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所在平台: Udemy |
课程主页: https://www.udemy.com/course/python-interview-test-quiz-in-data-science/
课程评论：没有评论
课程名称：数据科学中的Python面试测试问答 课程概述：数据科学中的Python面试测试问答旨在评估和提升您在数据科学背景下的Python编程理解。本课程非常适合准备数据科学面试或评估的人士，同时也适合希望深入了解数据科学应用中Python知识的学习者。问答覆盖广泛的主题，确保参与者充分准备应对数据科学面试中与Python相关的问题。 课程目标：通过本课程，您将能够： - 展示扎实的Python编程概念理解及其在数据科学中的应用。 - 使用Python解决复杂的数据处理、数据分析和数据可视化问题。 - 有效利用NumPy、Pandas、Matplotlib和Scikit-Learn等Python库执行各种数据科学任务。 - 理解并实现数据预处理技术、特征工程和机器学习模型。 - 自信地为专注于Python的数据科学技术面试和编码评估做好准备。 涵盖主题： - 数据科学的Python基础：变量、数据类型和操作符，控制结构（if语句、循环），函数和Lambda表达式，列表推导和生成器。 - Pandas的数据处理：数据框和系列，数据的读取与写入（CSV、Excel、SQL、JSON），数据清洗与准备，数据分组、合并与连接，处理缺失数据。 - NumPy的数值计算：NumPy数组与操作，数学和统计函数，广播和矢量化，数组的索引、切片和重塑。 - Matplotlib和Seaborn的数据可视化：创建基本图（折线图、条形图、直方图、散点图），自定义图（标题、标签、图例），高级图（热图、成对图、箱线图），样式和主题。 - 使用Scikit-Learn的机器学习：监督学习（回归、分类），无监督学习（聚类、降维），模型评估与选择，超参数调整与交叉验证。 - 数据预处理与特征工程：数据缩放与归一化，分类变量编码，处理不平衡数据集，特征选择与提取。 - 数据科学的高级Python主题：处理大数据集，使用Dask进行高效数据处理，在大数据生态系统中使用Python（PySpark），深度学习框架简介（TensorFlow，PyTorch）。 - 面试和测试准备：常见的Python数据科学面试问题，动手编码挑战，模拟面试与定时测验，技术面试和解决问题的策略提示。 课程形式： - 互动测验：通过每个模块末的测验测试知识。 - 动手编码练习：使用实际数据集和情景练习编码。 - 定时评估：以定时测试和挑战模拟面试条件。 - 讨论论坛：与同学和讲师讨论概念和解决方案。 - 模拟面试：参与模拟面试，增加信心并获得反馈。 适合人群： - 有志成为数据科学家的求职者，为技术面试或编码评估做准备。 - 需提升Python技能以进行数据分析和机器学习的数据科学专业人士。 - 希望为数据科学的Python编程打下坚实基础的学生和毕业生。 先决条件：基本的Python编程理解，建议具备数据科学基本概念的熟悉。
The Python Interview Test Quiz for Data Science is designed to evaluate and strengthen your understanding of Python programming within the context of data science. This course is ideal for individuals preparing for data science interviews or assessments, as well as for those looking to deepen their Python knowledge specifically for data science applications. The quiz covers a wide range of topics, ensuring that participants are well-prepared to tackle Python-related questions in a data science interview setting.Course Objectives:By the end of this course, you will be able to:Demonstrate a solid understanding of Python programming concepts and their application in data science.Solve complex problems involving data manipulation, data analysis, and data visualization using Python.Utilize Python libraries such as NumPy, Pandas, Matplotlib, and Scikit-Learn effectively to perform various data science tasks.Understand and implement data preprocessing techniques, feature engineering, and machine learning models in Python.Prepare confidently for technical interviews and coding assessments focusing on Python for data science.Topics Covered:Python Basics for Data Science:Variables, Data Types, and OperatorsControl Structures (if statements, loops)Functions and Lambda ExpressionsList Comprehensions and GeneratorsData Manipulation with Pandas:DataFrames and SeriesReading and Writing Data (CSV, Excel, SQL, JSON)Data Cleaning and PreparationGrouping, Merging, and Joining DataHandling Missing DataNumerical Computation with NumPy:NumPy Arrays and OperationsMathematical and Statistical FunctionsBroadcasting and VectorizationIndexing, Slicing, and Reshaping ArraysData Visualization with Matplotlib and Seaborn:Creating Basic Plots (Line, Bar, Histogram, Scatter)Customizing Plots (Titles, Labels, Legends)Advanced Plots (Heatmaps, Pair Plots, Box Plots)Styling and ThemesMachine Learning with Scikit-Learn:Supervised Learning (Regression, Classification)Unsupervised Learning (Clustering, Dimensionality Reduction)Model Evaluation and SelectionHyperparameter Tuning and Cross-ValidationData Preprocessing and Feature Engineering:Data Scaling and NormalizationEncoding Categorical VariablesHandling Imbalanced DatasetsFeature Selection and ExtractionAdvanced Python Topics for Data Science:Working with Large DatasetsEfficient Data Processing with DaskUsing Python in Big Data Ecosystems (PySpark)Introduction to Deep Learning Frameworks (TensorFlow, PyTorch)Interview and Test Preparation:Common Python Data Science Interview QuestionsHands-On Coding ChallengesMock Interviews and Timed QuizzesTips for Technical Interviews and Problem-Solving StrategiesCourse Format:Interactive Quizzes: Test your knowledge with quizzes at the end of each module.Hands-On Coding Exercises: Practice coding with real-world datasets and scenarios.Timed Assessments: Simulate interview conditions with timed tests and challenges.Discussion Forums: Engage with peers and instructors to discuss concepts and solutions.Mock Interviews: Participate in mock interviews to gain confidence and receive feedback.Who Should Enroll:Aspiring data scientists preparing for technical interviews or coding assessments.Data science professionals looking to enhance their Python skills for data analysis and machine learning.Students and graduates who want to build a strong foundation in Python programming for data science.Prerequisites:Basic understanding of Python programming.Familiarity with fundamental data science concepts is recommended but not required.
