I am starting with a data science interview series to check your knowledge, let's start with the first question. Here it is:
Question 1:
Explain the difference between supervised and unsupervised learning.
Let me know answer in comments 👇👇
Question 1:
Explain the difference between supervised and unsupervised learning.
Let me know answer in comments 👇👇
👍23❤5
Which of the following is a cluster computing framework that specialises in working with big data?
Anonymous Poll
8%
HTML
51%
Apache Spark
4%
CSS
28%
Pandas
9%
Scipy
👎4👍2❤1👏1
Question 2:
What is overfitting in machine learning, and how can you prevent it?
What is overfitting in machine learning, and how can you prevent it?
👍19❤2🔥1
Question 3:
What is the bias-variance tradeoff in machine learning?
What is the bias-variance tradeoff in machine learning?
👍9❤1
Question 4:
What are some common techniques to handle missing data in a dataset?
What are some common techniques to handle missing data in a dataset?
👍6
5 DataAnalytics Project Ideas to boost your resume:
1. Stock Market Portfolio Optimization
2. YouTube Data Collection & Analysis
3. Elections Ad Spending & Voting Patterns Analysis
4. EV Market Size Analysis
5. Metro Operations Optimization
1. Stock Market Portfolio Optimization
2. YouTube Data Collection & Analysis
3. Elections Ad Spending & Voting Patterns Analysis
4. EV Market Size Analysis
5. Metro Operations Optimization
👏29👍5❤4
Question 5:
Explain the concept of a confusion matrix. What are precision, recall, and F1-score?
Explain the concept of a confusion matrix. What are precision, recall, and F1-score?
Question 6:
What is cross-validation, and why is it important in machine learning?
What is cross-validation, and why is it important in machine learning?
👍8
Question 7:
Can you explain the difference between a parametric and a non-parametric model?
Can you explain the difference between a parametric and a non-parametric model?
👌11👍4
What type of project do you enjoy working on the most?
1. Personal projects
2. Open-source contributions
3. Freelance work
4. Corporate projects
5. Academic projects
If any other, add in comments 👇👇
1. Personal projects
2. Open-source contributions
3. Freelance work
4. Corporate projects
5. Academic projects
If any other, add in comments 👇👇
👍11
Question 8:
What is feature engineering, and why is it important in building machine learning models?
What is feature engineering, and why is it important in building machine learning models?
👍7❤2
Question 9:
What is the purpose of regularization in machine learning, and what are some common types of regularization techniques?
What is the purpose of regularization in machine learning, and what are some common types of regularization techniques?
Top Platforms for Building Data Science Portfolio
Build an irresistible portfolio that hooks recruiters with these free platforms.
Landing a job as a data scientist begins with building your portfolio with a comprehensive list of all your projects. To help you get started with building your portfolio, here is the list of top data science platforms. Remember the stronger your portfolio, the better chances you have of landing your dream job.
1. GitHub
2. Kaggle
3. LinkedIn
4. Medium
5. MachineHack
6. DagsHub
7. HuggingFace
Add more in comments 👇👇
Build an irresistible portfolio that hooks recruiters with these free platforms.
Landing a job as a data scientist begins with building your portfolio with a comprehensive list of all your projects. To help you get started with building your portfolio, here is the list of top data science platforms. Remember the stronger your portfolio, the better chances you have of landing your dream job.
1. GitHub
2. Kaggle
3. LinkedIn
4. Medium
5. MachineHack
6. DagsHub
7. HuggingFace
Add more in comments 👇👇
👍14❤1
Question 10:
What is the difference between bagging and boosting in ensemble methods?
What is the difference between bagging and boosting in ensemble methods?
👍1