Pickl AI

JUNE 5, 2025

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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Towards AI

AUGUST 16, 2023

AI engineering is the discipline focused on developing tools, systems, and processes to enable the application of artificial intelligence in real-world contexts, which combines the principles of systems engineering, software engineering, and computer science to create AI systems.

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Pickl AI

MAY 30, 2024

Understanding Data Science Data Science involves analysing and interpreting complex data sets to uncover valuable insights that can inform decision-making and solve real-world problems. You will collect and clean data from multiple sources, ensuring it is suitable for analysis.

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Dataconomy

AUGUST 16, 2023

” The answer: they craft predictive models that illuminate the future ( Image credit ) Data collection and cleaning : Data scientists kick off their journey by embarking on a digital excavation, unearthing raw data from the digital landscape.

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Pickl AI

APRIL 21, 2025

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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Big Data Big Data Data Science Machine Learning 52

Mlearning.ai

MAY 23, 2023

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.

Data Science 52

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AWS Machine Learning Blog

SEPTEMBER 12, 2023

Email classification project diagram The workflow consists of the following components: Model experimentation – Data scientists use Amazon SageMaker Studio to carry out the first steps in the data science lifecycle: exploratory data analysis (EDA), data cleaning and preparation, and building prototype models.

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