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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-analyst-coding-interview-questions-sql-python/
课程评论：没有评论
**课程名称：** 数据分析师编码面试题 2024：SQL + Python **课程概述：** 数据分析师是一个备受青睐的职业，它充满挑战，并提供丰厚的福利。因此，数据分析师职位的竞争异常激烈，充分的准备至关重要。本课程将深入探讨 15 个以上数据分析师面试中常遇到的问题。 课程内容涵盖 SQL 和 Python DataFrame 的重要概念。我们将通过简单和复杂的示例（包括自连接）来解释 SQL JOIN 的面试题。此外，还会详细讲解常用的分析函数，如 `lead`、`lag`、`sum`、`max`、`min`、`group by`、`rollup` 和 `cube` 的应用。我们还会探讨包含 `rank` 和 `dense_rank` 等函数的棘手问题，以及如何查找 Nth 高/低值、删除重复项以及在分析报告中查找分组内的最大/最小值。 同时，课程也将涵盖 Python DataFrame 的重要操作，包括 DataFrame 的合并、连接、去重和聚合。 **课程特点：** * **无需软件安装：** 所有编码练习均可在浏览器中在线完成。 * **完全免费：** 无需购买任何实验室课程进行练习。 * **实践导向：** 课程包含大量编码练习，并强调最佳实践。 **目标受众：** 希望为数据分析师职位面试做准备的学习者。
Data data analyst career is a dream career for many, as it is an exciting job with outstanding benefits. So, the competition is also high for any data analyst job interview. All you need is the perfect preparation.This course discusses 15+ commonly asked interview questions for a data analyst job.Four important concepts from SQL and dataframe questions from Python are discussed.Sample interview questions using SQL Join is explained with simple inner and outer joins as well as with tricky self-join examples.Questions using important analytical functions such as lead, lag, sum, max, min, group by, rollup, and cube are explained with various examples.Tricky interview questions using rank and dense rank are explained.Questions such as finding the Nth high/low value, duplicate removal, and max/min within a given group in analytical reports are explained in detail.Important operations using Python dataframes are also covered. Topics such as dataframe merging, concatenation, duplicate removal, and aggregations are explained.No software setup or installation is required. All of the coding exercises can be performed online.There is no need to purchase any lab sessions to practice the coding exercise. It is completely free.Coding exercises are explained with best practices.
