Top 30 Data Science Interview Questions And Answers 2023

 

Top Data Science Interview Questions 

 

It should be no wonder that in the new generation of big data and machine learning, data scientists are evolving as rock stars. Industries that can leverage immense amounts of data to enhance the way they serve consumers, produce products, and conduct their operations will be positioned to succeed in this economy.

 

Here are some basic Data Science interview questions and answers for you:

 

 

1. What is Data Science?

 

Data Science incorporates statistics, maths, specialised programs, artificial intelligence, machine learning etc. Data Science is merely the application of detailed principles and analytic approaches to extract information from data operated in strategic planning, decision-making, etc. In simple words, data science signifies analysing data for actionable insights.

 

2. What is the importance of Data Cleansing?

 

As the name implies, data cleansing is a process of removing or revising information that is incorrect, insufficient, duplicated, nonessential, or formatted improperly. It is essential to enhance the quality of data and hence the accuracy and productivity of the procedures and organisation. Real-world data is often captured in forms which have hygiene problems. There are sometimes mistakes due to various causes which make the data irresponsible and sometimes only some attributes of the data. Hence data cleansing is done to purify the usable data from the raw data, otherwise many systems consuming the data will deliver inaccurate results.

 

3. What is the importance of statistics in data science?

 

Statistics help data scientists to get a finer idea of a client's anticipations. Using statistical techniques, data scientists can attain an understanding of customer interest, conduct, engagement, retention, etc. It also helps create robust data models to validate specific assumptions and predictions.

 

4. What is an API? What are APIs used for?

 

API, an abbreviation of the application program interface, is a collection of routines, protocols, and tools for creating software applications. The API determines how software segments should interact. A good API makes it easier to generate a program by supplying all the building blocks. 

 

5. How is Data Science different from Big Data and Data Analytics?

 

Data Science uses algorithms and tools to draw influential and commercially useful understandings from raw data. It concerns tasks like data modelling, data cleansing, analysis, pre-processing etc. Big Data is the massive collection of structured, semi-structured, and unstructured data in its natural form developed through different channels.

 

And finally, Data Analytics gives an operational understanding of complicated business strategies. It also helps in predicting approaching opportunities and dangers for an organisation to control.

 

6. How is k-NN different from k-means clustering?

 

K-nearest neighbours are a classification algorithm, which is a subset of supervised machine learning. K-means is a clustering algorithm, that is a subset of unsupervised machine learning. And K-NN is a Classification or Regression Machine Learning Algorithm while K-means is a Clustering Machine Learning Algorithm. K-NN is the number of closest neighbours used to organise a test sample, whereas K-means is the number of clusters the algorithm is attempting to understand from the data. 

 

7. What are dimensionality reduction and its benefits?

 

Dimensionality reduction directs to the process of transforming a data set with vast measurements into data with fewer dimensions (fields) to obtain similar knowledge concisely. This reduction helps in condensing data and decreasing storage space. It also decreases computation time as fewer measurements lead to smaller computing. It clears redundant attributes; for example, there is no point in storing a value in two separate units (meters and inches). 

 

Read More: Top 30 Machine Learning Interview Questions & Answers 2023

 

8. Why Data Normalization is necessary for Machine Learning models?

 

Normalization is often used in the numeric columns during the data preprocessing phase of machine learning. Normalization is used to maintain the values of numeric columns on a common scale, without deforming the difference in the range of values. Not every dataset will need normalization. It is used only when attributes have a distinct range.

 

 

9. What is JSON and What is XML?

 

JSON is an abbreviation for JavaScript Object Notation. It is a preliminary data format that uses human-readable text to transmit data objects consisting of data interpretation language. Although initially emanated from the JavaScript scripting language, JSON is a language-independent data format. Code for developing JSON data is readily obtainable in many programming languages.

 

XML is an abbreviation for Extensible Markup Language. It describes a set of directions that is used for encoding records in a human and machine-readable form. The creation goals of XML emphasize clarity, generality and usability across the Internet. It is a textual data structure with strong backing for different human languages. 

 

10. What is the significance of a box plot?

 

A whisker plot also known as a box plot is used to display the reach and centres (mean) of a data set. It is also used to catch outliers datasets. It displays the dispersion of data across the mean. It sums up the solution as the minimum, first quartile, median, third quartile, and maximum. We draw a box from the first quartile to the third quartile in the box plot. The whiskers go from every quartile to the minimum or maximum.

 

 

Here are some advanced data science interview questions about machine learning and data visualisation.

 

11. What is the importance of Sampling?

 

Sampling is an essential statistical strategy to analyze large volumes of datasets. This applies to taking out some samples that describe the total data population. It is critical to select samples that are the true instances of the whole data set. 

 

12. What is the p-value?

 

P-value helps you decide the solidities of your results when you conduct a hypothesis test. It is a digit between 0 and 1. The declaration which is on trial is known as the Null Hypothesis. Lower p-values, i.e. ≤ 0.05, mean we can abandon the Null Hypothesis. A high p-value, i.e. ≥ 0.05, indicates we can obtain the Null Hypothesis. An exact p-value of 0.05 means that the Hypothesis can go either manner. P-value is the measure of the possibility of circumstances other than those presented by the null hypothesis. It means the probability of events is more irregular than the occurrence suggested by the null hypothesis.

 

13. Explain selection bias

 

Selection bias happens when the analysis does not have a spontaneous selection of participants. It is a contortion of statistical analysis resulting from the process of containing the sample. Selection bias is also called the selection effect. When experts fail to take selection bias into account, their judgments might be wrong.

 

14. What are systematic sampling and cluster sampling

 

Systematic sampling is a part of the probability sampling method. The sample components are picked from a larger population with a spontaneous starting point but a specified periodic interval. This interval is known as the sampling interval. The sampling interval is calculated by dividing the population size by the selected sample size. 

Cluster sampling concerns dividing the sample population into different levels, called clusters. Then, an easy random sample of clusters is chosen from the population. The research is performed on data from the sampled clusters.

 

15. What is dimensionality reduction? What are its benefits?

 

Dimensionality reduction is described as the method of converting a data set with vast measurements into data with lesser measurements — to share similar facts concisely. This method is especially useful in compressing data and decreasing storage space. It is also useful in decreasing computation time due to fewer measurements. Finally, it helps clear redundant attributes — for instance, storing a value in two separate units (meters and inches) is averted.

 

16. What is a ROC Curve? 

 

AUC – ROC curve is a performance measure for the classification issue at different threshold settings. ROC is a possibility curve and AUC describes the degree or measure of separability. It conveys how much the model is skilled in differentiating between classes.

 

17. What is the difference between “long” and “wide” format data?

 

Wide format is where we have a single row for each data point with numerous columns to have the values of various characteristics. The long format is where for each data point we have as many rows as the number of features and each row has the value of a certain feature for a given data point.

 

18. Explain the SVM machine learning algorithm in detail.

 

SVM is a machine learning algorithm which is used for classification and regression. For classification, it discovers a multi-dimensional hyperplane to differentiate between classes. SVM uses linear kernels, polynomials, and RBF. There are a few parameters which require to be given to SVM to identify the points to assume while the analysis of the hyperplane.

 

19. What is Collaborative Filtering?

 

Collaborative filtering is a process that can filter out items that a user might want based on responses from similar users. It operates by exploring a large group of people and finding a smaller set of users with preferences similar to a particular user.

 

 20. What is Ensemble Learning? Define types.

 

Ensemble Learning is clubbing numerous weak learners (ml classifiers) and then operating aggregation for result projection. It is observed that even if the classifiers function badly individually, they do nicely when their results are aggregated. An instance of ensemble learning is a spontaneous forest classifier.

 

21. What are Recommender Systems?

 

A recommendation engine is a system, based on data analysis of the history of users and the conduct of similar users, that presents products, services, and knowledge to users. A recommendation can take user-user connections, product-product associations, product-user associations etc. for recommendations.

 

22. What is variance in Data Science?

 

Variance is the value which describes the individual figures in a group of data which spreads themselves about the mean and describes the distinction of each value from the mean value. Data Scientists use variance to comprehend the distribution of a data set.

 

23. What is an RNN (recurrent neural network)?

 

RNN is an algorithm that operates sequential data. RNN is used in language translation, voice recognition, image capture, etc. There are different types of RNN networks, such as one-to-one, one-to-many, many-to-one, and many-to-many. RNN is used in Google’s voice search and Apple’s Siri.

 

24. What is root cause analysis?

 

Root cause analysis was originally developed to analyze industrial calamities but is now widely used in different areas. It is a problem-solving approach used for separating the root causes of faults or problems. A factor is called a root cause if its removal from the problem-fault-sequence prevents the final unwanted event from recurring.

 

25. What are the feature vectors?

 

A feature vector is an n-dimensional vector of numerical attributes that describe an object. In machine learning, feature vectors are used to represent numeric or figurative attributes (called features) of an object in a mathematical way that is easy to examine.

 

26. What are the steps in making a decision tree?

 

1. Take the total data set as input.
2. Look for a partition that maximizes the detachment of the classes. A split is any trial that separates the data into two sets.
3. Apply the separation to the input data (divide step).
4. Re-apply stages one and two to the separated data.
5. Stop when you meet any stopping standards.
6. This stage is called pruning. Clean up the tree if you went too far doing separations.

 

27. What is star schema?

 

It is a traditional database schema with the main table. Satellite tables map IDs to material names or definitions and can be attached to the central fact table using the ID fields; these tables are known as lookup tables and are mainly useful in real-time applications, as they preserve a lot of memory. Sometimes, star schemas concern several layers of summarization to retrieve details faster.

 

28. How regularly must an algorithm be updated?

 

  You will like to update an algorithm when:

• Do you like the model to develop as data streams via infrastructure
• The underlying data reference is transforming
• There is a possibility of non-stationarity

 

29. Why is R used in Data Visualization?

 

  R is widely used in Data Visualizations for the following causes

• We can make almost any kind of graph with R.
• R has numerous libraries like lattice, ggplot2, leaflet, etc., and so multiple inbuilt functions also.  
• It is more comfortable to customize graphics in R compared to Python.
• R is used in feature engineering and experimental data analysis as well.

 

30. Difference between Point Estimates and Confidence Interval

 

Confidence Interval: A scope of values likely including the population parameter is given by the confidence interval. Further, it even notifies us how likely that certain interval can hold the population parameter.

Point Estimates: An estimation of the population parameter is given by a certain value called the point estimate. Some famous techniques used to emanate Population Parameters’ Point estimators are – The maximum Likelihood estimator and the Method of Moments.