Data Science and Analytics have become the backbone of decision-making processes across industries, from telecommunications to healthcare, finance to e-commerce. Ericsson, a global leader in telecommunications technology and services, has been at the forefront of utilizing data science to drive innovation and efficiency. For those aspiring to join the ranks of Ericsson’s data wizards, preparing for the interview process can be a pivotal step. Let’s delve into some common interview questions and insightful answers to guide you through this process.
Table of Contents
Technical Interview Questions
Question: Explain Mean, Median, and Mode.
Answer: Mean: Average value of a dataset, found by summing all values and dividing by the number of values.
- Median: Middle value of a sorted dataset; unaffected by outliers.
- Mode: Most frequent value in a dataset; useful for categorical data.
Question: Explain logistic regression and why it is called regression instead of classification.
Answer: Logistic Regression is a statistical model used for binary classification tasks, predicting the probability of an event (0 or 1) based on input features. It is called “regression” because it predicts the probability of a categorical outcome, fitting a curve to the data points. Despite its name, it performs classification by setting a threshold on the predicted probabilities (usually 0.5) to assign class labels.
Question: What is A/B testing?
Answer: A/B testing, also known as split testing, is a method used to compare two versions of a webpage, app, or marketing campaign to determine which one performs better. It involves dividing the users into two groups, A and B, and showing each group a different version. By measuring the response of each group (such as click-through rates, conversions, or other metrics), businesses can make data-driven decisions about which version is more effective in achieving their goals.
Question: Explain SVM.
Answer: Support Vector Machine (SVM) is a supervised machine learning algorithm for classification tasks. It finds the best hyperplane that separates data into different classes with the maximum margin, making it robust to outliers. SVM can also use kernel functions to handle non-linear boundaries, making it versatile for various types of data.
Question: What are regularization techniques?
Answer: Regularization techniques in machine learning prevent overfitting and improve model generalization:
- L1 Regularization (Lasso) encourages sparsity by driving some coefficients to zero.
- L2 Regularization (Ridge) controls model complexity by shrinking coefficients.
- ElasticNet combines L1 and L2 penalties, balancing feature selection and coefficient shrinkage.
Question: What’s the difference between L1 and L2?
Answer:
L1 (Lasso) Regularization:
- Encourages sparsity by driving some coefficients to exact zero.
- Useful for feature selection, keeping only the most relevant features.
L2 (Ridge) Regularization:
- Controls model complexity by shrinking coefficients towards zero.
- Effective in handling multicollinearity, reducing the impact of correlated features.
Question: What is PCA?
Answer: Principal Component Analysis (PCA) is a dimensionality reduction technique used in machine learning and data analysis. It aims to transform a dataset of possibly correlated variables into a new set of uncorrelated variables called principal components. These components are ordered by the amount of variance they explain in the data, allowing for the retention of most of the important information in fewer dimensions. PCA helps in visualizing high-dimensional data, reducing noise, and improving the efficiency of machine learning algorithms by working with a smaller feature space.
Question: Explain supervised and unsupervised learning.
Answer:
Supervised learning involves training a model on labeled data, where each input is paired with an output label. The model learns patterns and relationships from these examples, allowing it to make predictions or classify new, unseen data points. For example, in a supervised classification task, the model learns from labeled images of cats and dogs to correctly classify new images into these categories.
Unsupervised learning works with unlabeled data, seeking to discover underlying patterns or structures within the dataset. It clusters similar data points together or reduces the dimensionality of the data without any predefined output labels. This type of learning is useful for exploratory analysis and understanding the inherent relationships within the data.
Question: What is LSTM?
Answer: Long Short-Term Memory (LSTM) is a type of recurrent neural network (RNN) architecture designed to overcome the limitations of traditional RNNs in capturing long-term dependencies in sequential data. LSTMs are particularly effective in tasks involving time series data, natural language processing, and speech recognition. The key feature of LSTMs is their ability to maintain and update cell states, allowing them to remember important information over long sequences.
Question: What is Backpropagation?
Answer: Backpropagation is a method used to train artificial neural networks by computing gradients of the loss function concerning the network’s weights. It involves propagating the error backward through the network to update the weights, improving the model’s predictions iteratively. This algorithm enables neural networks to learn from data and adjust their parameters to minimize errors during training.
Question: How do random forests work?
Answer: Random Forest is an ensemble learning algorithm that creates multiple decision trees during training. Each tree is trained on a random subset of the training data and a random subset of features. When making predictions, each tree votes on the output and the most popular prediction is chosen. This helps in reducing overfitting, handling missing values, and providing robust predictions by aggregating the results from multiple trees.
Question: What is the difference between the bagging and boosting algorithm?
Answer:
Bagging (Bootstrap Aggregating):
- Creates multiple models in parallel on random subsets of the data.
- Final prediction is an average or majority vote of individual predictions.
- Reduces variance and overfitting by combining diverse models.
Boosting:
- Trains multiple models sequentially, correcting errors of previous models.
- Assign weights to training instances, focusing on misclassified examples.
- Aims to reduce bias and variance, often leading to higher accuracy than individual models.
Question: What is Classification metrics?
Answer: Classification metrics are used to assess the performance of a classification model in predicting class labels. They include accuracy (overall correctness), precision (positive predictive value), recall (sensitivity), F1 score (harmonic mean of precision and recall), and ROC curve (trade-off between true positive rate and false positive rate). These metrics provide insights into the model’s ability to correctly classify instances and balance between different types of errors.
Question: Explain Regularization.
Answer: Regularization is a technique in machine learning to prevent overfitting by adding a penalty to the loss function. It encourages the model to learn simpler patterns, reducing the influence of complex features. Common types include L1 (Lasso) and L2 (Ridge) regularization, which control the size of model coefficients. The goal is to improve the model’s ability to generalize to new data while avoiding fitting noise in the training set.
Question: What is XGboost?
Answer: XGBoost, short for “Extreme Gradient Boosting,” is a powerful and efficient implementation of the gradient boosting algorithm. It builds decision trees sequentially, correcting errors of previous models, and includes regularization techniques to prevent overfitting. Known for its speed, accuracy, and scalability, XGBoost is widely used in machine learning for structured/tabular data tasks.
Question: Explain GAN.
Answer: GAN (Generative Adversarial Network) is a type of deep learning model with two neural networks: the generator and the discriminator. The generator creates new data instances, such as images, while the discriminator tries to distinguish between real and generated data. Trained in a competitive process, GANs are used for generating realistic images, art, and synthetic data for various applications.
Probability Interview Questions
Question: What is probability?
Answer: Probability is a measure of the likelihood of an event occurring, expressed as a number between 0 (impossible) and 1 (certain).
Question: What is the difference between probability and odds?
Answer: Probability represents the likelihood of an event occurring, while odds represent the ratio of the probability of success to the probability of failure.
Question: What is conditional probability?
Answer: Conditional probability is the probability of an event occurring given that another event has already occurred. It is denoted by P(A∣B), read as “the probability of A given B.”
Question: What is the formula for the union of two events (A or B)?
Answer: The formula for the union of two events is P(A∪B)=P(A)+P(B)−P(A∩B), where P(A) and P(B) are the probabilities of events A and B, and P(A∩B) is the probability of their intersection.
Question: What is the difference between independent and dependent events?
Answer: Independent events are events where the outcome of one event does not affect the outcome of another event. Dependent events, on the other hand, are events where the outcome of one event affects the outcome of another.
Python Interview Questions
Question: Explain the difference between __str__ and __repr__ methods in Python.
Answer: The __str__ method is used to return a human-readable string representation of an object and is typically used for end-users.
The __repr__ method returns an unambiguous string representation of the object and is mainly used for debugging and development.
Question: What is a decorator in Python?
Answer: A decorator in Python is a function that takes another function as an argument and extends its functionality without modifying its code directly. Decorators are used to add functionalities such as logging, authentication, or caching to functions.
Question: What is the Global Interpreter Lock (GIL) in Python?
Answer: The Global Interpreter Lock (GIL) is a mutex that protects access to Python objects, preventing multiple native threads from executing Python bytecode simultaneously. This means that in CPython, the standard implementation of Python, only one thread can execute Python code at a time, limiting parallelism in multi-threaded programs.
Question: Explain the difference between == and is operators in Python.
The == operator checks for equality of values between two objects.
The operator checks for object identity, meaning it checks if two variables refer to the same object in memory.
Question: What are the differences between __getattr__ and __getattribute__ methods in Python?
Answer: __getattr__ is called when an attribute is not found in the usual places, such as the instance dictionary or class hierarchy.
__getattribute__ is called every time an attribute is accessed, regardless of whether it exists or not. It is more general and used for more advanced attribute access control.
Question: Explain the use of the yield keyword in Python.
Answer: The yield keyword is used in generator functions to create iterators. When a function contains a yield statement, it becomes a generator. It allows the function to pause its execution yield a value to its caller, and then resume execution from where it left off.
Question: What is the purpose of the zip function in Python?
Answer: The zip function takes iterable (such as lists, tuples, etc.) and aggregates their elements into tuples. It returns an iterator of tuples, where the i-th tuple contains the i-th element from each of the input iterables.
Technical Interview Topics
- Basic Pandas data analysis
- Python basic
- Probability questions
- Basic ML understanding
Conclusion
Preparing for a data science and analytics interview at Ericsson, or any top-tier company, requires a blend of technical expertise, problem-solving skills, and effective communication abilities. Remember to tailor your responses to the company’s values and industry focus. Best of luck on your journey to becoming a part of Ericsson’s innovative data-driven team!
