Data Scientist - Advanced Test

Why this matters?

Understanding the different types of machine learning algorithms and when to use them is an important skill for an advanced data scientist.

What to listen for?

A good candidate should be able to explain the differences between supervised, unsupervised, and reinforcement learning, including the types of problems each is best suited for and the types of data each requires. They should also be able to provide an example of when each might be used in a data science project, such as supervised learning for classification or regression problems, unsupervised learning for clustering or dimensionality reduction, and reinforcement learning for decision-making problems.

Why this matters?

Deep reinforcement learning is a subfield of reinforcement learning that has gained significant attention in recent years, and understanding how it works is an important skill for an advanced data scientist.

What to listen for?

A good candidate should be able to explain what deep reinforcement learning is, including the concepts of neural networks and reinforcement learning, and how it differs from traditional reinforcement learning. They should also be able to provide an example of when deep reinforcement learning might be used in a data science project, such as for decision-making problems in areas such as robotics, gaming, and finance.

Why this matters?

Understanding the differences between generative and discriminative models is an important skill for an advanced data scientist, as it can impact the performance of a machine learning model.

What to listen for?

A good candidate should be able to explain the differences between generative and discriminative models, including the concepts of modeling the joint probability distribution of the inputs and outputs and modeling the conditional probability distribution of the outputs given the inputs. They should also be able to provide an example of when each might be used in a data science project, such as generative models for image generation and discriminative models for classification problems.

Why this matters?

Transfer learning is a technique used to improve the performance of a machine learning model by leveraging knowledge from a pre-trained model, and understanding how it works is an important skill for an advanced data scientist.

What to listen for?

A good candidate should be able to explain what transfer learning is, including the concept of fine-tuning a pre-trained model for a new task, and how it can be used in a data science project. They should also be able to discuss the benefits of transfer learning, such as faster training times and improved performance, and provide an example of when it might be used, such as for image classification or natural language processing problems.

Why this matters?

The curse of dimensionality is a phenomenon that occurs when working with high-dimensional data, and understanding its impact on the performance of a machine learning model is an important skill for an advanced data scientist.

What to listen for?

A good candidate should be able to explain what the curse of dimensionality is, including the concept of the exponential increase in the number of data points needed as the number of dimensions increases.