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Data science has emerged as one of the fastest growing career fields in just a few years. In fact, it was also named the best job of 2017 (in the US), as per a report by Glassdoor. The exponential growth witnessed by the industry has increased the demand for data scientists across several fields. However, breaking into the field can be challenging. It requires intense training and skill development that only a few corporate trainers such as Manipal ProLearn can offer.
While your course could train you to become a great data scientist, it may not be most effective without a job. So it is important to be well prepared for the first step – the interview. How do you nail the interview? Here are the answers and the questions!
1: Why is data cleaning crucial to the process of analysis?
Answer: The data collected from various sources could have multiple instances of errors, duplications and inconsistencies, making it irrelevant for modeling or analysis. Data cleaning helps identify irrelevant data, which can be replaced or deleted, thereby making the analysis an error-free and effective process.
2: What do you mean by statistical power?
Answer: Statistical power is the probability that an effect will be detected by a study when it is there to be detected. If the statistical power is high, it is less likely that you will make Type-II errors where you conclude that there is no effect, when in fact, there is one.
3: What is normal distribution?
Answer: When all the data is distributed around a central value without bias to the left or right, the data takes a bell-like structure – referred to as the Bell Curve and accounts for the nominal distribution of data. For example, most students are likely to score average marks in an exam, which is the central value. The marks of the remaining students are either distributed on the left or the right side.
4: For text analytics, which language would you prefer? Python or R?
Answer: Though the question inclines towards your personal preference, it would be ideal to answer it by stating that you prefer Python. The being that it has high-performance tools for data analysis along with panda library, which provides data structures that are simple to use.
5: What do you mean by selection bias?
Answer: Selection bias occurs when proper randomization is not achieved during the selection of groups, individuals or data to be analyzed. If there is a selection bias, it means that the obtained sample does not accurately represent the population, which was intended to be analyzed. Selection bias includes:
- Time interval
- Sampling bias
6: What is the goal of A/B testing?
Answer: The basic goal of A/B testing is to carry out a controlled comparison of two entities with each other to determine which one is performing better. Also known as split testing or bucket testing, this test is commonly used to compare versions of web pages/mobile apps. It is a popular and efficient testing method used by statisticians for examining the effectiveness of online processes/products.
7: What is clustered and systematic sampling?
Answer: Cluster sampling is a data science technique in which clusters of participants representing the whole population are identified and included in the sample.
Systematic sampling is the technique of selecting samples randomly based on the system of defined intervals. It is a great way to collect data without having to use a random number generator.
While your data science course must have taught you the answers to these questions, in a situation like a job interview, it is not uncommon to struggle because of performance pressure or anxiety. So the first thing you should keep in mind while preparing for your interview is to be calm and confident about your answers. Confidence and composure along with your data science knowledge and skills will help you create a positive impact on the interviewer.