Contact Us
×

Tag: Data Science interview questions

15 Most frequently asked Data Science interview questions!

Posted on by Imarticus Learning

According to the recent Glassdoor report on the 50 best jobs in America, data science jobs are still the most opted-for job choice in the IT sector. This report studies factors such as job satisfaction, salary, and the total number of jobs available.

Performing well in all sectors, data science jobs have scored an overall rating of 4.8 out of 5. With a huge gap between demand and supply of qualified individuals, this profession is expected to grow bigger. If you wish to develop a successful data science and analytics career, consider enrolling in the data science course by Imarticus Learning, called the Postgraduate Program in Data Science and Analytics, designed to foster the skills required for the modern data scientist.

In this era of Machine Learning and Big Data, data scientists are the stars. If you are looking to be a part of this, the following are some of the data science interview questions you might face while applying for jobs to display your technical proficiency. Brief answers are also provided to help you recall.

Data Science Interview Questions

What is Data Science ?

Data science is a field that involves collecting, analyzing, and understanding data to find useful information and patterns. It combines skills from math, computer science, and specific areas of knowledge to help make better decisions based on data.

Differentiate between Data Analytics and Data Science

While both the data science and data analytics fields are about working with data to gain insights, data science usually involves using data to build models that can predict future outcomes, whereas data analytics typically focuses on analyzing past data to inform present decisions.

What is root cause analysis?

This is a problem-solving technique used for isolating the root causes of a problem.

What is meant by Logistic Regression?

Also known as the Logit Model, it is a technique to predict the binary outcome from a linear combination of predictor variables.

What are the recommender systems?

They are a subclass of filtering systems that predict customer ratings of a product.

What is Collaborative Filtering?

It is a widely used filtering system to find patterns through collaborating perspectives, several agents, and multiple data sources.

Why do we do A/B Testing?

A/B testing detects any change to a web page and increases or maximises the strategic outcome.

What is the Law of Large Numbers?

It states that sample variance, standard deviation and the sample mean converges to the intended estimate. This theorem provides the basis for frequency style thinking.

What is Star Schema?

It is a database schema where data is organised into dimensions and facts. A sale or login marks a fact. The dimension means reference information about this fact, such as product, date, or customer.

Define Eigenvalue and Eigenvector

Eigenvalue denotes the direction at which a linear transformation acts by compressing, flipping, or stretching. Eigenvectors are used to understand the linear transformation. The correlation or covariance matrix can be found using eigenvectors.

What are the common biases during the sampling?

Under coverage bias
Selection bias
Survivorship bias

What is selective bias?

The problematic situations created by non-random samples are generally called selection bias.

What is Survivorship Biasing?

This is a logical error caused by overlooking some aspects due to their lack of prominence. It leads to wrong conclusions.

Define Confounding Variables

They are variables in a statistical model that correlate with both independent and dependent variables.

What are Feature Vectors?

It is an n-dimensional vector containing numerical features of an object. It makes an object easy to be analysed mathematically.

What is Cross-validation?

It is a popular model validation technique used to evaluate how the output of a statistical analysis will generalise to an independent data set.

Gradient descent methods always converge to a similar point, true or false?

False. In some cases, they approach local optima or local minima point. The data and starting conditions dictate whether you reach the global point.

Preparing for important data science interview questions is essential for landing your dream job. By familiarizing yourself with what is data science all about and common data science topics, you can showcase your technical proficiency. Ultimately, effective interview prep improves your confidence and helps you present yourself as a qualified data science candidate.

Optimization In Data Science Using Multiprocessing and Multithreading!

Posted on by Imarticus Learning

Every day there is a large chunk of data produced, transferred, stored, and processed. Data science programmers have to work on a huge amount of data sets.

This comes as a challenge for professionals in the data science career. To deal with this, these programmers need algorithm speed-enhancing techniques. There are various ways to increase the speed of the algorithm. Parallelization is one such technique that distributes the data across different CPUs to ease the burden and boost the speed.

Python optimizes this whole process through its two built-in libraries. These are known as Multiprocessing and Multithreading.

Multiprocessing – Multiprocessing, as the name suggests, is a system that has more than two processors. These CPUs help increase computational speed. Each of these CPUs is separate and works in parallel, meaning they do not share resources and memories.

Multithreading – The multithreading technique is made up of threads. These threads are multiple code segments of a single process. These threads run in sequence with context to the process. In multithreading, the memory is shared between the different CPU cores.

Key differences between Multiprocessing and Multithreading

  1. Multiprocessing is about using multiple processors while multithreading is about using multiple code segments to solve the problem.
  2. Multiprocessing increases the computational speed of the system while multithreading produces computing threads.
  3. Multiprocessing is slow and specific to available resources while multithreading makes the uses the resources and time economically.
  4. Multiprocessing makes the system reliable while multithreading runs thread parallelly.
  5. Multiprocessing depends on the pickling objects to send to other processes, while multithreading does not use the pickling technique.

Advantages of Multiprocessing

  1. It gets a large amount of work done in less time.
  2. It uses the power of multiple CPU cores.
  3. It helps remove GIL limitations.
  4. Its code is pretty direct and clear.
  5. It saves money compared to a single processor system.
  6. It produces high-speed results while processing a huge volume of data.
  7. It avoids synchronization when memory is not shared.

Advantages of Multithreading

  1. It provides easy access to the memory state of a different context.
  2. Its threads share the same address.
  3. It has a low cost of communication.
  4. It helps make responsive UIs.
  5. It is faster than multiprocessing for task initiating and switching.
  6. It takes less time to create another thread in the same process.
  7. Its threads have low memory footprints and are lightweight.

Optimization in Data Science

Using the Python program with a traditional approach can consume a lot of time to solve a problem. Multiprocessing and multithreading techniques optimize the process by reducing the training time of big data sets. In a data science course, you can do a practical experiment with the normal approach as well as with the multiprocessing and multithreading approach.

Data Science Courses with placement in IndiaThe difference between these techniques can be calculated by running a simple task on Python. For instance, if a task takes 18.01 secs using the traditional approach in Python, the computational time reduces to 10.04 secs using the pool technique. The multithreading process can reduce the time taken to mere 0.013 secs. Both multiprocessing and multithreading have great computational speed.

The parallelism techniques have a lot of benefits as they address the problems efficiently within very little time. This makes them way more important than the usual traditional solutions. The trend of multiprocessing and multithreading is rising. And keeping in mind the advantages they come up with, it looks like they will continue to remain popular in the data science field for a long time.

Related Article:

https://imarticus.org/what-is-the-difference-between-data-science-and-data-analytics-blog/

Interesting Puzzles To Prepare For Data Science Interviews !

Posted on by Imarticus Learning

A Data science career is a lucrative opportunity with many young professionals opting for it. With the easy accessibility to data science courses, the number of professionals pursuing it is rising. There is a huge demand for expertise in this area and it has been voted as the best career by Glassdoor in the United States.

Though there is a need for professionals in this field, it is often not easy to get into. Organizations look for problem-solving and analytical skills in their potential employees and judge them based on creative and logical reasoning ability.

Having a different approach towards a problem and solving it in a unique way can help one stand out from the crowd. It isn’t a cakewalk to master these abilities. One has to practice and try to improve their skills. Solving puzzles is a way to test the individual’s ability to think out of the ordinary and also puts to test problem-solving skills.

The interviewers while hiring fresher especially give them puzzles to solve during their interviews. Due to the pandemic, many companies now have a stricter policy when it comes to choosing the right candidate for the job. It is challenging and the chances of selection are less compared to earlier.

Data Science Career Interview

Some are even assessing the candidates based on their coding skills. To provide an insight into what is in store for the candidates, below mentioned are some of the commonly asked puzzles during a data science job interview.

  1. There are 4 boys A, B, C, and D who are supposed to cross a rope bridge. It is very dark and they have just one flashlight. It is difficult to cross the bridge without the flashlight and the rope bridge can only stand 2 people at once. The 4 boys take 1, 2, 5, and 8 minutes each. What is the minimum time required for the four boys to cross the rope bridge? 

Sol:

This is a question that is most repeated and has an easy solution. A and B are the fastest boys and can cross the rope bridge first. They take 2 minutes. B stands on one side and A returns with the flashlight in 1 minute. So the total time taken is 3 minutes. After that, C and D have to cross the rope bridge. They have taken 5 and 8 minutes each. The total time taken is 8 minutes.

When we add the time taken by all, it is 3+8 which equals 11 minutes. C and D stand on the other side and B takes 2 minutes to return. Hence the total time that is taken by all is 11+2 which equals 13 minutes. At last, A and B will cross the rope bridge and will take 2 minutes and that adds the total time to 13+2 which is 15 minutes. So the time required by all the 4 to cross is 15 minutes.

  1. A person is in a room with the lights turned off. There is a table. A total of 50 coins have been kept on the table. Out of the 50, 10 coins are in the head position while the other 40 are in the tails position. The person has to segregate the coins into 2 different sets in a way that both sets have equal numbers of coins that are in the tails position.

Sol:

Segregate the coins into two groups, one with 10 coins and the other with 40 coins. Turnover the coins of the group that has 10 coins

  1. A bike has 2 tyres and a spare one. Each tyre can only cover a distance of 5 kilometers. What is the maximum distance the scooter will complete? 

Sol: 

To simplify the problem, we will name the tyres X, Y and Z respectively. 

X runs 5 kms

Runs 5 kms

Z runs 5 kms

Initially, the bike can cover a distance of 2.5 kms with tyres X and Y

X=2.5 kms, Y=2.5 km, and Z=5 kms

Take off tyre X and ride the bike with YZ another 2.5 kms

Remaining X= 2.5, Y=0 and Z=2.5

Take off tyre Y and ride the bike with XZ another 2.5 kms

Remaining X=0, Y=0 and Z=0.

Hence, the total distance covered by the bike is 2.5+2.5+2.5 = 7.5 kms

The more an individual practices such puzzles, the better the chances of landing a data science job.

Related Articles:

Analytics & Data Science Jobs in India 2022 — By AIM & Imarticus Learning

The Rise Of Data Science In India: Jobs, Salary & Career Paths In 2022