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Cognitive Data Scientist
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IBM Cognitive Data Scientist Interview Questions and Answers

Updated 5 Feb 2024

Q1. Is it true that statistical models and Machine Learning are the same ?

Ans.

No, statistical models and Machine Learning are not the same.

  • Statistical models are based on mathematical equations and assumptions, while Machine Learning uses algorithms to learn patterns from data.

  • Statistical models require a priori knowledge of the data distribution, while Machine Learning can handle complex and unstructured data.

  • Statistical models are often used for hypothesis testing and parameter estimation, while Machine Learning is used for prediction and classificat...read more

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Q2. Which programming language are you familiar with ? Do you know R ?

Ans.

Yes, I am familiar with R.

  • I have experience in data analysis and visualization using R.

  • I have used R for statistical modeling and machine learning.

  • I am comfortable with R packages such as ggplot2, dplyr, and tidyr.

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Q3. What does Principal Component Analysis do?

Ans.

Principal Component Analysis is a statistical technique used to reduce the dimensionality of a dataset while retaining important information.

  • PCA identifies the underlying structure in the data by finding the directions of maximum variance.

  • It transforms the data into a new coordinate system where the first axis has the highest variance, followed by the second, and so on.

  • The transformed data can be used for visualization, clustering, or classification.

  • PCA assumes that the data ...read more

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Q4. What is Machine Learning ?

Ans.

Machine learning is a subset of artificial intelligence that involves training algorithms to make predictions or decisions based on data.

  • Machine learning involves using algorithms to learn patterns in data

  • It can be supervised, unsupervised, or semi-supervised

  • Examples include image recognition, natural language processing, and recommendation systems

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Q5. Tell me more about ML

Ans.

ML is a subset of AI that involves training algorithms to make predictions or decisions based on data.

  • ML algorithms can be supervised, unsupervised, or semi-supervised

  • Supervised learning involves training a model on labeled data to make predictions on new data

  • Unsupervised learning involves finding patterns in unlabeled data

  • Semi-supervised learning involves a combination of labeled and unlabeled data

  • Examples of ML applications include image recognition, natural language proces...read more

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Q6. WHAT DO YOU MEAN BY COGNITIVE?

Ans.

Cognitive refers to the mental processes and abilities related to perception, learning, memory, reasoning, and problem-solving.

  • Cognitive refers to the mental processes and abilities of the brain.

  • It involves perception, learning, memory, reasoning, and problem-solving.

  • Cognitive science studies how these processes work and interact.

  • Cognitive data science applies data analysis techniques to understand and improve cognitive processes.

  • Examples include analyzing brain activity data...read more

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Q7. What uses does it have?

Ans.

Cognitive Data Science has various uses in fields like healthcare, finance, marketing, and research.

  • Healthcare: Cognitive data science can be used to analyze patient data and predict diseases.

  • Finance: It can be used to analyze market trends and make investment decisions.

  • Marketing: It can be used to analyze customer behavior and personalize marketing campaigns.

  • Research: It can be used to analyze large datasets and discover patterns or insights.

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Q8. What are Kernals ?

Ans.

Kernels are small matrices used in image processing and machine learning algorithms to perform operations on images or data.

  • Kernels are used in convolutional neural networks (CNNs) to extract features from images.

  • They are also used in image processing techniques like blurring, sharpening, and edge detection.

  • Kernels can be represented as matrices of numbers that are applied to the input data to produce an output.

  • In machine learning, kernels are used in support vector machines ...read more

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