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Carbynetech Data Scientist Interview Questions and Answers
Carbynetech Data Scientist Interview Experiences
1 interview found
Anonymous
Interview Questionnaire
1 Question
- Q1. Seriously the interviewer was a total cartoon he holds a PhD but his IQ is less than room temperature as he doesn't know image processing can be done by traditional image processing, variational method and...
Data Scientist Jobs at Carbynetech
Top trending discussions
Interview questions from similar companies
Interview Questionnaire
1 Question
- Q1. What PCA, Decision tree and computer vision
- Ans.
PCA is a dimensionality reduction technique, decision tree is a classification algorithm, and computer vision is a field of study focused on enabling computers to interpret and understand visual information.
PCA is used to reduce the number of variables in a dataset while retaining the most important information.
Decision trees are used to classify data based on a set of rules and conditions.
Computer vision involves usin...
Skills evaluated in this interview
I applied via Company Website and was interviewed before Jan 2020. There was 1 interview round.
Interview Questionnaire
1 Question
- Q1. Which job gives me do that work because of this job very important to me
Interview Preparation Tips
I applied via Referral and was interviewed in Mar 2021. There were 4 interview rounds.
Interview Questionnaire
2 Questions
- Q1. What is data science
- Ans.
Data science is the field of extracting insights and knowledge from data using various techniques and tools.
Data science involves collecting, cleaning, and analyzing data to extract insights.
It uses various techniques such as machine learning, statistical modeling, and data visualization.
Data science is used in various fields such as finance, healthcare, and marketing.
Examples of data science applications include fraud...
- Q2. What is phyton and R
- Ans.
Python and R are programming languages commonly used in data science and statistical analysis.
Python is a general-purpose language with a large community and many libraries for data manipulation and machine learning.
R is a language specifically designed for statistical computing and graphics, with a wide range of packages for data analysis and visualization.
Both languages are popular choices for data scientists and hav...
Interview Preparation Tips
Skills evaluated in this interview
Interview Questionnaire
1 Question
- Q1. Please tell me something about yourself.What is your experience? What are your goals and ambitions?Why We should hire you? Strengths and weaknesses etc.
I applied via Campus Placement and was interviewed before Sep 2020. There were 3 interview rounds.
Interview Questionnaire
1 Question
- Q1. Nothing much technical
Interview Preparation Tips
2. Fluency in English is important (depends on interview panel)
3. Clarity on what your talking about
Interview Questionnaire
1 Question
- Q1. Basic ML
(3 Questions)
- Q1. About Machine learning basics, activation functions linear regression, cnn, all basics..
- Q2. About project questions, about sdlc basic 3 questions
- Q3. About Why not used another model for training?
Interview Preparation Tips
Interview Questionnaire
1 Question
- Q1. Describe the project , EDA
- Ans.
The project involved exploratory data analysis (EDA) to gain insights and identify patterns in the data.
Performed data cleaning and preprocessing
Visualized data using various charts and graphs
Identified correlations and relationships between variables
Used statistical methods to analyze data
Generated hypotheses for further analysis
Interview Preparation Tips
Skills evaluated in this interview
mahmood ghazi
(4 Questions)
- Q1. 2 round technical interview, both of them about ML and last one concerning statistics and ML.
- Q2. What is linear regression?
- Ans.
Linear regression is a statistical method to model the relationship between a dependent variable and one or more independent variables.
It assumes a linear relationship between the variables
It is used to predict the value of the dependent variable based on the independent variable(s)
It can be simple linear regression (one independent variable) or multiple linear regression (more than one independent variable)
It is commo...
- Q3. Question regarding confusion matrix?
- Q4. Name various ML algorithm?
- Ans.
ML algorithms are used to train models on data to make predictions or decisions. Some popular ones are SVM, KNN, and Random Forest.
Support Vector Machines (SVM)
K-Nearest Neighbors (KNN)
Random Forest
Naive Bayes
Decision Trees
Linear Regression
Logistic Regression
Neural Networks
Gradient Boosting
Clustering Algorithms (K-Means, Hierarchical)
Association Rule Learning (Apriori)
Dimensionality Reduction Algorithms (PCA, LDA)
Reinf
Interview Preparation Tips
Know basics is enough, not need sequenced models
Skills evaluated in this interview
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