Accenture Data Science Consultant Interview Questions, Process, and Tips

Q1. Design a dwmand planning system
Ans. 
Design a demand planning system for efficient forecasting and inventory management.
• Utilize historical sales data to identify trends and seasonality

• Incorporate external factors like market trends, promotions, and competitor activities

• Implement machine learning algorithms for accurate demand forecasting

• Integrate with inventory management systems for optimized stock levels

• Regularly review and adjust the system based on pe
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Q2. Pick one point from resume and explain
Ans. 
Implemented machine learning model to predict customer churn for a telecom company
• Developed and trained a machine learning model using Python and scikit-learn

• Utilized historical customer data to identify patterns and factors leading to churn

• Evaluated model performance using metrics such as accuracy, precision, and recall

• Provided actionable insights to the telecom company based on the model's predictions
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Python Coding and ML concepts

Q1. How would you support a colleague who has made a mistake.
Ans. 
Support colleague by offering assistance, understanding, and constructive feedback.
• Offer emotional support and reassurance to help alleviate any stress or anxiety.

• Provide constructive feedback in a respectful and non-judgmental manner.

• Offer assistance in rectifying the mistake and finding a solution together.

• Encourage open communication and a growth mindset to learn from the mistake.

• Avoid blaming or shaming the colleag
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Q2. Case study of a short analysis
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Q1. Guest Estimates on how many sofa sold in a day in your city
Ans. 
It is difficult to estimate the exact number of sofas sold in a day in a city without specific data.
• The number of sofas sold in a day can vary greatly depending on factors such as population size, economic conditions, and consumer preferences.

• One way to estimate could be to look at the number of furniture stores in the city and their average daily sales.

• Another approach could be to conduct a survey of a sample of furni...read more
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Q2. Ml algorithm’s terminology
Ans. 
ML algorithm's terminology refers to the specific vocabulary used to describe concepts, processes, and components in machine learning models.
• Supervised learning: algorithms learn from labeled training data, e.g. linear regression, support vector machines

• Unsupervised learning: algorithms find patterns in unlabeled data, e.g. clustering, dimensionality reduction

• Feature engineering: process of selecting, transforming, and...read more
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Q1. Difference between boosting and bagging techniques ?
Ans. 
Boosting and bagging are ensemble learning techniques used to improve the performance of machine learning models.
• Boosting focuses on improving the performance of a single model by training multiple models sequentially, where each subsequent model corrects the errors of its predecessor.

• Bagging, on the other hand, involves training multiple models independently and then combining their predictions through averaging or vo...read more
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Q1. Questions based on projects in resume
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