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Python is the most widely used programming language in data science. Proficiency in Python is essential for data manipulation, analysis, and machine learning.
Statistics: A strong understanding of statistical concepts is crucial for data analysis, hypothesis testing, and making data-driven decisions.
Spark: Apache Spark is used for big data processing and machine learning.
Data Preprocessing: Skills in data cleaning and preprocessing are necessary for handling real-world, messy data.
Having domain-specific knowledge is often required to make meaningful interpretations and recommendations based on data.
Understanding data ethics, privacy, and legal considerations is increasingly important in data science.
Proficiency in MLOps ensures that models are not just built but are scalable, maintainable, and continuously optimized for real-world deployment.
Data scientists must be able to communicate their findings effectively to both technical and non-technical stakeholders.
Data scientists need strong problem-solving skills to frame and address complex data-related issues.
Knowledge of experimental design and A/B testing is important for assessing the impact of changes based on data.
Proficiency in version control systems like Git is crucial for collaboration and tracking changes in code and data.
Familiarity with various data science tools and libraries, including Jupyter Notebook, VS Code, and more.
Skills like curiosity, critical thinking, and perseverance are valuable in data science.
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