Addverb Technologies Intern Interview Questions, Process, and Tips

It was a combination of Maths and Electronics basics.

Q1. Introduce yourself?
Ans. 
I am a recent graduate with a degree in Computer Science and a passion for coding and problem-solving.
• Recent graduate with a degree in Computer Science

• Passionate about coding and problem-solving

• Strong communication and teamwork skills
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Q2. Tell me about your projects?
Ans. 
I have worked on various projects including a web development project, a data analysis project, and a machine learning project.
• Developed a website for a local business using HTML, CSS, and JavaScript.

• Conducted data analysis on customer feedback data to identify trends and make recommendations for improvement.

• Implemented a machine learning algorithm to predict customer churn for a telecom company.
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Easy test was conducted at our college

Q1. Why do you like data?
Ans. 
I like data because it allows me to uncover insights, make informed decisions, and solve complex problems.
• Data helps me understand trends and patterns in various industries.

• I enjoy using data to drive business strategies and improve processes.

• Analyzing data allows me to make evidence-based decisions and solve real-world problems.

• Data visualization helps me communicate findings effectively to stakeholders.
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Q2. Why do you want to join Honeywell?
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Q3. Where do you see yourself in 5 yrs?
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Q1. 1. How would you deal with time series data
Ans. 
Time series data can be analyzed using techniques like smoothing, decomposition, forecasting, and anomaly detection.
• Use smoothing techniques like moving averages to remove noise and identify trends.

• Apply decomposition methods like seasonal decomposition of time series (STL) to separate trend, seasonality, and residual components.

• Utilize forecasting models such as ARIMA, SARIMA, or Prophet to predict future values.

• Imple...read more
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Q2. 2. How would you handle missing data in time series data
Ans. 
Handle missing data in time series data by imputation, interpolation, or deletion.
• Impute missing values using mean, median, mode, or predictive modeling techniques.

• Interpolate missing values using linear interpolation, spline interpolation, or time-based interpolation.

• Delete rows with missing values if they are few and do not significantly impact the analysis.
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