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Overview of Typical Tasks and Responsibilities in Data Science As a Data Scientist, your daily tasks and responsibilities will encompass many activities. You will collect and clean data from multiple sources, ensuring it is suitable for analysis. This step ensures that all relevant data is available in one place.
Statistical Concepts A strong understanding of statistical concepts, including probability, hypothesistesting, regression analysis, and experimental design, is paramount in Data Science roles. It forms the basis for many statistical tests and estimators used in hypothesistesting and confidence interval estimation.
R’s data manipulation capabilities make cleaning and preprocessing data easy before further analysis. · Statistical Analysis: R has a rich ecosystem of packages for statistical analysis. The caret package, for example, offers a unified interface for building and evaluating predictive models.
I conducted thorough data validation, collaborated with stakeholders to identify the root cause, and implemented corrective measures to ensure data integrity. I would perform exploratorydataanalysis to understand the distribution of customer transactions and identify potential segments.
Data Wrangling: The cleaning, transforming, and structuring of raw data into a format suitable for analysis. DecisionTrees: A supervised learning algorithm that creates a tree-like model of decisions and their possible consequences, used for both classification and regression tasks.
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