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Data Scientist Job Description – What Companies Look For in 2025

Pickl AI

Key Responsibilities of a Data Scientist in India While the core responsibilities align with global standards, Indian data scientists often face unique challenges and opportunities shaped by the local market: Data Acquisition and Cleaning: Extracting data from diverse sources including legacy systems, cloud platforms, and third-party APIs.

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Your Complete Roadmap to Become an Azure Data Scientist

Pickl AI

Summary: This blog provides a comprehensive roadmap for aspiring Azure Data Scientists, outlining the essential skills, certifications, and steps to build a successful career in Data Science using Microsoft Azure. What is Azure?

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Big Data vs. Data Science: Demystifying the Buzzwords

Pickl AI

Exploring the Ocean If Big Data is the ocean, Data Science is the multifaceted discipline of extracting knowledge and insights from data, whether it’s big or small. It’s an interdisciplinary field that blends statistics, computer science, and domain expertise to understand phenomena through data analysis.

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Data Science Career FAQs Answered: Educational Background

Mlearning.ai

Blind 75 LeetCode Questions - LeetCode Discuss Data Manipulation and Analysis Proficiency in working with data is crucial. This includes skills in data cleaning, preprocessing, transformation, and exploratory data analysis (EDA). in these fields.

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The Data Dilemma: Exploring the Key Differences Between Data Science and Data Engineering

Pickl AI

Their primary responsibilities include: Data Collection and Preparation Data Scientists start by gathering relevant data from various sources, including databases, APIs, and online platforms. They clean and preprocess the data to remove inconsistencies and ensure its quality. ETL Tools: Apache NiFi, Talend, etc.

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Artificial Intelligence Using Python: A Comprehensive Guide

Pickl AI

Natural Language Processing (NLP) This is a field of computer science that deals with the interaction between computers and human language. NLP tasks include machine translation, speech recognition, and sentiment analysis. Feature Engineering : Creating or transforming new features to enhance model performance.