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Skills and Tools of Data Scientists To excel in the field of Data Science, professionals need a diverse skill set, including: Programming Languages: Python, R, SQL, etc. Statistical Analysis: Hypothesistesting, probability, regression analysis, etc. Data Visualization: Matplotlib, Seaborn, Tableau, etc.
Data Cleaning and Transformation Techniques for preprocessing data to ensure quality and consistency, including handling missing values, outliers, and data type conversions. Students should learn about data wrangling and the importance of dataquality. js for creating interactive visualisations.
By visualizing data distributions, scatter plots, or heatmaps, data scientists can quickly identify outliers, clusters, or trends that might go unnoticed in raw data. This aids in detecting anomalies, understanding dataquality issues, and improving data cleaning processes.
I break down the problem into smaller manageable tasks, define clear objectives, gather relevant data, apply appropriate analytical techniques, and iteratively refine the solution based on feedback and insights. Describe a situation where you had to think creatively to solve a data-related challenge.
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