Data Science Interview Topics

Data Science Interview Topics

According to the latest studies conducted by Harvard and Bloomberg, the Data Scientist remains to be the sexiest profession. The question that arises is what exactly makes this profession an attractive position. Every business has some fish to fry in this stream, it is because of educated and precise decision-making to be executed in their marketing strategy. The interviewers generally look for a candidate who has outstanding technical skills with adequate management knowledge.

Data Science is more of a lifestyle than a profession. The person over time has to develop key qualities required to excel in this career. This article will be dealing with the technical aspect of the interview question. For soft skills please refer to another topic with Data Science Soft Skills. Data science works on the principles of Machine Learning which can be broadly characterized into two categories- Supervised Learning and Unsupervised Learning.

Supervised and Unsupervised Learning

Data science deals with algorithms that synthesize data and based on the scope of work give the desired outcome. Supervised and Unsupervised as the name suggests is a kind of learning that helps algorithms to make wise analyses. None of them is superior to one another. Supervised learning is used for the prediction of outcomes whereas unsupervised learning is used for the analysis. Supervised learning deals with the structured and labeled data but unsupervised learning is used only with unlabeled data.

Real-Life Examples of Supervised and Unsupervised Learning

Supervised Learning is like learning with the help of a teacher. The two most commonly used case scenarios for Supervised Learning are - Classification and Regression.

  • Classification - The algorithms will be treated with likely scenarios and identify if the presented case is similar or not. Examples -
    • The algorithm was first presented with a bunch of compounds some of them are drugs while the rest are basic medicine. With each compound, Algorithm was given information about the molecule. After cumulative training, the algorithm is capable to differentiate between the drug compound and the normal compound.
    • The second used case scenario is for spam filters used in mailboxes. The user identifies on the basis of the email whether the email is spam or important and gives information to the system in the forms of action taken or label given. The system on observing enough usage patterns becomes capable of identifying the spam mail and important mail and automatically moves spam mail to spam Folder
  • Regression - In this method of Supervised Learning, the machine is trained to predict the values like- time, price, weight, and height. Example -
    • The tool linear Regression is used for predicting house prices. In this process, various characteristics are termed labels. These labels are the differentiator of qualities offered by the house. The best tools to be used for regression are Python and Jupyter. In order to begin, a minimum of 1400 labels to be provided as a differentiator and also the flow of transactions over the past years. All these data are grouped together and on successful regression, the price can be easily predicted. The same model is used to predict the journey time that is currently being used in google maps etc.

Unsupervised learning is like learning without a teacher. When the learning is done without supervision, multiple faces of outcomes appear which helps in deepening the concepts and increases the curiosity. Similarly, Unsupervised learning in Machine Learning is a division where data sets are provided but without any labels. Therefore machine on itself with identify similar patterns and surface some key findings.

  • Clustering - Clustering in Unsupervised learning is an involuntary action taken by the algorithm where it forms clusters of similarity and anomaly and groups them together. These clusters then will be synthesized on the basis of results if needed by the formulae of the Principle of Mathematical Induction. Example -
    • These clusters are helpful in identifying usage patterns. A bunch of e-commerce and service-based startups uses a clustering model for the prediction of user behaviour and response to certain products.
  • Association - Association rule in unsupervised learning is also an involuntary action taken by machine learning where it forms similarities with datasets under relational databases. The Association rule is descriptive, not predictive therefore it is used to discover interesting relationships hidden in large datasets. This method is commonly used in consumer basket case offering, on the basis of consumers offering certain products are linked together in a combo pack. Example-
    • This model identifies critical relationships in data sets which gives useful insights. Consumers who buy "x" products often buy "y". This method is commonly used in e-commerce websites where on previous purchases, the customer was offered a package as an additional buy. It is also very pre-dominantly used in provisional stores with offers to boost sales.

Bias

Bias as the name suggests are prejudiced assumptions. The machine learning model is made extremely similar to the human brain. As we humans with our own bias assume things and reach the conclusion similarly machine learning also does the same thing. Our assumption is simply carried by past experiences that have some similarities to the current situation. These biases help us in making an educated yet informed decision and often decrease the time taken in reaching the verdict. The resemblance of the verdict with the real world is highly dependant on the education of bias.

In Machine Learning before reaching the conclusion or desired result, the algorithm predicts some biases by simulating the real-world scenarios. These biases are the main reasons that why the outcome in the Machine Learning algorithms always differs. There are two ways of executing biases in Machine learning- High Bias level, Low Bias level. The number indicates the new concepts learned by the algorithm. However, it can also lead to inconsistency and too much variance in the result received. Therefore it also implies the degree of intelligence of an algorithm in taking the right decision.

Conclusion

Data science is the mathematical modeling of data by applied intelligence and under the supervision of machine learning rules. Machine Learning of Data Science is capable to do the job of more than 100 individuals alone without 99% accuracy. Technical skills are the primary aspect of clearing a Data Science Interview. However, there are some soft skills that every data scientist should be focusing on to get better at explaining the intricacies of the result obtained. A well-articulated solved problem will influence each and every person in the room.

An average data scientist in India makes around 10 Lac INR per annum. In order to make yourself worthy of this package and lucrative job description, soft skills are extremely important. The machine will definitely do its task efficiently but it is the responsibility of the Data Scientist to explain the interesting linkages in a form of an aspiring storytelling way.

For any Data Science job, technical skills are the topmost priority. Therefore at EdifyPath, we have a course specifically designed to solve all the technical properties. There's also a mentor assist program wherein a real-life scenario will be implicated. These real-life scenarios are replicated and the students with the help of a mentor have to arrive at the desired outcome.