How to Crack the Data Science Interview at Google
Google is a dream company for numerous aspiring data scientists. Many people, in fact, begin their education in data science with the hope of getting into an esteemed tech giant like Google. If you’re one of those tech geeks who wish to get into Google, this blog is for you.
A data scientist’s role mainly consists of devising data analysis methods; compiling data for analysis; examining, analysing, and visualising data; creating data models using programming languages like Python and R; and integrating these models into apps.
A data scientist is not a lone wolf. In actuality, data science is efficient when done in teams. A business analyst defines the challenge, and a data engineer develops the data and how it is used. An IT architect supervises the foundational processes and infrastructure. Further, an application developer deploys the outputs or models of the analysis into applications and products.
These are the fundamental tasks that you have to undertake as a data scientist. If you are fortunate enough to land an interview with Google, a large segment of your questions would be based on these tasks. Hence, you should be prepared enough so that you don’t trip during the interview. After all, you don’t want to ruin your chances of getting selected at Google just because you haven’t prepared enough for the interview. In today’s blog, we are going to break down multiple interview questions along with appropriate answers to each of them.
At a Glance: The role of a data scientist
A data scientist is a specialist who collects and evaluates huge volumes of structured and unstructured information. As a result, they’re also known as data wranglers. All data scientists combine numerous mathematical and statistical tools in their work. They evaluate the data after analysing, processing, and modelling it in order to develop meaningful plans for the organisation.
Because they use their technical talents to detect trends in data, data scientists are also analytical experts. They must collaborate closely with corporate stakeholders to comprehend their objectives and establish how they may be met. They develop data modelling techniques, algorithms, and prediction modes for retrieving the information that the company requires.
The diverse professions in data science
There are a number of professions for you to choose from in data science. Some of the major ones are
Data Analysts
Data analysts are accountable for a wide range of duties, including data visualisation, managing, and processing. They must also run queries against databases from cycle to cycle. Optimization is one of the data analyst’s most prominent skills. This is due to the fact that they must develop and tweak algorithms that can be utilised to extract data from some of the world’s largest databases without causing data corruption. SQL, R, SAS, and Python are among the most popular data analysis tools. As a result, accreditation in these areas can substantially increase your job offers. You should also be able to solve problems effectively.
Key responsibilities:
- Using automated techniques to extract data from primary and secondary resources
- Using database development and maintenance
- Analyzing data and producing reports with recommendations
- Data analysis and trend forecasts that affect the organization/project
- Collaborating with other members of the team to improve data gathering and quality assurance procedures
Database Administrator
Data engineers build and test resilient big data environments for businesses, allowing data scientists to run their algorithms on robust and well-optimized data platforms. They’re also in charge of database backups and restores. A database administrator should be proficient in database recovery, data security, data modelling and other vital abilities. It’s a big plus if you’re excellent at crisis management.
Key responsibilities:
- Handling database software for data storage and management
- Working on database development and design
- Putting in place database security measures
- Preparation of reports, documentation, and operational instructions
- Archiving of data
- Collaboration between programmers, project supervisors, and other members of the team
Data Engineer
Data engineers create and test resilient Big Data environments for organisations so that data scientists may run their algorithms on robust and well-optimized data platforms. To boost database performance, data engineers also update existing infrastructures with updated or improved versions of current technologies. Hive, NoSQL, R, Ruby, Java, C++, and Matlab are all technologies that demand hands-on knowledge. These are the main things you must learn if you want to work as a data engineer. For instance, working with prominent data APIs and ETL tools can also be advantageous.
Key Responsibilities:
- Create and keep data management systems up to date
- Collection, acquisition, and administration of data
- Finding underlying patterns and anticipating trends
- Collaboration with relevant teams to gain a better understanding of the organization’s goals
- Creating reports on the basis of analytics and providing updates to stakeholders
Data Architect
A data architect builds data management plans so that databases may be readily integrated, centralised, and secured with the greatest security methods possible. They also ensure that data engineers have the necessary updated tools with which the operations are carried out. Proficiency in data warehousing, data modelling, extraction, transformation, and other areas is required for a job in data architecture. You should also be familiar with Hive, Pig, and Spark, among other things.
A Data Architect’s Important Roles and Responsibilities Include:
- Developing and implementing a comprehensive data strategy that is in line with business objectives
- Identification of data collection sources in accordance with the data strategy
- Collaboration with cross-functional departments and stakeholders to ensure that database systems run smoothly
- End-to-end data architecture planning and management
- Maintaining database systems and architecture in a way that is both efficient and secure
- Auditing the performance of data management systems on a regular basis and making modifications to improve the systems as needed
Data Scientist
Data scientists must comprehend business difficulties and provide top quality solutions through data analysis and processing. For example, they are expected to undertake predictive analysis and go over “structured or unstructured” data with sharp monitoring in order to provide valuable insights. They can also do so by spotting emerging trends that might aid businesses in making better judgments. You must be competent in R, MatLab, SQL, Python, or other associated technologies in order to work as a data scientist. If you have a graduate degree in mathematics, computer engineering, or a related field, it can also help.
Apart from this, pursuing a data science course can provide individuals with the necessary skills and knowledge to excel in these areas. Additionally, having a graduate degree in mathematics, computer engineering, or a related field can further bolster one’s qualifications and open doors to exciting opportunities in the field of data science.
Key Responsibilities:
- Identifying sources of data collecting for business needs
- Data cleansing, processing, and integration
- Undertaking the process of data collecting and management automation
- Improving processes with Data Science methodologies and technologies
- Analysis of large volumes of data in order to forecast trends and produce reports with recommendations
- Collaboration with the solution, development, and business teams
Business Analyst
Business analysts have a slightly distinct role from other data scientists. They understand how data-oriented technology solutions function and how to handle massive volumes of data. They also know how to distinguish high-value data from low-value data. In other words, they discover how Big Data can be linked to business insights that contribute to the company’s growth. Business analysts bridge the gap between data engineers and executives in charge of management. As a result, they should be familiar with corporate finances and business intelligence, and IT technologies such as data modelling and data visualisation tools.
Key responsibilities:
- Understanding the organization’s business
- Conducting a thorough company analysis — identifying issues, opportunities, and potential solutions
- Implementing efforts to improve current business procedures
- Implementation of new technology and systems
- Forecasting and budgeting
- Analysis of Costs
Machine Learning Engineer
Engineers who specialise in machine learning are in considerable demand right now. However, the work profile has its own challenges. Machine learning engineers are expected to do A/B testing, design data pipelines, and execute standard machine learning algorithms like classification, clustering, and others. In addition to this, they should have an in-depth understanding of some of the technologies like SQL, REST APIs, and so on. To commence a career as a machine learning engineer, you must have a thorough understanding of some of the technologies, such as Java, Python, and JS. Second, you must have a solid understanding of statistics and mathematics. It’s way simpler to ace a job interview once you’ve mastered both.
Key Responsibilities:
- Machine Learning system design and development
- Studying Algorithms for Machine Learning
- Putting Machine Learning systems to the test
- Creating apps/products based on the needs of the client
- Existing Machine Learning frameworks and libraries are being extended.
- Data exploration and visualisation for greater understanding
- Optimizing systems for training and retraining
Click here to discover the different pay scales for the various roles in data science. You’ll get exclusive insights into each role in Google on the basis of experience, skill sets, location and other important factors.
The type of DS interview questions you should be prepared for
The interview would consist of a number of questions from various spheres. This is to gauge your overall capabilities and expertise in the respective spheres. Some of them are
Behavioural Questions
These questions are designed to understand how you would react in various professional scenarios and how you tackle difficulties to get a positive conclusion.
The main thing the interviewers will ask you is a question that will allow you to demonstrate how you interpreted a problem and how you solved it. These questions are designed to determine whether you are the ideal fit for the organization’s data science team.
Some common behavioural questions that may be asked during a data science interview are:
- How have you leveraged data insights to convince someone to change their mind?
- Have you ever faced a problem when working on a data science project with a group?
- Give an instance of a team conflict to illustrate your point.
- Describe a controversial decision you made.
- Give some examples of how you collaborated with others.
- What data have you used to improve the customer experience?
Technical Questions
The interviewer is attempting to assess your technical expertise in both the theory and implementation types of questions. As a result, the interviewer’s questions usually fall into one of two categories: Theory and Implementation
- Theory
To preemptively deal with theory questions, you can provide a couple of personal projects on your resume. It can include two to three comprehensive projects about a data science concept you’ve completed in the past. You should also be prepared for answering questions such as:
- What made you choose this particular model?
- What assumptions do you need to validate to properly use this model?
- What are the disadvantages of that model?
- Implementation
If you can answer these questions correctly, you are essentially demonstrating to the recruiter that you are familiar with both theory and model implementation in the project. It can be a curricular project, a personal project, or any other project you’ve worked on recently. So, here are some modelling techniques you would need to know:
- Random Forest
- Regression
- K-Nearest Neighbour
- Gradient Boosting
- Going the extra mile
These are the most basic models that each and every data scientist should be familiar with and have experience applying. So, the easiest method to demonstrate your knowledge is to talk about your projects and show the interviewers that you’ve gotten your hands dirty and put these models into practice. Furthermore, if you want to be a great data scientist, you must clean the data, design a data pipeline, evaluate the results, and convey the results to the stakeholders, in addition to simply applying the models.
So, if you can show the interviewer that you understand the entire data science procedure from beginning to end, from obtaining data to explaining the results to stakeholders, and that you can explain why you did each step in detail, the interviewer will be pleased that you can successfully complete data science projects.
Coding Questions
What are the interview questions for data science coding? These are the questions that must be answered by coding in any programming language. If you’re seeking a data science job, you’ll need to pass the coding interview.
What is the purpose of coding questions?
- Data science is a technological subject in which you must collect, clean, and transform data into usable formats, as you are aware. As a result, the coding questions assess not only your technical abilities but also your reasoning and approach to breaking down complex problems into simpler answers. As a result, key coding ideas must be prepared in order to excel in the data science interview.
- These questions also assess whether you tackle real-world situations in a rational manner. True, there are several solutions to a single issue, but the goal is to select the one that is the most efficient in terms of execution time and storage. As a result, you must be able to find the best solution to any real problem.
- The interviewer will also assess the general quality of your code by determining whether you have considered all edge situations in your solution.
You can practise a variety of problem statements from LeetCode and GlassDoor. Don’t be put off by the types of questions that may appear intimidating at first. You’ll need some time to prepare them, but you’ll also need a strong understanding of basic programming principles and machine learning algorithms to do so.
Why is data science becoming a popular go-to career option?
There are many reasons why data science is popular. Some of the chief ones include:
The data science landscape is continuously changing
Career fields with limited room for advancement face the threat of becoming stagnant. This implies that in order for opportunities to establish and grow in the market, the various disciplines must constantly evolve and change. Data science is a vast professional path that is always evolving, promising a plethora of opportunities in the future. Job tasks in data science are expected to be more niche, resulting in specialities in the discipline. People who are interested in this domain can benefit from these possibilities and pursue what suits them based on these parameters and specialities.
The inability of businesses to handle data
Businesses gather data on a daily basis for online operations and transactions. Many businesses have the fundamental problem of analysing and categorising the data they collect and store. In a circumstance like this, a data scientist becomes the saviour. Businesses can make remarkable progress if data is maintained appropriately and efficiently, leading to increased production.
Acceleration of data growth at an unprecedented rate
Almost every person generates data on a daily basis, intentionally or unintentionally. As time passes, the volume of data we interact with on a daily basis will only surge. Furthermore, the volume of data available on the planet will grow at breakneck speed. As data creation soars, data scientists will have a great demand to help businesses effectively use and maintain it.
Revision of the Data Privacy Regulations
In May 2018, the European Union nations gave approval to the General Data Protection Regulation (GDPR). This will develop a synergistic relationship between businesses and data scientists to meet the needs for ample and accountable data storage. People are becoming more familiar with data breaches and their negative impact. They are becoming safer and more cautious about sharing data with businesses and handing control to them. Hence, companies cannot afford to be irresponsible with their data anymore. Soon, the GDPR will guarantee some kind of data privacy.
Data science is used to update the blockchain
Blockchain is the most widely used technology for complying with cryptocurrencies such as Bitcoin. In this regard, data security will perform as expected, as precise transactions will be protected and recorded. If big data succeeds, the IoT will follow suit and expand in popularity. Edge computing will be in charge of dealing with and resolving data concerns.
Virtual Reality will be more user-friendly
We can see and are seeing how Artificial Intelligence is expanding over the world and how companies are relying on it in today’s environment. With advanced concepts such as Deep Learning and neural networking, Big Data’s chances will blossom even more. Machine learning is being deployed and implemented in nearly every application at the moment. Augmented Reality (AR) and Virtual Reality (VR) are also witnessing major transmutations. Furthermore, human-machine interaction and dependency are anticipated to improve and grow significantly.
The Bottom Line
A data science interview is similar to a typical interview in many ways. It should come as no surprise that data scientists are becoming celebrities in the new age of big data and machine learning. Companies that can use vast volumes of data to optimise the way they serve consumers, manufacture products, and carry out their operations will do well in this economy.
And, if you’re pursuing a career as a data scientist, you’ll need to be ready to wow potential recruiters with your knowledge. To do so, you’ll need to be able to ace your forthcoming data science interview in one sitting. We hope that this blog has made a notable contribution to your interview preparation.
