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Novelvox
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I applied via Naukri.com and was interviewed in Oct 2024. There was 1 interview round.
Learning Data Analysis involves understanding key concepts, tools, and techniques through practice and real-world projects.
Start by learning basic statistics and data visualization techniques
Practice using tools like Excel, SQL, and Python for data manipulation and analysis
Take online courses or attend workshops to learn advanced data analysis techniques
Work on real-world projects to apply your skills and build a portf...
I am choosing Data Analysis because I enjoy uncovering insights from data and using it to drive decision-making.
Passion for uncovering insights from data
Interest in using data to drive decision-making
Enjoyment of analyzing trends and patterns in data
Ability to communicate findings effectively to stakeholders
Data analysis is the process of inspecting, cleaning, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making.
Data analysis involves examining raw data to find patterns and draw conclusions
It includes cleaning and transforming data to ensure accuracy and consistency
Statistical methods and machine learning techniques are often used in data analysis
The results of data...
A pivot table in Excel is a data summarization tool that allows you to reorganize and summarize selected columns and rows of data.
Allows users to summarize and analyze large datasets
Can easily reorganize data by dragging and dropping fields
Provides options to calculate sums, averages, counts, etc. for data
Helps in creating interactive reports and charts
Useful for identifying trends and patterns in data
ETL stands for Extract, Transform, Load. It is a process used in data warehousing to extract data from various sources, transform it into a consistent format, and load it into a target database.
ETL stands for Extract, Transform, Load
Extract: Involves extracting data from various sources such as databases, applications, and files
Transform: Involves cleaning, filtering, and transforming the extracted data into a consiste...
Overfitting occurs when a machine learning model learns the training data too well, including noise and outliers, leading to poor generalization on new data.
Overfitting happens when a model is too complex and captures noise in the training data.
It leads to poor performance on unseen data as the model fails to generalize well.
Techniques to prevent overfitting include cross-validation, regularization, and early stopping.
...
Overfitting occurs when a model learns the details and noise in the training data to the extent that it negatively impacts the model's performance on new data.
Overfitting happens when a model is too complex and captures noise in the training data.
It leads to poor generalization and high accuracy on training data but low accuracy on new data.
Techniques to prevent overfitting include cross-validation, regularization, and...
A pivot table in Excel is a data summarization tool that allows you to reorganize and summarize selected columns and rows of data.
Allows users to summarize and analyze large datasets
Can easily reorganize data by dragging and dropping fields
Provides options to calculate sums, averages, counts, etc. for data
Helps in creating interactive reports and charts
Useful for identifying trends and patterns in data
Forecasting problem - Predict daily sku level sales
Bias is error due to overly simplistic assumptions, variance is error due to overly complex models.
Bias is the error introduced by approximating a real-world problem, leading to underfitting.
Variance is the error introduced by modeling the noise in the training data, leading to overfitting.
High bias can cause a model to miss relevant relationships between features and target variable.
High variance can cause a model to ...
Parametric models make strong assumptions about the form of the underlying data distribution, while non-parametric models do not.
Parametric models have a fixed number of parameters, while non-parametric models have a flexible number of parameters.
Parametric models are simpler and easier to interpret, while non-parametric models are more flexible and can capture complex patterns in data.
Examples of parametric models inc...
Easy and moderate level question
Given a relevant topic and discussion on it
Reasoning and aptitude questions were asked
posted on 29 Feb 2024
I applied via Approached by Company and was interviewed before Mar 2023. There were 3 interview rounds.
based on 1 review
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