If you’re gearing up for a data science or analytics interview at Shopee, one of the leading e-commerce platforms in Southeast Asia and Taiwan, it’s essential to be prepared. Shopee, known for its innovative use of data to enhance user experiences and drive business decisions, often seeks talented professionals who can leverage data to gain insights and create impactful solutions. To help you ace your interview, we’ve compiled a list of common questions along with detailed answers:
Table of Contents
Machine Learning Interview Questions
Question: What is Overfitting in Machine Learning?
Answer: Overfitting occurs when a model learns the details and noise in the training data to the extent that it negatively impacts the performance of new, unseen data. In other words, the model performs well on the training data but fails to generalize to new examples. This often happens when a model is too complex relative to the amount of training data available.
Question: Explain the Bias-Variance Tradeoff.
Answer: The Bias-Variance Tradeoff is a fundamental concept in supervised learning. It refers to the balance between a model’s ability to capture the underlying patterns in the data (low bias) and its tendency to be sensitive to small fluctuations in the training data (high variance).
- Bias: High-bias models are simplistic and tend to underfit the data, meaning they do not capture the underlying patterns.
- Variance: High variance models are overly complex and tend to overfit the training data, capturing noise along with the underlying patterns.
Question: What is Gradient Descent?
Answer: Gradient Descent is an optimization algorithm used to minimize a function by iteratively moving in the direction of the steepest descent. In the context of machine learning, it is used to update the parameters of a model to minimize the loss function. The algorithm calculates the gradient of the loss function concerning the model’s parameters and takes steps proportional to the negative of this gradient to reach the minimum.
Question: Explain the difference between Classification and Regression.
Answer:
- Classification: In classification, the goal is to predict a categorical label or class for a given input. The output is a discrete value representing a category, such as “spam” or “not spam,” “fraudulent” or “non-fraudulent.”
- Regression: In regression, the goal is to predict a continuous value for a given input. The output is a real number, such as predicting house prices, stock prices, or temperature.
Question: How does K-Means Clustering work?
Answer: K-Means Clustering is an unsupervised learning algorithm used to partition a dataset into K clusters. It works as follows:
- Initialization: Randomly initialize K cluster centroids.
- Assignment: Assign each data point to the nearest cluster centroid.
- Update Centroids: Calculate the mean of the points in each cluster and update the cluster centroids.
- Repeat: Repeat the assignment and centroid update steps until convergence, when the centroids no longer change significantly or a maximum number of iterations is reached.
Question: What is the purpose of a Validation Set?
Answer: A Validation Set is a portion of the dataset used to tune hyperparameters and evaluate model performance during the training phase. It is separate from the training set and helps prevent overfitting by providing an independent dataset for model validation.
Question: Explain the concept of Feature Engineering.
Answer: Feature Engineering is the process of selecting, transforming, and creating new features from the raw data to improve the performance of machine learning algorithms. It involves:
- Feature Selection: Choosing the most relevant features for the model.
- Feature Scaling: Normalizing or standardizing features to ensure they have similar scales.
- Feature Transformation: Creating new features by combining existing ones, such as polynomial features or interaction terms.
SQL Interview Questions
Question: What is the difference between SQL and NoSQL databases?
Answer: SQL databases are relational databases that store data in tables with predefined schemas, while NoSQL databases are non-relational and store data in various formats, such as key-value pairs, documents, or graphs. SQL databases are typically used for structured data with complex relationships, while NoSQL databases are often used for unstructured or semi-structured data and scalability.
Question: What is the purpose of the GROUP BY clause in SQL?
Answer: The GROUP BY clause is used to group rows that have the same values in specified columns into summary rows. It is often used with aggregate functions like SUM, COUNT, and AVG to perform calculations on groups of rows.
Question: Explain the difference between WHERE and HAVING clauses in SQL.
Answer:
- WHERE: The WHERE clause is used to filter rows before any grouping or aggregation occurs. It is applied to individual rows.
- HAVING: The HAVING clause is used to filter groups of rows after the GROUP BY clause has been applied. It is applied to groups of rows.
Question: What is a Subquery in SQL?
Answer: A Subquery, also known as a nested query or inner query, is a query within another query. It can be used to return data that will be used in the main query’s condition, calculation, or selection.
Big Data Interview Questions
Question: What is Big Data?
Answer: Big Data refers to extremely large and complex datasets that cannot be easily processed using traditional data processing applications. It is characterized by the volume, velocity, and variety of data.
Question: What are the three V’s of Big Data?
Answer: The three Vs of Big Data are:
- Volume: The vast amount of data generated from various sources.
- Velocity: The speed at which data is generated and must be processed in real-time.
- Variety: The different types and formats of data, including structured, semi-structured, and unstructured data.
Question: What is Hadoop?
Answer: Hadoop is an open-source framework designed for storing and processing large volumes of data in a distributed computing environment. It consists of the Hadoop Distributed File System (HDFS) for storage and the MapReduce programming model for processing.
Question: What is the role of Apache Spark in Big Data processing?
Answer: Apache Spark is a powerful open-source distributed computing system that provides in-memory processing capabilities for large-scale data processing. It is known for its speed and ease of use, offering libraries for various tasks such as SQL, streaming data, machine learning, and graph processing.
Question: Explain the concept of Data Partitioning in Big Data.
Answer: Data Partitioning involves dividing large datasets into smaller, more manageable parts based on certain criteria, such as ranges of values or hashing algorithms. It helps improve query performance and scalability by allowing parallel processing on distributed systems.
Python (Pandas and Numpy) Interview Questions
Question: How do you create a NumPy array?
Answer: There are several ways to create a NumPy array:
Using a Python list: numpy.array([1, 2, 3])
Using numpy.arange: numpy.arange(10)
Using numpy.zeros or numpy.ones: numpy.zeros((3, 3)) or numpy.ones((2, 4))
Using numpy.random for random arrays: numpy.random.rand(2, 2)
Question: Explain the difference between a Python list and a NumPy array.
Answer:
- NumPy arrays are more efficient for numerical computations than Python lists because:
- NumPy arrays have a fixed size at creation, whereas Python lists can grow dynamically.
- NumPy arrays have a uniform data type for all elements, leading to more efficient storage and operations.
- NumPy arrays support vectorized operations, which means operations are performed on entire arrays rather than individual elements.
Question: How would you find the maximum value of a NumPy array?
Answer: You can use the numpy.max function:
import numpy as np
arr = np.array([1, 5, 2, 7, 3])
max_value = np.max(arr)
print(max_value)
Question: What is broadcasting in NumPy?
Answer: Broadcasting is a feature of NumPy that allows arithmetic operations to be performed on arrays of different shapes. When operating on two arrays, NumPy compares their shapes element-wise. It starts with the trailing dimensions and works its way backward, broadcasting dimensions if they are compatible.
Question: Explain the use of np.reshape() in NumPy.
Answer: np.reshape() is used to change the shape of an array without changing its data. It returns a new array with the specified shape. For example:
arr = np.arange(1, 10)
reshaped_arr = arr.reshape(3, 3)
print(reshaped_arr)
Question: How do you create a DataFrame in Pandas?
Answer: There are several ways to create a DataFrame in Pandas:
- From a dictionary: pd.DataFrame({‘A’: [1, 2, 3], ‘B’: [4, 5, 6]})
- From a NumPy array: pd.DataFrame(np.array([[1, 2], [3, 4]]), columns=[‘A’, ‘B’])
- From a CSV file: pd.read_csv(‘file.csv’)
Question: How would you select a specific column from a DataFrame in Pandas?
Answer: You can select a column using square brackets [] or by using dot notation:
import pandas as pd
df = pd.DataFrame({‘A’: [1, 2, 3], ‘B’: [4, 5, 6]})
column_A = df[‘A’] # or column_B = df.B
Question: Explain the use of pd.groupby() in Pandas.
Answer: pd.groupby() is used to group DataFrame rows based on one or more columns. It creates a groupby object that can then be used with aggregation functions. For example:
import pandas as pd
data = {‘Name’: [‘Alice’, ‘Bob’, ‘Charlie’, ‘Alice’], ‘Score’: [85, 92, 78, 88]}
df = pd.DataFrame(data)
grouped_df = df.groupby(‘Name’).mean()
print(grouped_df)
Question: How do you handle missing values in a DataFrame in Pandas?
Answer: You can handle missing values in Pandas using pd.dropna() to remove rows or columns with missing values, or pd.fillna() to fill missing values with a specified value. For example:
import pandas as pd
df = pd.DataFrame({‘A’: [1, 2, None], ‘B’: [4, None, 6]})
df_cleaned = df.dropna() # Drop rows with NaN values
df_filled = df.fillna(0) # Fill NaN values with 0
General Behavioral Interview Questions
Que: Tell me more about yourself.
Que: What are some of the projects you have worked on?
Que: What is the most difficult part of your project?
Que: What is your motivation to join Shopee?
Que: What can you do for our company?
Que: What is the most challenging task?
Que: What are some projects you’ve worked on?
Que: Why did you join and want to leave the previous company?
Que: Years of experience as a data scientist?
Que: Basic project experience to decide how to relate you to their opening positions.
Que: What are the main factors regarding your job selection?
Que: What is your expectations Salary?
Que: How did you approach the problem?
Conclusion
Preparing for a data science or analytics interview at Shopee requires a solid understanding of fundamental concepts, practical experience with data projects, and familiarity with key tools and techniques. Through this blog, we’ve explored a range of interview questions and answers tailored to help you succeed in the process.
Remember, Shopee values professionals who can harness the power of data to drive business decisions, enhance user experiences, and innovate within the e-commerce landscape. Be ready to showcase your ability to handle complex datasets, develop machine learning models, and derive actionable insights.
