The field of data science is ever-expanding, with new technologies…
The field of data science is ever-expanding, with new technologies and methodological advances happening daily. There is no shortage of opportunities for those seeking to make their mark in data science. But to secure a job in this area, you will have to prepare well for your tests and interview. To help you do that, it helps to answer some data science job interview questions.
In this article, we’ll look at some interview questions you should prepare for. These cover questions that will evaluate your technical skills and personal traits.
Sample Data Science Interview Questions With Answers
A data set consists of more than 30 percent missing values. How do you plan on dealing with them?
There are many ways to handle missing data values.
Rather than having large data sets, we can remove missing data values by simply removing the rows. This is the easiest way; we can predict values with the rest of the data.
For smaller data sets, we can substitute missing values for the mean or average of the rest using a pandas’ data frame in Python. There are various ways to do this, including df. mean() and dF.fillna(mean).
How is logistic regression done?
Logistic regression measures the relationship between dependent variables (our labels of what we want to predict) and one or more independent variables (Our features). This is done by using its logarithmic function inference (sigmoid).
What is dimensionality reduction, and what are its benefits?
Dimensionality reduction refers to converting data sets with vast dimensions into data with fewer dimensions (fields). This is done to convey similar information quickly.
Reducing data storage space helps compress it. A smaller dimension also reduces computation time. The redundant features are removed; for example, one unit should never store a value in both units (meters and inches).
How can you select k for k-means?
In order to select k from Rk-means clustering, we use the elbow method. In the elbow method, one runs k-means clustering on a data set where ‘k’ is the number of clusters. As defined in the sum of squares (WSS), it is the sum of the squared distance between each cluster member and its centroid.
What is a Confusion Matrix?
A Confusion Matrix is the summarizing of predictions made for a particular problem. It is a table used to describe the model’s performance. The confusion Matrix uses an n*n matrix to evaluate classification models.
How does Data Science differ from traditional application programming?
Data Science has a fundamental difference from traditional application programming. In traditional programming, one has to define rules to translate input into output. Data Science, on the other hand, automatically produces rules for the data.
Can you mention some sampling methods? What are the main advantages of Sampling?
Sampling is selecting individual members or subsets of the population to estimate their character. Probability and non-probability are the two main types of Sampling.
Other Data Science Job Interview Questions
Personal Interview Questions
- Can you tell me more about yourself?
- What are your top professional qualities? What are your weaknesses?
- Is there a Data Scientist that you look up to?
- What led you to the field of data science?
- What unique skills or characteristics would you bring to our company?
- What led you to leave your last job?
- What kind of compensation are you expecting from this job?
- Do you prefer to work alone or in a team of Data Scientists?
- What career goals do you want to achieve in five years?
- Do you have a specific strategy for handling stress on the job?
- How do you find your motivation to work?
- How do you measure success?
- How would you describe your ideal workplace?
- What are your interests outside of data science?
- How do a type I error and a type II error differ?
- Can you give an example of a non-Gaussian distribution for data sets?
- Can you explain the difference between the K Nearest Neighbors (KNN) algorithm and the K-means clustering algorithm?
- How would you approach creating a logistic regression model?
- What is the 80/20 rule? How important is it to model validation?
- Would you please explain the differences between L1 and L2 regularization methods?
- What are the steps for data wrangling and data cleaning before applying machine learning algorithms?
- Do you know what a histogram and a box plot are?
- What is the difference between a false positive and a negative? Do you think it would be better to have too many false positives or too many negatives?
- Do you prefer an ensemble of 50 small trees or a large one?
- What specific data science project at our company are you interested in?
- Could you please list a few examples of best practices in data science?
Technical Data Science Questions
- Have you ever worked on a data science project that required a significant programming component? What have you taken away from experience?
- How can you represent data effectively using five dimensions?
- Multiple regression is needed to generate a predictive model. What are your methods for validating this model?
- How do you make sure that the changes you make to an algorithm are improving?
- Consider providing your method for handling an imbalanced data set that is being used to predict patterns (e.g., far more negative classes than positive classes).
- How would you validate your model of a quantitative outcome variable that you created through multiple regression?
- There are two different models of comparable computational performance and accuracy. Can you explain why and how you decided which to use for production?
- A data set consists of variables with a substantial portion missing values. Do you have any suggestions for a better approach?
Data science is a diverse field, one that encompasses many skill sets. Data scientists are the backbone of how our data is collected, how it is analyzed, and how it is used. And through answering these data science job interview questions, you will increase your chance of landing your dream job, whatever that may be!