Python Coding Interview Question #1: Math in Python Image by Author Take a look at this question by Google. Link to the question:https://platform.stratascratch.com/coding/10067-google-fit-user-tracking Your task
Get ready for your Python data science interview with these essential interview questions. Learn the most important concepts and techniques in data science.
Feel free to check this reference online to answer the question. Even better, feel free to browse the web to find the full answer to the question (this will test the candidate's ability to quickly search online and find a solution to a problem without spending hours reinventing the wheel)...
1. Differentiate between Data Analytics and Data Science Aspect Data Analytics Data Science Scope Analyzing the historical data for insights and trends. Focuses on descriptive, predictive modeling, and decision-making with the data. Methods Analyses the structured data with the help of statistical ...
Pythonis a widely-used, high-level programming language that is popular for its simplicity, versatility, and ease of learning. It is used in a wide range of applications, from web development to machine learning and data science. In today’s competitive job market, having a strong understanding...
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4. How to know if a data model is performing well or not? This question is subjective, but certain simple assessment points can be used to assess the accuracy of a data model. They are as follows: A well-designed model should offer good predictability. This correlates to the ability to ...
Hands-on Time Series Anomaly Detection using Autoencoders, with Python Data Science Here’s how to use Autoencoders to detect signals with anomalies in a few lines of… Piero Paialunga August 21, 2024 12 min read Feature engineering, structuring unstructured data, and lead scoring ...
For a data engineer, most code execution is database-bound, not CPU-bound. Because of this, it makes sense to capitalize on Python’s simplicity, even at the cost of slower performance when compared to compiled languages such as C# and Java....
Stemming: Useful for tasks like text classification when speed is a priority. The technique is simpler and quicker than lemmatization, but it may sacrifice precision and produce non-real words. Lemmatization: Ideal when semantic accuracy is crucial, such as in question-answering systems or topic mo...