Accenture Jr. Data Scientist Interview Questions and Answers

Normal there is not advance requirment

Q1. Will you work will less amount package
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Q2. You wouldn't get any travel cost?
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Q3. What about machine learning
Ans. 
Machine learning is a branch of artificial intelligence that focuses on developing algorithms and models that can learn from and make predictions or decisions based on data.
• Machine learning involves training algorithms to learn patterns from data and make predictions or decisions.

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

• Examples include recommendation systems, image recognition, and natural langu
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Q4. Tellm me about any cloud platform
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A cloud platform is a service that allows users to store, manage, and process data remotely.
• Cloud platforms provide scalable and flexible storage solutions

• They offer various services such as computing power, databases, and analytics tools

• Examples include Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform
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Q5. Telll me about pointer
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A pointer is a variable that stores the memory address of another variable.
• Pointers are used to access and manipulate memory directly.

• They are commonly used in programming languages like C and C++.

• Example: int *ptr; // declaring a pointer variable
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Do well and contribute well

Do well and contribute well

Do well and contribute well

Q1. Why we choose this job
Ans. 
I am passionate about using data to solve complex problems and make meaningful insights.
• I have a strong background in statistics and data analysis

• I enjoy working with large datasets and finding patterns within them

• I am excited about the opportunity to work on real-world problems and make a positive impact
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Q2. What are your expectations
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I expect challenging projects, opportunities for growth, supportive team environment, and continuous learning.
• Challenging projects that allow me to apply my skills and knowledge

• Opportunities for growth and advancement within the company

• Supportive team environment where collaboration and knowledge sharing are encouraged

• Continuous learning through training, workshops, and mentorship programs
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Q1. Tell me about your self
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I am a data-driven individual with a strong background in statistics and machine learning.
• Graduated with a degree in Statistics

• Proficient in programming languages like Python and R

• Experience with data visualization tools like Tableau

• Completed projects involving predictive modeling and data analysis
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Q2. What are your salary expectations
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I am looking for a competitive salary based on my skills and experience.
• Research industry standards for Jr. Data Scientist salaries

• Consider my level of experience and education

• Factor in the cost of living in the area where the job is located
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This was good aptitude test computer based

Coding round share screen and code

Q1. Explains OOPs concept
Ans. 
OOPs (Object-Oriented Programming) is a programming paradigm based on the concept of objects, which can contain data and code.
• OOPs focuses on creating objects that interact with each other to solve complex problems.

• Key principles of OOPs include encapsulation, inheritance, polymorphism, and abstraction.

• Encapsulation involves bundling data and methods that operate on the data into a single unit (object).

• Inheritance allo...read more
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Q2. Explain SOLID principles
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SOLID principles are a set of five design principles in object-oriented programming to make software more maintainable, flexible, and scalable.
• Single Responsibility Principle (SRP) - A class should have only one reason to change.

• Open/Closed Principle (OCP) - Software entities should be open for extension but closed for modification.

• Liskov Substitution Principle (LSP) - Objects of a superclass should be replaceable with...read more
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Q1. Introduction and overview of the projects
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Q1. Computer vision questions
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Q1. Evaluation metrics used in multiclass classification
Ans. 
Evaluation metrics for multiclass classification
• Accuracy

• Precision

• Recall

• F1 Score

• Confusion Matrix
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Q2. Difffereence between append and extend
Ans. 
Append adds a single element to a list while extend adds multiple elements.
• Append adds the element as it is to the end of the list.

• Extend takes an iterable and adds each element of the iterable to the end of the list.

• Append can be used to add a single element to a list.

• Extend can be used to add multiple elements to a list.

• Example: list1 = [1, 2, 3], list2 = [4, 5], list1.append(4) will result in [1, 2, 3, 4], list1.ext
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Q3. Sort a list and take out second minimum
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Sort a list and extract the second minimum value.
• Sort the list in ascending order using the sort() method.

• Extract the second minimum value using indexing.

• Handle cases where the list has less than two elements.
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Fibonacci series print

Sales dataset to predict future sales

Q1. What is precision and recall
Ans. 
Precision and recall are evaluation metrics used in machine learning to measure the performance of a classification model.
• Precision is the ratio of correctly predicted positive observations to the total predicted positive observations.

• Recall is the ratio of correctly predicted positive observations to the all observations in actual class.

• Precision is important when the cost of false positives is high, while recall is i...read more
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