Data science is one of the most in-demand skills today. As a result, interviewers are looking for candidates who have the right mix of technical skills and soft skills. While every data science interview is different, there are some questions that are asked more often than others. In this article, we will discuss the most important data science interview questions.
Whether you’re a candidate or interviewer, preparing for a data science interview is key to success. To help you in your preparation, we’ve compiled a list of questions that are commonly asked in data science interviews, along with advice on how to answer them. Questions about your technical skills and experience are to be expected, but you may also be asked behavioral or fit questions.
what data science is, why it’s important
Data science is the process of extracting information from data. It is a relatively new field that combines statistics, computer science, and machine learning.
Data science is important because it allows us to make better decisions by understanding the data we have available. For example, by analyzing customer data, companies can better target their marketing efforts and improve their products. Additionally, data science can be used to predict things like future demand for a product or service.
The basics: types of data, features, etc.
Data science is a relatively new field, and as such, there are a lot of questions that interviewer may ask about the basics of the field. Here are some examples of questions you should be able to answer:
What are the different types of data?
There are three main types of data: structured, unstructured, and semi-structured. Structured data is typically found in databases and has a predefined schema. Unstructured data does not have a predefined schema and is often found in text or social media data. Semi-structured data has some elements of both structured and unstructured data.
What are features?
Features are pieces of information that can be used to describe an object.
Data collection: how to collect and clean data
1. Data collection is a process of gathering data from various sources and then cleaning it to make it usable for analysis.
2. There are many different methods of data collection, such as surveys, interviews, focus groups, and observation. The most important part of data collection is to make sure that the data is accurate and representative of the population.
3. Data cleaning is an important step in the data analysis process, and it involves removing invalid or inaccurate data and standardizing the format of the data. This step ensures that the data is ready for further analysis.
exploratory data analysis: finding trends and patterns
1. When it comes to data science, one of the most important skills is exploratory data analysis. This involves finding trends and patterns in data sets in order to better understand them.
2. There are a few different techniques that can be used for exploratory data analysis, such as visualisation and statistical modelling. It is important to choose the right technique for the job, as each has its own strengths and weaknesses.
3. Exploratory data analysis is a vital part of any data scientist’s toolkit, and can be used to gain insights that would otherwise be hidden in the data. With the right approach, it can help you uncover trends and patterns that could be used to improve decision-making.
modeling: building models to make predictions
In predictive modeling, a model is created or chosen based on data that has already been collected. This data is then used to make predictions about future events.
Predictive modeling is a powerful tool that can be used in a variety of fields, from marketing to medicine. It can help answer questions such as “What are the chances of a customer buying this product?” or “What is the likelihood of this patient developing this disease?”
Predictive modeling is not always accurate, but it can give us insights that we would not otherwise have. When used correctly, it can be a valuable tool for making decisions.
evaluation: assessing the accuracy of predictions
When it comes to data science, one of the most important skills is being able to assess the accuracy of predictions. This is because data science is all about making decisions based on data, and if those decisions are wrong, it can cost a company dearly.
There are a few different ways to assess the accuracy of predictions, but one of the most common is called cross-validation. This involves dividing the data set into two parts, training the model on one part and then testing it on the other. The idea is that if the model can accurately predict the outcome for new data, then it is likely to be accurate in the real world as well.
Another way to assess accuracy is to look at how well the model performs on similar data sets.
communication: presenting findings to stakeholders
In today’s business world, data is everything. It drives decisions large and small, from which products to stock on store shelves to where to open new locations. But data isn’t useful unless it can be understood and communicated clearly to the people who need to use it.
That’s where data scientists come in. They take complex data sets and turn them into insights that businesses can use to make better decisions. But before they can do that, they need to be able to answer some tough questions from stakeholders.
What are the most important things you need to know when presenting findings to stakeholders? We asked a panel of Forbes Data Science contributors to weigh in. Here’s what they had to say:
1. What is the problem you’re trying to solve?
2. What data are you using?
3. How did you go about solving the problem?
conclusion: recap main points
As data scientists, we are constantly being asked questions. Whether it’s an interview for a new job, or a meeting with stakeholders to discuss the results of our work, we need to be able to communicate effectively. Here are some of the most common questions you’re likely to encounter, and how you can answer them confidently.
1. What is data science?
2. What kinds of problems can data science help solve?
3. What is the difference between supervised and unsupervised learning?
4. What is a neural network?
5. What is a support vector machine?
6. What is linear regression?
7. What is logistic regression?
8. What are some common issues in machine learning?