Data Analysis with Python Project
1.符合資格者將於出貨後三個工作日陸續發送交易訊息通知。2.點數將於廠商出貨後,隔天起算110天後陸續確認發送。3.國際商家之商品金額及回饋點數依據將以商品未稅價格為準。4.國際商家之商品金額可能受匯率影響而有微幅差異。5.禮品卡支付以及使用未授權優惠碼不符合贈點資格。6.點數發送依據及返點上限將以「訂單總金額」計算(不含運費及稅額),不論訂單中有多少商品,於LINE購物皆視為只購買一商品(金額為當筆訂單所有商品加總金額),亦即點數回饋計算並非以coursera實際購買商品數量拆分計算 。7. 同6說明,訂單完成後的顯示金額可能包含部分運費或稅金,可返點金額將以系統回傳金額為準 8.若於商家App下單,不符合LINE購物導購資格。商品描述
The "Data Analysis Project" course empowers students to apply their knowledge and skills gained in this specialization to conduct a real-life data analysis project of their interest. Participants will explore various directions in data analysis, including supervised and unsupervised learning, regression, clustering, dimension reduction, association rules, and outlier detection. Throughout the modules, students will learn essential data analysis techniques and methodologies and embark on a journey from raw data to knowledge and intelligence. By completing the course, students will be proficient in data analysis, capable of applying their expertise in diverse projects and making data-driven decisions. By the end of this course, students will be able to: 1. Understand the fundamental concepts and methodologies of data analysis in diverse directions, including supervised and unsupervised learning, regression, clustering, dimension reduction, association rules, and outlier detection. 2. Define the scope and direction of a data analysis project, identifying appropriate techniques and methodologies for achieving project objectives. 3. Apply various classification algorithms, such as Nearest Neighbors, Decision Trees, SVM, Naive Bayes, and Logistic Regression, for predictive modeling tasks. 4. Implement cross-validation and ensemble techniques to enhance the performance and generalizability of classification models. 5. Apply regression algorithms, including Simple Linear, Polynomial Linear, and Linear with regularization, to model and predict numerical outcomes. 6. Perform multivariate regression and apply cross-validation and ensemble methods in regression analysis. 7. Explore clustering techniques, including partitioning, hierarchical, density-based, and grid-based methods, to discover underlying patterns and structures in data. 8. Apply Principal Component Analysis (PCA) for dimension reduction to simplify high-dimensional data and aid in data visualization. 9. Utilize Apriori and FPGrowth algorithms to mine association rules and discover interesting item associations within transactional data. 10. Apply outlier detection methods, including Zscore, IQR, OneClassSVM, Isolation Forest, DBSCAN, and LOF, to identify anomalous data points and contextual outliers. Throughout the course, students will actively engage in tutorials, practical exercises, and the data analysis project case study, gaining hands-on experience in diverse data analysis techniques. By achieving the learning objectives, participants will be well-equipped to excel in data analysis projects and make data-driven decisions in real-world scenarios.