Wipro GE Health Care Data Scientist Interview Questions and Answers

Q1. What are the relevant projects in Data science & expertise in whatt all tools & technologies
Ans. 
Relevant projects in Data Science and expertise in tools and technologies
• Projects: Predictive modeling, Natural Language Processing, Computer Vision, Recommender Systems, Time Series Analysis

• Tools: Python, R, SQL, Tableau, Hadoop, Spark, TensorFlow, Keras, Scikit-learn

• Technologies: Machine Learning, Deep Learning, Big Data, Cloud Computing, Data Visualization
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Q1. He asked only coding question in Python & time duration is less & expectations are pretty unclear .
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Platform HackerEarth
9 Questions
2 Coding Questions on Python based on Dynamic Programming
7 Questions MCQ on Data Science and SQL

Q1. Data Science Projects discussion Python Coding by sharing screen
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Q1. Data Science Projects Detailed Questions
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Q2. In Depth Knowledge on any one of your project.
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Q3. Cross Questioning on Regression, Classfication Problems. Dummy Variable Trap Statistics Questions
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Q4. Questions on Deep Learning Models working and architecture like RNN Exploding/Vanishing Gradient Problem, Solutions like LSTM, CNN model working and different terminologies used.
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Q1. Again this round was pure Technical. All questions were on Data Science Projects and scenario based. 2 Questions on Probability and puzzle based.
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Q1. Final Round Discussion about Company's Policies and Roles. Your Expectations from the company, the kind of role you are looking for and why Tiger.
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Q1. Python programming, basic linear regression and logistic regression
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Q1. Linked list, dynamic programming questions
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Q1. Dicision Tree algorithm
Ans. 
Decision Tree algorithm is a supervised learning algorithm used for classification and regression tasks.
• Decision Tree algorithm is based on a tree-like model of decisions and their possible consequences.

• It uses a set of rules to split the data into branches and make predictions at the leaf nodes.

• The algorithm selects the best attribute to split the data based on certain criteria like information gain or Gini index.

• Deci...read more
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Q1. Machine Learning algorithms
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Machine Learning algorithms are used to train models on data to make predictions or decisions without being explicitly programmed.
• Machine Learning algorithms can be categorized into supervised, unsupervised, and reinforcement learning.

• Examples of supervised learning algorithms include linear regression, decision trees, and support vector machines.

• Examples of unsupervised learning algorithms include k-means clustering, ...read more
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Q2. Overfitting and underfitting
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Q3. Questions related to the project
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Q1. Questions on project, how much time required for training model
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Q2. Version control, Flask/ Django, Cloud service experience
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Q1. Basic ML Question, Timesereis and RNN with some questions on Comp Vision
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Q1. Project driven. Some coding
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Q1. Salary Discussion
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