Data science is a growing field in the tech world and has become a career path for many individuals. This article will guide you through the various processes that are involved in data science, how to find data scientists, how to select them, and how to train them.
Data science is a subfield of computer science that applies statistical, mathematical, and scientific methods to big data. It has become increasingly important in business, where it is used to gain insights from large amounts of unstructured information. In this article, we will discuss the different types of data science, how it is applied and what are the benefits of using them.
What is data science?
Data science is the field of computer science that uses statistical and mathematical methods to discover and analyze data. The goal of data science is to extract information from large amounts of data for the purpose of making predictions, improving decisions, or developing models.
It can be used for a variety of purposes such as helping you find products that will sell well in your store, predicting outcomes from different scenarios, making recommendations based on previous purchases, analyzing what content people are reading online, etc.
Common data science interview questions from students
Here are nine of the most frequently asked data science interview questions:
These are the questions I got when I interviewed for big companies (Yelp, Facebook, Square, Intel, eBay, etc)
- What is the difference between snowflake and star schema?
- When you type in a URL into your browser, what happens?
- Data structure questions (typically simple lists or array manipulation)
- complicated SQL questions that involve Joins and sub-queries
- How you would test certain features and create metrics for them
- What is A/B Testing?
- Basic statistic questions
Why do you want to work at this company as a data scientist?
How did your previous work experiences prepare you for a role as a data scientist?
How do you overcome any professional challenges?
What tools and devices do you plan to use in your role as a data scientist?
What is selection bias, and why do you need to avoid it?
How do you organize big sets of data?
Is having large amounts of data always preferable?
What is root cause analysis?
How do you usually identify outliers within a data set?
- Which programming languages and environments are you most comfortable working in?
- Do you contribute to any open-source projects?
- How would you clean a data set in (insert language here)?
- Tell me about the coding you did during your last project?
- Tell me about how you designed a model for a past employer or client.
- What are your favorite data visualization techniques?
- How would you effectively represent data with 5 dimensions?
- How is k-NN different from k-means clustering?
What questions can I ask for a Data Science intern job interview?
Thank you for the A2A.
Let me congratulate you on a non-vague interview questions question.
Let me start with what I wouldn’t expect. I wouldn’t expect the intern to know what is related to business. For instance, customer retention is a simple concept in theory that is very tricky in practice. Sales forecasting might be easy from a technical point of view but communication to stakeholders is something that needs experience. Others apply.
With this in mind what I expect is exposure to the technical aspects of getting your hands dirty in those areas. For instance, let’s assume you do clustering for market segmentation, focus on the clustering part, leave the market segmentation out, that is what you’re likely to help him with.
Focus on the technical ability to deliver… I guess that’s my point.
As for the questions, I avoid making questions that imply that they know or not. I prefer to have a dialog about the area I expect them to work on early on. For instance, instead of asking how he interprets the F-statistic of linear regression, I discuss a linear regression problem with him, from start to finish. The good part of this approach is that you are not rejecting people on the basis of a very specific question. If you reject them it is because you are not happy with their thought process and the technical know-how is, at least, adequate.
Let me give you an example, I had a position open for a junior data analyst. Most of the job description was about creating dashboards and doing simple descriptive analysis. I simply discussed some visualizations with the candidates that did a nice test task. Why did they choose a line graph over a bar chart? Why did they choose those colors? Things like that. I already knew they handled the technical part because the test task asked for the code. I just wanted to hear them think.
This is a very simple example where I was able to understand what were they thinking when they made the choices they did.
So bullet points:
- Tech-focused, don’t expect internship candidates to have the business sensitivity or understanding.
- Discussion over questions, you want smart people that think.
- Focus on what they will do not what they know.
How do I prepare for a data scientist interview?
Interviews are one of the most stressful aspects of any job, and Data Science interviews are no exception. In anything, they’re more daunting because the corporations require highly qualified employees. Data scientists are in high demand, and people want to be one of them. To prepare for the interview, people look for various materials on the internet. In most cases, however, one does not come up with the necessary information. As a result, you’ll need some guidance in order to prepare for a data scientist interview. Data science interviews necessitate a great deal of planning. So, if you’re serious about preparing for a Data Scientist interview, I recommend enrolling in Learnbay’s Data Science course and practicing for the interview.
The major concern is how to prepare for an interview with a data scientist:
As a result, I would advise you to consider the following points:
Basics of statistics
This section contains some beginner-level questions. As a result, be ready with descriptive and inferential statistics.
Mathematics behind Data Science
The interviewer does not expect you to understand all of the mathematics underlying all machine learning methods. However, you should be familiar with the fundamental math (or workings) of machine learning algorithms that will be employed in your projects.
Prepare yourself for coding and logical (puzzle) questions.
Students with a non-CS background should prepare for coding questions. I’m not sure what degree of coding questions non-CS students should prepare for. However, if you have a CS background and are familiar with the fundamentals of programming, data structures, and algorithms, no more preparation is required.
If you want to understand more and study for an interview, you should definitely join Learnbay.
Learnbay is a job assistance program that includes mock interviews and helps in resume writing. They offer IBM-certified courses that cover all of the necessary abilities and include the best course curriculum. And their blended program allows you to gain a practical understanding of how industries work on various projects, which will benefit you when you go to work in the real world. This program is led by accomplished mentors who are working professionals in the field of data science.
So, before you start preparing for the interview, make sure you understand some of the topics Learnbay will teach you:
- Putting together a solid résumé:Outline all of your qualifications and experiences, with a focus on Python, SQL, predictive analytics, machine learning, and some other relevant technologies.
- Making an application for proper employment: You may not have all of the skills, but you should be able to match at least half of them.
- How to Make an Impression in the First (Initial) Interview: Because most interviews are conducted over the phone or via video call due to COVID, conduct a quality check of all technicalities beforehand.
- Refresh your knowledge.
- Also, look for frequently asked questions on the internet.
- Some technical issues were re-examined.
Courses available include:
Data Science & AI Certification | Domain Specialisation For Professionals- This course is for working professionals with at least one year of experience.
The course will take 7.5 months to complete.
Data Science & AI Certification Program For Managers and Leaders- This is a one-of-a-kind course for working professionals with more than 8 years of experience as a manager, team leader, or in other high-profile positions.
The duration of the project is 11 months.
Data Science & Business Analytics Program | Fast Track Course- This 4-month course is meant for those who have had a professional hiatus of more than six months.
Other features provided by Learnbay include:
- Specialized doubt classes are available.
- Non-programmers will benefit from this advice.
- IBM Certified courses are available.
- A session for the project
- Sessions range from 200 to 300 hours.
- Classes are held on weekdays and weekends.
- Online classes that are live and interactive.
- This is a full-stack program.
- This is a blended curriculum in which you will participate in classroom-based project sessions.
Another useful Learnbay feature is a domain-specific elective module. Aspirants can choose from the following choices to gain elective industry-specific data science skills and complete their capstone projects:
- HR, advertising, and sales
It uses a bottom-up method to explain trending and future-proof applications of analytics in various marketing and sales processes, making it easy to understand for non-mathematicians and everyday statistical data analysis experts.
- Energy and gas
Candidates will study the role of data science in the energy sector, including power outage detection and prediction, power failure prediction, dynamic energy management, and more.
- Clinical research and medical management
The information in these categories can assist the government in a variety of ways. Medical imaging is one of the most effective uses of Data Science in healthcare. With the help of Data Science and Machine Learning, computers may learn to read MRIs, X-rays, mammograms, and other medical information.
- Transportation and hospitality
The methods for analysis are covered in the courses on elective media, hospitality, and transportation, with an emphasis on actual applications and implementations for corporate growth.
Banks are growing in size, yet client loyalty is low, implying that consumers expect greater operational efficiency. Banks seek to learn more about their customers in order to retain them on their books. The analytics team is concentrating on data patterns in order to better communicate with customers and understand transactional data.
I would strongly advise you to use Learnbay for your educational endeavors. Learnbay provides you with high-quality, industry-based training that is IBM-certified. Its goal is to assist learners in becoming experts in their own right through strategic learning and step-by-step learning.
The course framework covers all concepts, whether they are fundamental or advanced. The most astounding element is that they not only focus on academic learning but also place equal emphasis on practical learning, as seen by the fact that they provide 15+ real-time projects as well as job support.
I hope I was of good service to you. Thank you for reading, and please continue to read my responses!
How to Find Data Science Course in Top Cities
Data Science is the most in-demand job in the IT industry and is a profession that provides an opportunity to make money by solving complex problems. In this post, I am going to answer your question on how you can get into the Data Science course in top cities like Chicago, London, San Francisco, and other big cities around the world.
I would suggest that you look for online courses which provide practical training from experts who have been working with companies like Google, Microsoft, Amazon and many more. These are organizations that need skilled data scientists to solve their business problems. If you can’t find any courses available for your city then it’s time to start looking at different options such as MOOCs (Massive Open Online Courses) or Private Tutoring where students learn online under one instructor who has taught thousands of students worldwide.
Once you have found a suitable course then enroll in it and start learning everything about Data Science including; Python programming language, R programming language, statistics, machine learning, etc… Try joining forums where people discuss topics related to Data Science so that you don’t miss anything out while learning the subject. Keep yourself updated with all important news on Data Science because it’s constantly changing every day and there will be new trends emerging every day so keep up with it!