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所在平台: Udemy |
课程主页: https://www.udemy.com/course/complete-python-for-data-science-and-cloud-computing/
课程评论：没有评论
课程名称：完整的Python数据科学与云计算 课程概述：本课程时长近50小时，将全面介绍Python在数据科学和云计算领域的应用，帮助学员开启相关职业生涯。该课程是目前最为全面的指南，涉及数据科学、商业分析、统计测试与建模、数据可视化、机器学习、云计算、大数据分析及现实世界案例等内容。数据科学不仅仅是传统的IT或纯技术领域，它是一个综合性的领域，学员需要理解进行数据分析的意义，以及如何将结果部署以创造企业或自身的价值。因此，本课程不仅涵盖实际数据科学的各个方面，还包括数据工程所需的技能及在不同行业中所需的商业模型与知识。 课程内容包括： - 数据分析的Python编程，包含Python基础、Numpy数组、Pandas数据框和Scipy函数。 - 基于多个现实案例的如何收集和分析大数据，例如使用Python抓取网页数据，与平面文件、Parquet文件、SAS数据、SQLite、MongoDB和AWS上的Redshift进行交互。 - 统计学及其在不同商业用例中的应用，包括银行、风险管理、营销、定价、社交媒体、欺诈检测和客户流失及生命价值分析等使用的统计技巧。 - 机器学习算法在各类用例中的应用，由拥有超过20年经验的商业分析师和数学博士教授，关注理论根源而非仅仅是模型的调用。 - 数据可视化与统计分析案例结合，帮助学生通过图形理解数据，教学包括matplotlib、plotly、seaborn和ggplot等著名图形工具的实际案例应用。 - 多个实际项目，以复习和巩固课堂所学，例如零售产品定价、在线销售预测、推荐系统、消费者信用评分、欺诈检测和性能追踪、自然语言处理情感分析等。 - 使用Spark进行大数据分析、云计算与AWS和Azure上的机器学习，提供详细的技术解释和实际案例。 - 实践导向的学习方式，通过参与练习，巩固学习成果，课程设置了大量练习、项目和家庭作业，并附有详细解决方案。 - 专家团队提供全面的在线支持，课程将定期更新。 完成本课程后，学员将能够运用Python解决各种数据科学、机器学习、统计分析和商业问题，能够回答不同的求职面试问题，并将Python与云计算整合到完整的应用中。想要成功？加入本课程，跟随学习和实践的每一步！您将在挑战中学习，成为真正的数据科学家！
In this nearly 50 hours course, we will walk through the complete Python for starting the career in data science and cloud computing!This is so far the most comprehensive guide to mastering data science, business analytics, statistical tests & modelling, data visualization, machine learning, cloud computing, Big data analysis and real world use cases with Python. Data science career is not just a traditional IT or pure technical game - this is a comprehensive area, and above all, you must know why you conduct data analysis and how to deploy your results to generate values for the company you are working for or your own business. Therefore, this course not only covers all aspects of practical data science, but also the necessary data engineering skills and business model & knowledge you need in different industries. Whether you are working in financing, marketing, health companies, or you are running start-up, knowing the complete application of Python for data science and cloud computing is the must to achieving various business objective and looking insights into data. Yes, this complete course introduces you to a solid foundation based on the following contents and features· Python programming for data analytics, including Python fundamentals, Numpy array, Pandas Data Frames and Scipy functions.· How big data are collected and analyzed based on many real world examples. such as using Python scraping web data, communicating with flat files, parquet files, SAS data, SQLite, MongoDB and Redshift on AWS· Statistics and its application into various types of business use cases, such as the most useful statistical techniques you'll need for banking, risk, marketing, pricing, social medium, fraud detection, customers churn & life value analysis and more.· Machine learning algorithms in each use case - all necessary theories and usages for real world applications. Note, this part is taught by both business analyst and PHD mathematician with more than 20 years experience, we teach you ‘why' from the root, rather than just ‘model.fit() model.predict()' instructed in many other courses.· Data visualization combined with statistical analysis use cases to help students develop a working familiarity to understand data by graph. We will teach you how to apply all famous graphics tools such as matplotlib, plotly online and offline, seaborn and ggplot into many practical cases.· Many hands-on real world projects to review and improve what you have learned in the lectures. For example, we have provided the following typical use cases along with the business backgrounds: Pricing retail products by checking elasticity; Online sales forecasting using time course data; Recommender system by transaction segmentation; Consumer credit score system; Fraud detection and performance tracking; Natural Language Processing for sentimental analysis and more.· Spark for big data analysis, cloud computing, machine learning on AWS and Azure. We provide detailed technical explanation and real word uses cases on the real cloud environments including the specific process of system configuration. · Features for listening by doing: the best way to become an expert is to practice while learning. This course is not an exception. Not only we'll each programming codes and theories, but also need your involvement into reviewing you have learned. · Hundreds to thousands exercises, projects and homework along with detailed solutions. You can hardly find any other similar course with so many hands-on opportunities to solve so many practical problems· Our experts team will provide comprehensive online support. The course will also be on-going updated with announcement Upon completing this course, you'll be able to apply Python to solve various data science, machine learning, statistical analysis and business problems under different environments and interfaces. You can answer different job interview questions and integrate Python and cloud computing into complete applications.Want to be successful? then join this course and follow each learning-practicing step! You'll learn by doing and meet various challenges to become a real data scientist!
