When it comes to interviews, one of the most common features today is the emphasis on specifications. In other words, there is strict adherence to specific aspects of a subject, thanks to a renewed focus on the depth of the candidate’s knowledge. Naturally, the competition is fierce and therefore companies look forward to picking only the finest candidates for their jobs. This means that you need to be at your best in the interview.
Here are some of the most frequently asked data science interview questions and answers:
1. Can you define the meaning of logical regression?
Logical regression is a technique used in data science in order to trot out a binary outcome of a particularly linear conglomeration of the concerned predictor variables. By binary outcome it is meant that the output is either o or 1. 2. What do you mean by the term linear regression?
Essentially, the process of linear regression is a statistical method in which one variable is used to calculate the value of another variable. Technically speaking, the two variables are known as the predictor variable and criterion variable. So linear regression is essentially defined as the prediction of a criterion variable with the help of the given predictor variable. 3. Can you tell us how would you identify the likes of outlier values?
So far as outlier values are concerned, they may be identified by the application of a graphical analysis procedure. However, it is important to keep in mind that all outlier values should not be confused with extreme values. You can either alter the value to bring it within range or remove it straight away. 4. Can you explain summarily the box cox transformation in regression models?
Essentially, the box cox transformation technique is chiefly used for the transformation of non-dependent variables. There might be situations when it is necessary to transform the fundamental response variable in order for the data to comply with certain requisite assumptions. Broadly speaking, the box cox transformation ensures the running of a relatively broader number of tests. 5. Since we are dealing with data science, can you describe the essential procedure of a basic analytics project?
First and foremost, it is essential to thoroughly comprehend the breadth and depth of the current business problem and acquaint yourself with the data involved. Then you need to set the ground for data modeling by identifying and treating potential outliers, exploring the ratio of missing values and transforming variables. After having prepared the new model, it needs to be run repetitively until the favourable outcome is reached. The model awaits validation by a new data set. Lastly, you implement the model and follow up the results in order to scrutinize the performance ratio of the model within a particular period of time. 6. Can you highlight the differences between a test set and a validation set?
A test set, as the name suggests, is chiefly used to test or evaluate the performance of a machine learning model. A validation test, on the other hand, is primarily used for parameter selection. 7. Do you think there is a definitive significance to the aspect of selection bias?
Indeed, selection bias is one of the most important aspects. In fact, the essential advantage of selection bias lies in the fact that it alone highlights the holes in a particular sample. Therefore, the significance of selection bias cannot be undermined in that it spotlights upon the lack of randomization across elements of a particular data or group. 8. What do you understand by the term skewed distribution?
Skewed distributions are those instances when the graph representation is visually lopsided, with more observations on one side of the graph. 9. Can you highlight the optimum assessment of a standard logistical model?
First and foremost, you need to assess a model by searching out the true negatives and the false positives using the classification matrix. Next, concordance assumes a great significance in that it allows you to se whether the model can tell an event from a non-event. Also, you can do a comparative study in order to assess the model through the perspective of random selection. 10. Tell us how can you tackle the issue of missing values?
So far as missing values are concerned, it is important to first identify the variables of those values. Generally speaking, the missing value is often treated as the default value. Specifically speaking, it is better to designate it as a default value if the missing value is a categorical variable. However, at the same time, it is important to consider the extent to which the consideration of missing values should be taken up. If a majority of the values of a variable, it is best to shuck the variable altogether.
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