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Wipro
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I applied via Recruitment Consulltant and was interviewed in Dec 2024. There was 1 interview round.
I applied via Naukri.com and was interviewed before Oct 2023. There were 2 interview rounds.
Basic TI and rule. With some case study.
I have worked on 3 Agile projects as a Cognos TM1 Developer. My role involved developing and maintaining TM1 models, collaborating with stakeholders, and ensuring project timelines were met.
Worked on 3 Agile projects as a Cognos TM1 Developer
Developed and maintained TM1 models
Collaborated with stakeholders to gather requirements
Ensured project timelines were met
posted on 24 Aug 2024
Java and manual testing
Arithmetic and reasoning
I applied via Naukri.com and was interviewed before Oct 2023. There were 2 interview rounds.
Basic TI and rule. With some case study.
I have worked on 3 Agile projects as a Cognos TM1 Developer. My role involved developing and maintaining TM1 models, collaborating with stakeholders, and ensuring project timelines were met.
Worked on 3 Agile projects as a Cognos TM1 Developer
Developed and maintained TM1 models
Collaborated with stakeholders to gather requirements
Ensured project timelines were met
Master detail and drill through are two different techniques used in Cognos reporting.
Master detail is used to display data in a hierarchical manner, where the details are shown based on a selected master item.
Drill through allows users to navigate from summary information to detailed information by clicking on a specific data point.
Master detail is typically used for displaying related data in a structured format, whi...
I applied via Approached by Company and was interviewed in Mar 2021. There were 3 interview rounds.
I applied via campus placement at Indian Institute of Technology (IIT), Chennai and was interviewed in Dec 2016. There were 5 interview rounds.
The company is a data-driven organization that provides cognitive solutions to businesses.
The company specializes in cognitive solutions.
They use data to provide insights to businesses.
Their focus is on helping businesses make better decisions.
They have a team of data scientists who work on developing these solutions.
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.
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
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, whi...
My expertise in machine learning and data analysis combined with my strong cognitive psychology background makes me a unique fit for this role.
Strong background in cognitive psychology
Expertise in machine learning and data analysis
Experience in developing and implementing cognitive models
Ability to translate complex data into actionable insights
Strong communication and collaboration skills
My unique combination of technical skills, creativity, and communication abilities make me a valuable asset for any corporate team.
Strong technical skills in data analysis and machine learning
Creative problem-solving approach to complex business challenges
Excellent communication and collaboration skills
Proven track record of delivering results and driving business growth
Ability to adapt to new technologies and learn qu
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 appli...
I applied via campus placement at Indian Institute of Technology (IIT), Chennai and was interviewed in Jan 2016. There were 4 interview rounds.
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...
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 mach...
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 disco
posted on 4 Dec 2016
I applied via campus placement at Indian Institute of Technology (IIT), Chennai and was interviewed in Dec 2016. There were 5 interview rounds.
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 cognitiv...
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