it down into smaller steps, identifying the inputs, outputs, and any variables involved. This ensures that the automation captures the full scope of the task and that it runs efficiently.

Next, I would write a Python script to automate these steps, paying close attention to time efficiency and eliminating redundant code. I would use libraries such as pandas for data manipulation, os to access and navigate the file system, and sched or cron for scheduling tasks to run automatically. This is particularly useful if the task runs in a Linux environment, but I could adjust the approach for Windows as needed.

To add flexibility, I would incorporate user-defined variables, allowing the script to accept different parameters, such as time intervals, expected outputs, or specific conditions, ensuring that the automation can adapt to various needs. This would allow users to adjust the script without having to modify the underlying code.

Finally, I would document the entire process, including instructions for setup, usage, and troubleshooting. Clear documentation ensures that the script is accessible and understandable by others, making it easier for team members to use or modify the code in the future. This process aligns with Daikin’s emphasis on innovation by streamlining repetitive tasks and promoting efficiency across the team.”