Data science is a combination of scientific methods, data analysis, informatics, mathematics, business knowledge, algorithms, and machine learning techniques used to understand and analyze actual phenomena and get key insights from large distributions of structured and unstructured data. It is associated with data mining, machine learning, and big data.
Discovering insights and trends from data has been from time immemorial, from Mesopotamians to Greek everyone used it. The early Egyptians used census data to increase tax collection efficiency, and most of the time they were able to accurately predict Nile river flooding based on different metrics. Since then, people who work in data science have carved out distinct technical expertise in their field of work.
Data science deals with quantitative and qualitative data analysis and emphasizes mostly prediction and action. Login to the Entri and study the newest data science course.
Let us take a look at the top 10 data science interview questions below.
Data Science Interview Questions
Let’s skip the first question, “what is Data Science”, as we have already answered it, and move on to other serious questions to expect for Interviews.
1. What is Selection Bias?
When researchers make errors in selecting who will be studied for their research is selection bias. It happens when participants are not taken from random groups. This is also called the selection effect. It is a distortion in statistical analysis caused by the method of sample collection.
2. What do you understand about the term Normal Distribution?
A probability distribution shows that data near the mean are more frequent in occurrence compared to data that are far from the mean. In graph form, the normal distribution will look like a bell curve.
3. What is the p-value?
The p-value is the number between 0 and 1, Based on the value it will denote the strength of the results. In a hypothesis test, the p-value can help in determining the strength results.
4. What Is the Law of Large Numbers?
The Law of large numbers is a theorem that describes the outcome by repeating the same experiment many times. It serves as the foundation for frequency-style reasoning as it states that the sample means, variance, and standard deviation converge to what they are attempting to estimate.
5. Explain Cross Validation?
A model validation technique is used to determine how statistical analysis results will generalize to a different data set. It is typically used in situations where the goal is to forecast and one wants to estimate how accurately a model will perform in real-time.
6. What Are Confounding Variables?
A confounding variable is a third variable in statistics that influences both the dependent and independent variable causing a spurious association.
7. What is Survivorship Bias?
A logical error that occurs in some processes, when casually overlooking some process that did not work because of their lack of prominence and only looking and focusing on aspects that support surviving. This can cause wrong conclusions in numerous different means.
8. What is pruning in a Decision Tree?
To reduce the size of decision trees in machine learning and search algorithms, pruning is done to diminish decision trees by removing branches with little power to classify instances. When we remove sub-nodes that have little power from a decision node, we call this process pruning, it is the inverse of splitting.
9. What is Systematic Sampling?
When elements are selected from an ordered sampling frame, that is the list is progressed circularly so once you reach the end of the list, it is progressed from the top again. An example of systematic sampling is the equal probability method.
10. What is a confusion matrix?
To derive various measures such as error rate, accuracy, specificity, sensitivity, precision and recall, a 2X2 table containing 4 outputs provided by the binary classifier is used. This 2×2 table is called a confusion matrix.
Final thoughts
Data science is one of the largest growing industries and its impact is far-reaching, many new job opportunities are opening up for those having skills, and the lack of competition for these jobs makes data science a very lucrative option for a good career path.
