The industry exposure for a data scientist is in the boom phase today.The Economic Times says that the number of job postings for Data Science profiles has grown more than 400 times in the past year. So, if you wish to become a Data Scientist, here are some of the best interview questions and answers to help you get the job.
So here are the top 10 data science interview questions with answers to master the selection process. In this article, we will talk about data science interview questions, data science interview questions and answers, and data science interview questions for freshers.
Data science interview questions and answers
What do you mean by data science?
Data science is a branch of computer science that focuses on turning data into information and figuring out what that information means. Why is Data Science so popular? It’s because the insights we can get from the data thanks to Data Science have led to some big changes in many products and companies. With this information, we can figure out a customer’s tastes, how likely it is that a product will do well in a certain market, etc.
What do you mean by linear regression?
Linear regression helps you figure out how the dependent and independent variables are connected in a straight line. Linear regression is the supervised learning algorithm that helps figure out how two variables are related in a straight line. One is the predictor or independent variable, and the other is the response or dependent variable. In Linear Regression, we try to figure out how the change in the dependent variable depends on the change in the independent variable. Simple linear regression is when there is only one independent variable. Multiple linear regression is when there is more than one independent variable.
How is data science and traditional application programming different from each other?
Data science is fundamentally different from traditional application development in the way it builds systems that have value.
Data science is a type of applied statistics that asks programmers to do more with data. The statistical side of data science makes it a little different from traditional application programming. Also, data science requires a lot of analyzing, making predictions based on time series, and paying close attention to details. This is not something that traditional programmers are expected to do.
What do you mean by bias in Data Science?
Bias is a type of error that can happen in a Data Science model when an algorithm isn’t strong enough to pick up on the patterns or trends in the data. This error happens when the data is too complicated for the algorithm to understand, so it builds a model based on simple assumptions instead. This makes the accuracy worse because it leads to underfitting. Linear regression, logistic regression, and other algorithms can lead to a lot of bias.
Why do we use Python for Data Cleaning in Data Science?
Data scientists have to clean and change huge amounts of data into a form that they can use. For better results, it’s important to deal with redundant data by getting rid of things like outliers that don’t make sense, records that aren’t formatted right, missing values, inconsistent formatting, etc.
Data cleaning and analysis are done with Python libraries like Matplotlib, Pandas, Numpy, Keras, and SciPy. These libraries are used to load the data, clean it up, and do good analysis. For example, a CSV file called “Student” has information about a school’s students, such as their names, grades, marks, addresses, phone numbers, etc.
Why does Data Visualization use R?
With more than 12,000 packages in Open-source repositories, R has the best ecosystem for analyzing and displaying data. It has a big community behind it, so you can easily find answers to your questions on sites like StackOverflow.
It has better data management and supports distributed computing by dividing operations among multiple tasks and nodes. This makes large datasets easier to use and faster to run.
Tell me about the popular libraries used in data science.
- TensorFlow lets you do parallel computing, and Google backs its library management.
- SciPy is mostly used to solve differential equations, do multidimensional programming, change and display data in graphs and charts, and solve multidimensional programming problems.
- Pandas are used to make business applications that can do ETL (Extracting, Transforming, and Loading the datasets).
- Matplotlib is free and open-source, so it can be used as an alternative to MATLAB. This makes it run faster and use less memory.
- PyTorch is best for projects that use Deep Neural Networks and Machine Learning algorithms.
What do you mean by variance in data science?
Variance is a type of error that happens when a Data Science model is too complicated and learns features from data along with the noise in the data. This kind of mistake can happen if the algorithm used to train the model is complicated, even though the data and underlying patterns and trends are easy to find. This makes the model very sensitive, so it does well on the training dataset, but not so well on the testing dataset or any other kind of data, it hasn’t seen before. Most of the time, variation makes testing less accurate and leads to overfitting.
In a decision tree algorithm, what does pruning mean?
When you prune a decision tree, you get rid of the parts of the tree that aren’t needed or are redundant. By pruning, the decision tree gets smaller, which makes it work better and gives better accuracy and speed.
How does a normal distribution work?
Data distribution is a way to see how data is spread out over time. There are many ways to share data. For example, it might lean to the left or right or be all jumbled up.
Data can also be spread out around a central value, such as the median, mean, or mode. This kind of distribution looks like a bell-shaped curve and has no bias to the left or right. The mean and the middle point of this distribution are also the same. The name for this type of distribution is “normal distribution.”
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Top 10 data science interview questions for freshers
- What tools and gadgets do you plan to use as a data scientist?
- How do you put together big data?
- Is it always better to have a lot of data?
- What do you mean by root cause analysis?
- Why do you want to work in this company as a data scientist?
- What is the difference between regression and classification?
- How is sampling important? Tell some sampling techniques
- What is the difference between normalization and standardization?
- What is imbalanced data?
- How will you manage to balance the data?
Conclusion
I hope this blog has helped you gain knowledge about data science. Data science is one of the most demanded skills in 2023. Read these questions and answers carefully; this will help you to ace the data science interview.
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