Data science Job interview is mostly a complex process and Knowing the right or exact Interview topics or frequently ask questions makes the selection process easy.
Getting the right job as a data scientist is a very complex part and tidies process whereas having a well understanding of the industry and business tool or technology is a more crucial part.
Data science is a highly diverse and massively growing field, that consisting of several techniques, tools, and technologies to solve any type of business or data-related issues.
It including topics like computer science, data engineering, probability and statistics, machine learning, and domain understanding.
It is simply not easy for anyone as a data scientist to be proficient in all areas. That Below, you can get a brief outline of the topics that will mostly be asked and cover in the data science interview.
Data Science Interview Question Topics
Computer Science (25%)
1.Programming Languages (Python or R)
R and Python both are well-known data processing and statistical analysis programming languages which easy to learn and code.
These are the base of data science that is 100% need to be prepared with hands-on practice to crack Data Science Interview easily.
2. Databases Concepts (SQL, NoSQL)
SQL is the base of data science work and it is highly used in any position of data science-related profiles like data analyst, data engineer, machine learning engineer, etc. everyone should need to be an expert at SQL.
3. Data Structures (Lists, Hash Tables, Stacks, Queues, Trees, Graphs)
4. Algorithms (searching, sorting, graph traversals)
5. Distributed Computing (MapReduce, Spark, Hadoop)
Big data techniques and technologies are the basis of big data analytics that is getting more popular and extensively used for handling large amounts of daily customer data.
Every one-third (1/3) of companies are using big data technologies, so learning and getting experts in this can easily give you a well-qualified job.
Data Engineering (25%)
1.Data Wrangling, Processing, and Cleaning, Data reporting and visualization (using PowerBI, Tableau, Excel)
2. Data Pipeline (ETL, Data Lake, Data Wearhouse)
It is the workflow of data and processing tools that transfer the data from one end to another end with few intermediate data processing activities.
4. Feature Engineering
5. Cloud Computing (Azure, AWS, DataBricks)
Statistics and Probability (5%)
1.Statistical Techniques (mean, median, mode, standard deviation, variance, etc)
Several Statistical Graphs (Histogram, Bar chart, Pie chart, Box plot, scatterplot)
2. Probability Distributions
3. Conditional probability (Bayes’ Theorem)
4. Covariance and correlation
5. Hypothesis Testing (null hypothesis, p-values, confidence intervals)
Machine Learning (25%)
1.Supervised Learning and its algorithms (Linear Regression, Logistic Regression, k-NN, SVM, Random Forest, Gradient Boosting)
2. Unsupervised Learning and its algorithms (k-means, hierarchical clustering, Principal Component Analysis (PCA), etc.)
3. Deep Learning and neural network (ANN, CNN, RNN, LSTM)
4. General Predictive Modeling (choosing the right evaluation metrics, train and test sets, cross-validation)
Domain Understanding (15%)
The experienced candidate has more weightage than any data scientist who is fresher and the reason is domain expertise and relevant experience in a specific sector.
Fresher data scientists and engineers mostly don’t have the industrial experience that takes them away from getting the right opportunity.
Preparing the industrial problems or doing an internship can make you good in domain understanding similarly it Really depends on the company if they really looking for this or not.
For example, if you are working as a data scientist in real estate, would be good to have experience in real estate. Or for an NLP role, be prepared for NLP-related questions.
If you are interviewing for a product-focused company, would be a good idea to know the products along with key success metrics for the company’s various products.
Logical and Personality Topics(5%)
Always try to improve your logical thinking and puzzles solving skills that most relevant and useful in any organization while dealing with any business problems.
However different behaviors and personality questions need to be prepared which is most Certainly ask in the interview to determine if you will fit well and get along with the company’s culture and people.
All the mentioned topics are well related and repeatedly ask in different interviews for any type of data science and related positions.
These topics are highly used to solve any type of business problem which are related to data and that why all those are significant in the data science interview.
On the other hand, these skills are very helpful to increase the decision-making capabilities to increase sales and business revenue.
That’s the reason each and every organization are relying on data scientist and they are hiring data person who has this set of skills to solve their business issues.
Meet Nitin, a seasoned professional in the field of data engineering. With a Post Graduation in Data Science and Analytics, Nitin is a key contributor to the healthcare sector, specializing in data analysis, machine learning, AI, blockchain, and various data-related tools and technologies. As the Co-founder and editor of analyticslearn.com, Nitin brings a wealth of knowledge and experience to the realm of analytics. Join us in exploring the exciting intersection of healthcare and data science with Nitin as your guide.