Cognitive Data Scientist

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Cognitive Data Scientist Interview Questions and Answers

Updated 5 Dec 2016

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

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.

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

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

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.

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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