[Đánh giá] Practical Statistics for Data Scientists: 50 Essential Concepts (Peter Bruce) Tóm tắt.

Practical Statistics for Data Scientists: 50 Essential Concepts (Peter Bruce) - Amazon USA Store: https://www.amazon.com/dp/1491952962?tag=9natree-20 - Amazon Worldwide Store: https://global.buys.trade/Practical-Statistics-for-Data-Scientists%3A-50-Essential-Concepts-Peter-Bruce.html - Apple Books: https://books.apple.com/us/audiobook/data-analytics-for-beginners-a-practical-guide-to/id1255643144?itsct=books_box_link&itscg=30200&ls=1&at=1001l3bAw&ct=9natree - eBay: https://www.ebay.com/sch/i.html?_nkw=Practical+Statistics+for+Data+Scientists+50+Essential+Concepts+Peter+Bruce+&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339060787&customid=9natree&toolid=10001&mkevt=1 - Shopee: https://asia.buys.trade/shopee_Practical-Statistics-for-Data-Scientists-Peter-Bruce.html - Lazada: https://asia.buys.trade/lazada_Practical-Statistics-for-Data-Scientists-Peter-Bruce.html - Tiktok: https://asia.buys.trade/tiktok_Practical-Statistics-for-Data-Scientists-Peter-Bruce.html - Đọc thêm: https://vietnam.9natree.com/read/1491952962/ #khámphádữliệu #lấymẫungẫunhiên #kiểmđịnhAB #hồiquyvàphầndư #họckhônggiámsát #PracticalStatisticsforDataScientists Practical Statistics for Data Scientists: 50 Essential Concepts là sách hướng dẫn thống kê ứng dụng cho công việc khoa học dữ liệu, do Peter Bruce và Andrew Bruce biên soạn. Cuốn sách không đi theo kiểu giáo trình thống kê thuần túy, bắt đầu từ chứng minh toán học hay lý thuyết xác suất dài dòng. Thay vào đó, sách chọn khoảng năm mươi khái niệm có ảnh hưởng trực tiếp đến cách một người phân tích dữ liệu, xây dựng mô hình và đánh giá kết quả. Mục tiêu chính là lấp khoảng cách giữa kiến thức thống kê cơ bản với những quyết định thực tế trong quy trình dữ liệu. Nội dung trải từ khám phá dữ liệu, lấy mẫu, phân phối, kiểm định, thiết kế thí nghiệm, đến hồi quy, phân loại, học máy thống kê và học không giám sát. Bản mang ISBN này tập trung minh họa bằng R. Giá trị của sách nằm ở việc giải thích khi nào nên dùng một kỹ thuật, các giả định nào cần kiểm tra, và vì sao một chỉ số quen thuộc có thể gây hiểu nhầm nếu dùng sai bối cảnh.

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