Facing a data science interview can be nerve-wracking, but our guide has you covered. Discover the most common data science interview questions and expertly crafted answers to help you confidently tackle any interview and secure your dream data science role. Data Science has come up as the most lucrative career option for many individuals.
As per a recent survey conducted by Glassdoor, it has been placed #1 on the American list of 25 Best Jobs. In the coming years, the demand for data scientists is bound to go higher. Companies that are able to leverage a huge amount of data for making their customers happy, and build products that are expected to succeed in the future.
Since many students are keen on pursuing the best data science course online from teenagers to working professionals, different options are available for each one as per their individual needs. If you planning to take up a data science certification course, you should also prepare yourself for the interview questions.
It is not just the theoretical knowledge but your practical experience along with how well you are able to give answers will matter in getting that dream job. Here is a list of the most commonly asked top data science interview questions and answers in 2022:
How you will define the term Data Science?
Data Science is basically a field of study that combines mathematics and statistics, programming skills and advanced analytics, artificial intelligence, and machine learning to extract meaningful insights from the data. These insights thus collected are further used by businesses for problem-solving and decision-making.
What are the major differences between data science and data analytics?
Even though people use data science and business intelligence parallel to each other but there is a huge difference between them.
- Data Science is used in making algorithms, writing questions, and building statistical models whereas data analytics uses data to bring out meaningful insights and solve problems.
- Data Science uncovers new questions which help to derive innovation while data analytics makes use of existing information to reveal actionable data.
- Tools in data science include machine learning, python, java, software development, and Hadoop on the other hand, the tools in data analytics are data modeling, data mining, data analysis, and database management.
- The job of a data scientist is to identify the questions and then find the best way to get answers whereas data analytics receive questions first and then make use of data analysis to provide answers.
What is Deep Learning?
Deep Learning and Statistics are two important terms in data science. It makes us work closely with the human brain and what are the thoughts that are coming into the brain. Therefore the algorithms that are created are also similar to the human brain.
What do you understand by RNN (recurrent neural network)?
It is an algorithm that makes use of sequential data. RNN is basically used in voice recognition, language translation, and image capturing. The different types of RNN networks are one-to-one, many-to-many, many-to-one, and one-to-many.
What Python is used for Data Cleaning in DS?
A large amount of data needs to be converted into an effective one by data scientists. Data Cleaning mainly includes the removal of outliners, redundant formatting, and malware records.
Name the popular libraries that are used in Data Science
Some of the popular libraries used in Data Science are:
- Tensor Flow
How to become a data science engineer?
In order to become a data scientist, it is very important to:
- Get a data science degree. Employers are likely to offer you a job if they see some academic credentials.
- Sharpen and upgrade the skills needed by a data scientist.
- Opt for an entry-level data analytics job to start with.
- Prepare the questions and answers well for the interview.
If you are given a data set that consists of variables that are more than 30 percent missing values. How are you going to handle it?
The different ways to handle missing data values are:
If it is a large data set, simply remove rows of the missing data values. This is the quickest way to deal with it. And then make use of the rest of the data to predict values.
If it is a small data set, substitute missing values with the mean or average of the rest of the data by using the pandas’ data frame in python
Discuss in detail the feature selection methods which are used to select the right variables
The two main methods used for feature selection are filter and wrapper methods.
The Filter Method involves Chi-Square, Linear discrimination analysis, and ANOVA. The best analogy used for selecting features is “bad data in, bad answer out.
Wrapper Methods involve forward selection and backward selection. In forwarding selection, the features are tested one by one till a good fit is found. And in the backward selection, all the features are tested and then we start removing to see what works better.
Recursive Feature Elimination involves recursively looking through all different features and how they end up pairing together.
How is it possible to avoid overfitting your model?
The term Overfitting refers to a model which is just set for a very small amount of data. It totally ignores and takes away the bigger picture. The three main methods to avoid overfitting are:
- You should keep the model simple.
- Make use of cross-validation techniques like k folds cross-validation
- You can use regularization techniques like LASSO which penalize certain model parameters if they cause overfitting.
How is a random forest model built?
A random forest is built with the help of a number of decision trees. If the data is split into different packages and a decision tree is made in each of the different groups of data then the random forest brings all the trees together.
Steps for building a random forest model:
- First randomly the ‘k’ features from a total of ‘m’ features where k << m.
- Then among the ‘k’ features, calculate node D using the best split point
- Split the node into daughter nodes using the best split
- Repeat steps two and three until leaf nodes are finalized
- Build a forest by repeating steps one to four for ‘n’ times to create ‘n’ a number of trees
How to Differentiate between the three: univariate, bivariate, and multivariate analysis?
Univariate data consists of only one variable. By univariate analysis, you can describe the data and find patterns that exist within it.
Bivariate data includes two different variables. In the analysis of this type of data the causes, relationships, and the analysis is done to determine the relationship between the two variables.
Multivariate data comprises three or more variables. This data is categorized under multivariate. Quite similar to a bivariate but consists of more than one dependent variable.
Calculate the Euclidean distance in Python for the given five points.
plot1 = [1,3]
plot2 = [2,5]
The Euclidean distance can be calculated as follows:
euclidean_distance = sqrt( (plot1-plot2)**2 + (plot1-plot2)**2 )
Explain dimensionality reduction and also state its benefits
Dimensionality reduction deals with the process of converting a data set with vast dimensions into data with fewer dimensions to convey similar information concisely. It helps in compressing the data and also reduces the storage space. It removes redundant features; for example, there’s no point in storing a value in two different units (meters and inches).
Explain the steps in making a decision tree
- Take the entire data set as input
- Calculate the entropy of the target variable, as well as the predictor attributes
- Calculate your information gain of all attributes (we gain information on sorting different objects from each other)
- Choose the attribute with the highest information gain as the root node
- Repeat the same procedure on every branch until the decision node of each branch is finalized
For example, let’s say you want to build a decision tree to decide whether you should accept or decline a job offer. The decision tree for this case is as shown:
Explain the steps to maintain a deployed model
The steps to maintain a deployed model are:
Monitor: With constant monitoring of all models, it is needed to determine their performance accuracy. And when changing something, you also want to figure out how your changes are going to affect various things. This needs continuous monitoring to ensure it’s doing what it’s supposed to do.
Evaluate: The evaluation metrics of the current model are calculated to determine if a new algorithm is needed.
Compare: All the new models are compared to each other for determining which model performs the best.
In rebuilding, the best performing model is rebuilt on the current state of data.
What do you understand about recommender systems?
A recommender system is used to predict what a user will be rating a particular product based on their preferences. It can be split into two different areas:
It is commonly seen on Amazon where once you make a purchase; customers may get a message accompanied by product recommendations: “Users who bought this also bought…
Pandora uses the properties of a song to recommend music with similar properties.
How to write a basic SQL query that lists all orders with customer information?
Generally, there are order tables and customer tables which contain the following columns:
- Order Table
- total amount
- Customer Table
- The SQL query is:
- SELECT OrderNumber, TotalAmount, FirstName, LastName, City, Country
- FROM Order
- JOIN Customer
- ON Order.CustomerId = Customer.Id
You conduct a study on the behavior of a population, and now you have identified four specific individual types which are valuable to your study. Now you want to find all users similar to each individual type. Which algorithm is most appropriate for this study?
Choose the correct option:
- K-means clustering
- Linear regression
- Association rules
- Decision trees
Since we are looking at grouping people together by four different similarities, it indicates the value of k. Therefore, K-means clustering (answer A) is the most appropriate algorithm for this study.
What do you understand by the Confusion Matrix?
A confusion Matrix refers to a summary of the prediction results of a particular problem. It is an n*n matrix that evaluates the performance of the classification model.
What do you understand by true-positive rate and false-positive rate?
The true-positive rate gives the proportion of correct predictions of the positive class while the false-positive rate gives the proportion of incorrect predictions of the positive class.
How is Data Science different from traditional application programming?
The most important difference between Data Science and traditional application programming is that in traditional programming, you need to create rules to translate the input to output. And in Data Science, the rules are automatically produced from the data.
What is logistic regression?
Logistic regression is also known as the logit model. It is a technique used to forecast the binary outcome from a linear combination of predictor variables.
What is linear regression machine learning?
It is a machine learning algorithm that is based on supervised learning. It performs regression tasks. The regression models target prediction values based on independent variables. Mostly used for finding out the relationship between variables and forecasting.
What is data science boot camp?
It is a program that covers critical Data Science topics like Python programming, R programming, Machine Learning, Deep Learning, and Data Visualization tools through an interactive learning model with live sessions by global practitioners and practical labs.
Data Science is not at all hard to learn. This is the misconception that many beginners have when they begin the course but gradually and with practice students realize that data science like any other field becomes easy when you start working hard. The online data science course combines both soft skills(business skills) and hard skills (Python, SQL). A data scientist should have the ability to solve problems of great complexity. They should be well-versed in how to apply their theoretical knowledge in real-life situations.
To decide whether you should opt for a data science course online in India or not, you should have a face-to-face session with a counselor or an industry expert who can clear all your doubts and provide answers to the questions that have in mind.
There are different data science courses online free which students can take to get basic knowledge about data science. These courses are equally good and can equip you with the latest skills that a data scientist needs. But for an in-depth study and for certification that will help you in getting a job it is advisable to enroll in an online data science course that provides certification along with practical knowledge and a chance to clear your doubts under expert supervision and guidance. A data science course online is what many students prefer due to the flexibility to learn and also leaving them with time to do something else.
In terms of growth, good salary, and bright future prospects, Data Science has been regarded as the best career option in India. In the coming years, everything is bound to become data-oriented. And there is a huge shortage of profit in this field. The sectors like healthcare and IT are booming but all these sectors rely on data information. It will help in making better decisions based on data, the consumer preferences will be revealed and further help promote products to the correct audience.
A student interested in data science should always choose a domain-oriented course that will give him industry expertise. The field of data science is gaining a lot of momentum and growing day by day. It will keep getting bigger and bigger as more and more companies are going to move toward this approach. India is no different when it comes to career opportunities in data science as both global and local players are coming up here too. Since the margins are very high, these companies make a lot of money and hence give plenty of money to data scientists.
Data science is the hottest buzzword in the technology world. Almost everyone is trying to get associated with this field in some way or the other. Beginners are looking for courses that can pursue and make their future bright and secure while professionals are trying to up-skill their skill set and move into the world of data science.
Companies are ready to hire freshers as data analysts but most of them are entry-level jobs. If you wish to make it big in the field of data science it is crucial to gain that certification and then move ahead in this field. This is just that time when you shouldn’t be wasting time thinking about when to start and how to start, just gather all the information on your desired course and begin with the classes. You will get all the more success after a certification course than beginning without a certification. It can revolutionize your career if you take the right steps and one day you will become an expert in this field.
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