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Data science is growing fast and changing how businesses use data to make decisions. Many companies want data scientists to analyze their data and improve results. This has created a high demand for data scientists, making these jobs competitive. To help you prepare, here are the top 30 common data science interview questions.
Top 30 Data Science Interview Questions for Freshers
This section is a complete guide for freshers to prepare for data science job interviews. It includes common interview questions to help you get your dream job as a data scientist. By studying these questions, you can handle even tough interviews at top companies. The article covers many topics to show your knowledge and skills. Learning these questions is an important step to becoming a successful data scientist.
1. Why are NumPy arrays faster than Python lists for numerical operations?
NumPy arrays are faster than Python lists for math operations. This is because NumPy is written in C, which is a compiled language and works faster. Moreover, Python lists are written in Python, an interpreted language, which is slower. NumPy also has many efficient functions for array operations.
2. What is the difference between a Python list and a tuple?
A Python list is an ordered collection of items that can change (mutable). In addition, you can add, remove, or edit elements in a list. Lists are made using square brackets [ ].
A tuple is also an ordered collection, but it cannot change (immutable). You cannot add, remove, or change elements in a tuple. Tuples are made using parentheses ( ).
Lists have more functions to modify them, but tuples are faster than lists.
3. Explain Python Sets.
In Python, a set is an unordered collection of unique items. It is used to store distinct objects and check if an item is in the set. Moreover, sets are created using curly braces { } with items separated by commas.
4. How can you differentiate between the split() and join() functions in Python?
The split() function in Python turns a string into a list using a separator like a space. For example, 'This is a string'.split(' ') gives ['This', 'is', 'a', 'string'].
The join() function does the opposite. It joins a list of strings into one string with a separator. For example, ' '.join(['This', 'is', 'a', 'string']) gives 'This is a string'.
Here are the basic data scientist interview preparation questions that will help you further.
5. What do you mean by logical operations in Python?
In Python, and, or, and not are used for boolean operations.
- and gives True if both conditions are True, otherwise it gives False.
- or gives True if at least one condition is True, otherwise it gives False.
- not reverses the value: it gives False if the condition is True and True if the condition is False.
6. How is logistic regression performed in machine learning?
Logistic regression predicts a label (dependent variable) based on features (independent variables). Moreover, it calculates the probability using a sigmoid function, which helps estimate the relationship between the label and features.
7. How to build a random forest model?
A random forest is made up of multiple decision trees. To build one, you follow these steps:
- Randomly choose 'k' features from a total of 'm' features (where k is much smaller than m).
- For these 'k' features, find the best-split point to create a node.
- Split the node into smaller nodes based on the best split.
- Repeat steps 2 and 3 until you reach the final leaf nodes.
- Build multiple trees (n trees) by repeating these steps.
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8. How to avoid an overfitting model?
Overfitting happens when a model focuses too much on a small amount of data and ignores the bigger picture. To avoid overfitting, you can:
- Keep the model simple by using fewer variables, which reduces noise in the data.
- Use cross-validation techniques, like k-folds cross-validation, to test the model on different data.
- Use regularization methods, like LASSO, to penalize certain model settings that may cause overfitting.
9. If a data set has more than 30% missing values, how will you handle them?
If a data set has more than 30% missing values, here’s how you can handle them:
- For large data sets, you can remove the rows with missing values. Moreover, this is quick, and the remaining data can use to make predictions.
- For smaller data sets, you can replace missing values with the average (mean) of the other data. In Python, you can use pandas functions like df.mean() or df.fillna(mean) to do this.
10. How to calculate Euclidean distance between two points in Python?
To calculate the Euclidean distance between two points in Python, use this formula:
euclidean_distance = sqrt((x2 – x1)**2 + (y2 – y1)**2).
For points plot1 = [1,3] and plot2 = [2,5], substitute the values:
distance = sqrt((2-1)**2 + (5-3)**2).
11. Explain dimensionality reduction.
Dimensionality reduction means turning large data with many features into smaller data with fewer features while keeping similar information. Moreover, this helps save storage space and reduces computation time. It also removes repeated features, like storing the same value in meters and inches.
Here, we’ve gone through some important data science interview questions and answers. Moreover, we’ll have a proper discussion on the other questions.
12. Explain recommender systems.
A recommender system predicts what a user might like based on their preferences.
- Collaborative Filtering: Suggests items based on what similar users like. For example, Amazon shows "Users who bought this also bought…" or Last.fm recommends songs played by users with similar interests.
- Content-based Filtering: Suggests items based on the item's features. For example, Pandora recommends songs with similar properties to the one you like.
13. What is the significance of the p-value in hypothesis testing?
The p-value helps decide if a result is significant:
- If p ≤ 0.05, it shows strong evidence against the null hypothesis, so you reject it.
- P > 0.05, it shows weak evidence against the null hypothesis, so you accept it.
- If p = 0.05, it is borderline and could go either way.
14. How to handle Outlier Value?
Outliers can handle in these ways:
- Remove outliers if they are garbage values, like a height recorded as "abc."
- Remove extreme values, like a point far outside the normal range (e.g., 100 when others are 0-10).
If you cannot remove them:
- Use a different model. Nonlinear models may handle outliers better than linear ones.
- Normalize the data to reduce the impact of extreme points.
- Use algorithms less affected by outliers, like random forests.
15. Write an SQL query to list all orders along with customer details.
To list all orders with customer details, you can use this SQL query:
SELECT OrderNumber, TotalAmount, FirstName, LastName, City, Country
FROM Order
JOIN Customer
ON Order.CustomerId = Customer.Id
This combines the Order and Customer tables. It matches rows where CustomerId in the Order table is equal to Id in the Customer table. In addition, it shows order numbers, amounts, and customer details like name, city, and country.
Further, we’ll elaborate on the most important data science interview cheat sheet.
16. Explain the utilization of Python Pass Keyword.
The pass keyword in Python is a placeholder that does nothing. Moreover, it is used when a statement is needed but no action is required. For example, you can use pass while creating a function or class if you haven't decided what it should do yet.
17. Explain the Use of the Python Continue Keyword.
The continue keyword in Python is used in loops to skip the current iteration and move to the next one. When Python sees continue, it stops the current loop cycle and starts the next one.
18. Briefly explain the difference between mutable and immutable data types.
Now, we’re moving further towards the intermediate level of data science interview questions.
In Python, immutable data types cannot be changed after they are created. Examples include numbers, strings, and tuples. Once created, you can't change their values.
Mutable data types can be changed after creation. Examples include lists and dictionaries, where you can modify their values.
Understanding this difference is important. For example, you can sort a list, but you can't sort a tuple because tuples are immutable. Instead, you'd need to create a new sorted tuple.
19. What is the significance use of try and except block?
In Python, the try and except blocks are used to handle errors (exceptions) that may occur during the program’s execution. The try block contains code that might cause an error. The except block contains the code to run if an error happens. However, this helps prevent the program from crashing and allows you to display a message or handle the error in a controlled way.
20. Explain Python Functions.
In Python, a function is a block of code that can be used in different parts of your program. Functions help by allowing you to reuse code, making it easier to manage and test. To use a function, you call its name and provide any necessary inputs. Functions can also help optimize code in several ways:
- They reduce repetition by allowing code to be used in multiple places.
- Python functions can make code easier to read and understand.
- They make it easier to test small parts of the program.
- They can improve performance by using optimized libraries or allowing the program to run more efficiently.
21. Why is NumPy so popular in the field of data science?
NumPy is a popular Python library used for scientific computing. It’s widely used in data science because it makes working with large amounts of numerical data easy and fast. In addition, NumPy is faster than Python's built-in tools because it uses C and Fortran code secretly. Moreover, it provides many functions for math and statistics on arrays and matrices. NumPy also helps work with large datasets efficiently, even those that don't fit in memory, by allowing data to be loaded in parts. Besides that, it works well with other libraries like SciPy and Pandas, making it perfect for complex data science tasks.
22. What is list comprehension and dict comprehension?
List comprehension and dict comprehension are ways to make new lists or dictionaries in a short form.
List comprehension creates a list. It has square brackets, an expression, and a for clause, with optional if or for clauses. Moreover, this makes a new list based on the expression and conditions.
Dict comprehension makes a dictionary. It uses curly braces with a key-value pair, a for clause, and optional if or for clauses. In addition, this creates a new dictionary based on the key-value pair and conditions.
23. Explain the ordered dictionary.
This is one of the common data science questions for interviews.
An ordered dictionary, or OrderedDict, is a special type of Python dictionary that keeps the order of items as they are added. In a regular dictionary, the order of items can change because it depends on the hash values of the keys. But in an ordered dictionary, the order stays the same because it uses a linked list to remember how items were added, even as the dictionary changes.
24. Explain the difference between return and yield keywords.
The return statement is used to exit a function and give a value back to where the function was called. When return is used, the function stops running, and the value after return is sent back.
On the other hand, yield is used in a generator function. Moreover, this kind of function gives one value at a time and can pause to remember its state, so it can continue from where it left off when needed.
25. What are lambda functions in Python, and why are they important?
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In Python, a lambda function is a small, unnamed function. You use it when you don't want to define a full function with the def keyword. Moreover, Lambda functions are helpful for short tasks and are often used with functions like map(), filter(), and reduce() to perform quick operations.
26. Explain the Assert keyword in Python.
In Python, the assert statement checks if a condition is True. If it's True, the program keeps running. If it's False, an error (AssertionError) is raised. It’s often used to make sure the program is working correctly, like checking if a list is sorted before searching it. However, assert is mainly for debugging, not for handling errors in live programs. For handling real errors, use try and except blocks.
27. What do you mean by Python decorators?
In Python, decorators are used to change or add new features to a function, method, or class without changing the original code. They are usually written as functions that take another function and return a new one with extra behavior. Moreover, a decorator is written with the @ symbol right before the function, method, or class it modifies. The @ symbol shows that the next function is a decorator.
28. How to perform univariate analysis for numerical and categorical variables?
Univariate analysis is a way to study one variable to understand its key features. For numerical variables, you calculate the mean, median, mode, and standard deviation to summarize the data. You can also use graphs like histograms or boxplots to see the data's spread, check for outliers, and test if the data follows a normal pattern. For categorical variables, you count how many times each category appears and find out what percentage it represents. You can use bar plots or pie charts to show this. It’s important to check for any imbalances or unusual patterns in the data.
29. What are the different ways in which we can find outliers in the data?
Outliers are data points that are very different from most of the data. They can affect the results of analysis or machine learning models. Here are some ways to find them:
- Visual inspection: Look at plots like histograms, scatterplots, or boxplots to spot outliers.
- Summary statistics: Compare the mean, median, or interquartile range. A big difference may indicate outliers.
- Z-score: Calculate how far a point is from the mean. If the z-score is very high (e.g., over 3), it could be an outlier.
There are various ways to identify outliers, and the best method depends on the data. Additionally, selecting the appropriate method is crucial for accurate results.
30. How is skewness defined in statistics, and what are the two types of skewness?
Skewness in statistics measures how symmetrical or lopsided a data set is. Moreover, a symmetrical distribution has most data points close to the middle, forming a bell curve. If the data is not symmetrical, it is skewed. There are two types of skewness:
- Positive skewness: The right side of the data has a long tail, with most data points on the left. This means a few high values pull the average to the right.
- Negative skewness: The left side of the data has a long tail, with most data points on the right. This means a few low values pull the average to the left.
Wrapping Up!
Data Science interview questions test how well candidates can analyze and understand data to find useful insights. Moreover, these interviews cover many topics like statistics, programming, machine learning, and data visualization. Candidates should know how to clean data, create useful features, evaluate models, and solve problems. Being able to explain findings clearly is also important. To prepare, candidates need knowledge, hands-on practice, critical thinking, and problem-solving skills.
Frequently Asked Questions
Q1. How do I prepare for a data science interview?
Ans. Research the company’s mission and values to prepare for data science interview questions. Practice mock interviews for feedback. Develop skills in data visualization, machine learning, and data analysis. Improve technical skills (programming, algorithms) and soft skills (communication, problem-solving). Be ready to discuss managing multiple projects and prioritizing tasks.
Q2. Is a data scientist interview difficult?
Ans. Data science interview formats and questions can be very different, making it hard to prepare for them.
Q3.What are the four pillars of data science?
Ans. Data science uses different methods and tools, which are mainly divided into four types: descriptive, inferential, predictive, and prescriptive.