{"id":2481,"date":"2025-11-08T19:23:42","date_gmt":"2025-11-08T12:23:42","guid":{"rendered":"https:\/\/kienthucmo.com\/?p=2481"},"modified":"2025-11-08T19:26:13","modified_gmt":"2025-11-08T12:26:13","slug":"practical-statistics-for-data-scientists-50-essential-concepts-using-r-and-python","status":"publish","type":"post","link":"https:\/\/kienthucmo.com\/vi\/practical-statistics-for-data-scientists-50-essential-concepts-using-r-and-python\/","title":{"rendered":"Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python"},"content":{"rendered":"\n

Trong k\u1ef7 nguy\u00ean m\u00e0 d\u1eef li\u1ec7u tr\u1edf th\u00e0nh \u201cng\u00f4n ng\u1eef chung\u201d c\u1ee7a th\u1ebf gi\u1edbi, vi\u1ec7c hi\u1ec3u v\u00e0 bi\u1ebft c\u00e1ch khai th\u00e1c d\u1eef li\u1ec7u kh\u00f4ng c\u00f2n l\u00e0 l\u1ee3i th\u1ebf \u2014 m\u00e0 l\u00e0 y\u00eau c\u1ea7u t\u1ed1i thi\u1ec3u. Th\u1ebf nh\u01b0ng, gi\u1eefa v\u00f4 s\u1ed1 c\u00f4ng c\u1ee5, th\u01b0 vi\u1ec7n v\u00e0 m\u00f4 h\u00ecnh m\u00e1y h\u1ecdc m\u1edbi xu\u1ea5t hi\u1ec7n m\u1ed7i ng\u00e0y, c\u00f3 m\u1ed9t k\u1ef9 n\u0103ng n\u1ec1n t\u1ea3ng v\u1eabn gi\u1eef nguy\u00ean s\u1ee9c m\u1ea1nh qua th\u1eddi gian: th\u1ed1ng k\u00ea<\/strong>. Kh\u00f4ng c\u00f3 th\u1ed1ng k\u00ea, m\u1ecdi m\u00f4 h\u00ecnh ch\u1ec9 l\u00e0 nh\u1eefng ph\u00e9p th\u1eed m\u00f9 m\u1edd; kh\u00f4ng c\u00f3 th\u1ed1ng k\u00ea, m\u1ecdi con s\u1ed1 ch\u1ec9 l\u00e0 nh\u1eefng d\u1eef li\u1ec7u r\u1eddi r\u1ea1c kh\u00f4ng mang \u00fd ngh\u0129a.<\/p>\n\n\n\n

V\u1ea5n \u0111\u1ec1 l\u00e0: th\u1ed1ng k\u00ea th\u01b0\u1eddng b\u1ecb xem nh\u01b0 m\u1ed9t b\u1ed9 m\u00f4n kh\u00f4 khan, \u0111\u1ea7y c\u00f4ng th\u1ee9c, kh\u00f3 ti\u1ebfp c\u1eadn. Nhi\u1ec1u ng\u01b0\u1eddi b\u1eaft \u0111\u1ea7u h\u1ecdc Data Science \u0111\u1ec1u v\u1ea5p ph\u1ea3i c\u1ea3m gi\u00e1c \u201ckh\u00f4ng bi\u1ebft m\u00ecnh th\u1eadt s\u1ef1 c\u1ea7n hi\u1ec3u nh\u1eefng g\u00ec\u201d, ho\u1eb7c \u201ckh\u00f4ng bi\u1ebft ph\u1ea3i b\u1eaft \u0111\u1ea7u t\u1eeb \u0111\u00e2u trong m\u1edb ki\u1ebfn th\u1ee9c r\u1ed9ng l\u1edbn n\u00e0y\u201d.<\/p>\n\n\n\n

Ch\u00ednh \u1edf kho\u1ea3ng tr\u1ed1ng \u0111\u00f3, Practical Statistics for Data Scientists<\/strong> xu\u1ea5t hi\u1ec7n nh\u01b0 m\u1ed9t c\u00e2y c\u1ea7u \u2014 k\u1ebft n\u1ed1i ng\u01b0\u1eddi h\u1ecdc v\u1edbi th\u1ed1ng k\u00ea theo h\u01b0\u1edbng th\u1ef1c t\u1ebf, d\u1ec5 hi\u1ec3u v\u00e0 tr\u1ef1c ti\u1ebfp ph\u1ee5c v\u1ee5 c\u00f4ng vi\u1ec7c ph\u00e2n t\u00edch d\u1eef li\u1ec7u. Kh\u00f4ng n\u1eb7ng n\u1ec1 l\u00fd thuy\u1ebft, kh\u00f4ng ch\u00ecm trong c\u00e1c c\u00f4ng th\u1ee9c d\u00e0i d\u00f2ng, cu\u1ed1n s\u00e1ch n\u00e0y \u0111i th\u1eb3ng v\u00e0o \u0111i\u1ec1u m\u00e0 m\u1ed9t Data Scientist c\u1ea7n: hi\u1ec3u \u0111\u00fang, d\u00f9ng \u0111\u00fang v\u00e0 \u1ee9ng d\u1ee5ng hi\u1ec7u qu\u1ea3 h\u01a1n 50 kh\u00e1i ni\u1ec7m th\u1ed1ng k\u00ea quan tr\u1ecdng nh\u1ea5t.<\/p>\n\n\n\n

N\u1ebfu b\u1ea1n \u0111ang mu\u1ed1n n\u1eafm ch\u1eafc n\u1ec1n t\u1ea3ng th\u1ed1ng k\u00ea, hi\u1ec3u s\u00e2u nh\u1eefng g\u00ec m\u00ecnh \u0111ang l\u00e0m v\u1edbi d\u1eef li\u1ec7u, ho\u1eb7c \u0111\u01a1n gi\u1ea3n ch\u1ec9 mu\u1ed1n tr\u1edf n\u00ean t\u1ef1 tin h\u01a1n khi l\u1eadp m\u00f4 h\u00ecnh, ph\u00e2n t\u00edch, tr\u1ef1c quan h\u00f3a hay \u0111\u00e1nh gi\u00e1 ch\u1ea5t l\u01b0\u1ee3ng d\u1ef1 \u0111o\u00e1n \u2014 th\u00ec \u0111\u00e2y ch\u00ednh l\u00e0 cu\u1ed1n s\u00e1ch b\u1ea1n c\u1ea7n \u0111\u1eb7t tr\u00ean b\u00e0n l\u00e0m vi\u1ec7c.<\/p>\n\n\n\n

\"Practical<\/figure>\n\n\n\n

1. Th\u00f4ng tin c\u01a1 b\u1ea3n v\u1ec1 cu\u1ed1n s\u00e1ch<\/h2>\n\n\n\n

T\u00ean s\u00e1ch:<\/strong> Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python
T\u00e1c gi\u1ea3:<\/strong> Peter Bruce, Andrew Bruce, v\u00e0 Peter Gedeck
Nh\u00e0 xu\u1ea5t b\u1ea3n:<\/strong> O\u2019Reilly Media
N\u1ed9i dung ch\u00ednh:<\/strong> Cung c\u1ea5p n\u1ec1n t\u1ea3ng th\u1ed1ng k\u00ea hi\u1ec7n \u0111\u1ea1i, th\u1ef1c ti\u1ec5n v\u00e0 d\u1ec5 \u00e1p d\u1ee5ng cho khoa h\u1ecdc d\u1eef li\u1ec7u; gi\u00fap ng\u01b0\u1eddi \u0111\u1ecdc hi\u1ec3u \u0111\u00fang \u2013 d\u00f9ng \u0111\u00fang c\u00e1c kh\u00e1i ni\u1ec7m th\u1ed1ng k\u00ea quan tr\u1ecdng trong ph\u00e2n t\u00edch v\u00e0 x\u00e2y d\u1ef1ng m\u00f4 h\u00ecnh.
Ng\u00e0y ph\u00e1t h\u00e0nh:<\/strong> Phi\u00ean b\u1ea3n \u0111\u1ea7u ti\u00ean: 2017 \u2013 Phi\u00ean b\u1ea3n th\u1ee9 hai (b\u1ea3n ph\u1ed5 bi\u1ebfn nh\u1ea5t): 2020
Gi\u1ea5y ph\u00e9p:<\/strong> B\u1ea3n th\u01b0\u01a1ng m\u1ea1i do O\u2019Reilly ph\u00e1t h\u00e0nh (b\u1ea3n PDF l\u01b0u h\u00e0nh th\u01b0\u1eddng l\u00e0 b\u1ea3n s\u1ed1 h\u00f3a \u0111\u1ec3 tham kh\u1ea3o)
S\u1ed1 trang:<\/strong> Kho\u1ea3ng 350+ trang t\u00f9y phi\u00ean b\u1ea3n
\u0110i\u1ec3m n\u1ed5i b\u1eadt:<\/strong> Tr\u00ecnh b\u00e0y h\u01a1n 50 kh\u00e1i ni\u1ec7m th\u1ed1ng k\u00ea c\u1ed1t l\u00f5i d\u01b0\u1edbi g\u00f3c nh\u00ecn th\u1ef1c t\u1ebf c\u1ee7a Data Science; minh h\u1ecda song song b\u1eb1ng R v\u00e0 Python, ph\u00f9 h\u1ee3p cho nhi\u1ec1u \u0111\u1ed1i t\u01b0\u1ee3ng; t\u1eadp trung v\u00e0o \u00fd ngh\u0129a \u2013 \u1ee9ng d\u1ee5ng \u2013 c\u00e1ch tri\u1ec3n khai, tr\u00e1nh n\u1eb7ng n\u1ec1 c\u00f4ng th\u1ee9c; m\u1ed7i ch\u01b0\u01a1ng \u0111\u1ec1u c\u00f3 v\u00ed d\u1ee5, h\u00ecnh v\u1ebd, code m\u1eabu v\u00e0 t\u00f3m t\u1eaft nhanh; ph\u00f9 h\u1ee3p c\u1ea3 ng\u01b0\u1eddi t\u1ef1 h\u1ecdc l\u1eabn s\u1eed d\u1ee5ng l\u00e0m t\u00e0i li\u1ec7u gi\u1ea3ng d\u1ea1y.
Practical Statistics for Data Scientists kh\u00f4ng ch\u1ec9 l\u00e0 m\u1ed9t gi\u00e1o tr\u00ecnh th\u1ed1ng k\u00ea truy\u1ec1n th\u1ed1ng. Cu\u1ed1n s\u00e1ch \u0111\u01b0\u1ee3c thi\u1ebft k\u1ebf \u0111\u1ec3 \u0111\u00e1p \u1ee9ng nhu c\u1ea7u h\u1ecdc t\u1eadp c\u1ee7a th\u1eddi \u0111\u1ea1i d\u1eef li\u1ec7u: h\u1ecdc th\u1ef1c chi\u1ebfn, h\u1ecdc nhanh, h\u1ecdc th\u00f4ng qua v\u00ed d\u1ee5, v\u00e0 h\u1ecdc v\u1edbi kh\u1ea3 n\u0103ng \u00e1p d\u1ee5ng v\u00e0o d\u1ef1 \u00e1n th\u1ef1c t\u1ebf ngay l\u1eadp t\u1ee9c.<\/p>\n\n\n\n

2. T\u00f3m t\u1eaft s\u01a1 l\u01b0\u1ee3c n\u1ed9i dung<\/h2>\n\n\n\n

Cu\u1ed1n Practical Statistics for Data Scientists<\/em> bao g\u1ed3m h\u01a1n 50 kh\u00e1i ni\u1ec7m th\u1ed1ng k\u00ea quan tr\u1ecdng m\u00e0 b\u1ea5t k\u1ef3 ai l\u00e0m vi\u1ec7c v\u1edbi d\u1eef li\u1ec7u c\u0169ng c\u1ea7n n\u1eafm. M\u1ed7i ch\u01b0\u01a1ng \u0111\u1ec1u \u0111\u01b0\u1ee3c tr\u00ecnh b\u00e0y theo c\u00e1ch r\u1ea5t d\u1ec5 ti\u1ebfp c\u1eadn: gi\u1ea3i th\u00edch r\u00f5 r\u00e0ng, c\u00f3 v\u00ed d\u1ee5 tr\u1ef1c quan, k\u00e8m m\u00e3 R\/Python v\u00e0 \u1ee9ng d\u1ee5ng th\u1ef1c t\u1ebf, n\u00ean b\u1ea1n c\u00f3 th\u1ec3 hi\u1ec3u v\u00e0 \u00e1p d\u1ee5ng ngay.<\/p>\n\n\n\n

Ch\u01b0\u01a1ng 1 \u2013 Exploratory Data Analysis (EDA)<\/h3>\n\n\n\n

Ch\u01b0\u01a1ng n\u00e0y gi\u1ed1ng nh\u01b0 b\u01b0\u1edbc \u201cl\u00e0m quen\u201d v\u1edbi d\u1eef li\u1ec7u. B\u1ea1n s\u1ebd h\u1ecdc c\u00e1ch xem d\u1eef li\u1ec7u d\u1ea1ng b\u1ea3ng, ph\u00e2n lo\u1ea1i c\u00e1c ki\u1ec3u bi\u1ebfn (li\u00ean t\u1ee5c, r\u1eddi r\u1ea1c, ph\u00e2n lo\u1ea1i), nh\u1eadn bi\u1ebft d\u1eef li\u1ec7u l\u1ec7ch hay c\u00f3 outlier. C\u00e1c ph\u00e9p t\u00ednh c\u01a1 b\u1ea3n nh\u01b0 mean, median, IQR, MAD \u0111\u01b0\u1ee3c gi\u1ea3i th\u00edch b\u1eb1ng nh\u1eefng v\u00ed d\u1ee5 d\u1ec5 hi\u1ec3u. Ngo\u00e0i ra, b\u1ea1n c\u0169ng s\u1ebd l\u00e0m quen v\u1edbi histogram, boxplot, density plot \u2013 nh\u1eefng c\u00f4ng c\u1ee5 c\u1ef1c k\u1ef3 quan tr\u1ecdng \u0111\u1ec3 nh\u00ecn nhanh c\u1ea5u tr\u00fac d\u1eef li\u1ec7u.<\/p>\n\n\n\n

Ch\u01b0\u01a1ng 2 \u2013 Data and Sampling Distributions<\/h3>\n\n\n\n

\u0110\u00e2y l\u00e0 ch\u01b0\u01a1ng gi\u00fap b\u1ea1n hi\u1ec3u t\u1ea1i sao ch\u00fang ta c\u00f3 th\u1ec3 d\u00f9ng m\u1ed9t m\u1eabu nh\u1ecf \u0111\u1ec3 suy ra c\u1ea3 qu\u1ea7n th\u1ec3. T\u00e1c gi\u1ea3 gi\u1ea3i th\u00edch c\u00e1c kh\u00e1i ni\u1ec7m nh\u01b0 sampling, CLT (\u0111\u1ecbnh l\u00fd gi\u1edbi h\u1ea1n trung t\u00e2m) hay standard error theo c\u00e1ch r\u1ea5t nh\u1eb9 nh\u00e0ng. \u0110\u00e2y l\u00e0 n\u1ec1n t\u1ea3ng cho vi\u1ec7c x\u00e2y d\u1ef1ng m\u00f4 h\u00ecnh v\u00e0 \u0111\u01b0a ra k\u1ebft lu\u1eadn c\u00f3 \u0111\u1ed9 tin c\u1eady.<\/p>\n\n\n\n

Ch\u01b0\u01a1ng 3 \u2013 Statistical Experiments & Significance Testing<\/h3>\n\n\n\n

Ch\u01b0\u01a1ng n\u00e0y n\u00f3i v\u1ec1 A\/B testing, p-value, t-test, chi-square v\u00e0 nh\u1eefng ph\u00e9p ki\u1ec3m \u0111\u1ecbnh ph\u1ed5 bi\u1ebfn. T\u00e1c gi\u1ea3 gi\u00fap b\u1ea1n hi\u1ec3u c\u00e1ch thi\u1ebft k\u1ebf th\u00ed nghi\u1ec7m sao cho \u0111\u00e1ng tin, tr\u00e1nh sai l\u1ec7ch, v\u00e0 \u0111\u1eb7c bi\u1ec7t l\u00e0 c\u00e1ch di\u1ec5n gi\u1ea3i p-value \u0111\u00fang ngh\u0129a \u2013 \u0111i\u1ec1u m\u00e0 r\u1ea5t nhi\u1ec1u ng\u01b0\u1eddi d\u1ec5 m\u1eafc sai l\u1ea7m.<\/p>\n\n\n\n

Ch\u01b0\u01a1ng 4 \u2013 Regression & Prediction<\/h3>\n\n\n\n

N\u1ebfu b\u1ea1n t\u1eebng nghe \u201ch\u1ed3i quy tuy\u1ebfn t\u00ednh\u201d nh\u01b0ng ch\u01b0a hi\u1ec3u r\u00f5 b\u1ea3n ch\u1ea5t, ch\u01b0\u01a1ng n\u00e0y s\u1ebd gi\u00fap b\u1ea1n s\u00e1ng t\u1ecf. T\u00e1c gi\u1ea3 n\u00f3i v\u1ec1 c\u00e1c gi\u1ea3 \u0111\u1ecbnh quan tr\u1ecdng, c\u00e1ch ki\u1ec3m tra sai s\u1ed1 (residuals), multicollinearity, c\u00e1ch \u0111\u00e1nh gi\u00e1 m\u00f4 h\u00ecnh\u2026 T\u1ea5t c\u1ea3 \u0111\u1ec1u \u0111\u01b0\u1ee3c minh h\u1ecda b\u1eb1ng v\u00ed d\u1ee5 th\u1ef1c t\u1ebf, n\u00ean r\u1ea5t d\u1ec5 h\u00ecnh dung.<\/p>\n\n\n\n

Ch\u01b0\u01a1ng 5 \u2013 Classification<\/h3>\n\n\n\n

\u0110\u1ebfn \u0111\u00e2y, b\u1ea1n s\u1ebd b\u01b0\u1edbc v\u00e0o th\u1ebf gi\u1edbi c\u1ee7a ph\u00e2n lo\u1ea1i, v\u1edbi logistic regression, LDA, na\u00efve Bayes\u2026 Ngo\u00e0i m\u00f4 h\u00ecnh, cu\u1ed1n s\u00e1ch c\u00f2n h\u01b0\u1edbng d\u1eabn c\u00e1ch \u0111\u00e1nh gi\u00e1 nh\u01b0 ROC curve, AUC, F1-score v\u00e0 c\u00e1ch x\u1eed l\u00fd d\u1eef li\u1ec7u m\u1ea5t c\u00e2n b\u1eb1ng \u2013 nh\u1eefng v\u1ea5n \u0111\u1ec1 r\u1ea5t hay g\u1eb7p khi l\u00e0m vi\u1ec7c th\u1ef1c t\u1ebf.<\/p>\n\n\n\n

Ch\u01b0\u01a1ng 6 \u2013 Statistical Machine Learning<\/h3>\n\n\n\n

\u0110\u00e2y l\u00e0 ph\u1ea7n m\u00e0 nhi\u1ec1u ng\u01b0\u1eddi y\u00eau th\u00edch v\u00ec t\u00e1c gi\u1ea3 gi\u1ea3i th\u00edch c\u00e1c kh\u00e1i ni\u1ec7m quan tr\u1ecdng nh\u01b0 regularization, bias\u2013variance, c\u00f9ng c\u00e1c m\u00f4 h\u00ecnh nh\u01b0 c\u00e2y quy\u1ebft \u0111\u1ecbnh, random forest v\u00e0 boosting. C\u00e1ch tr\u00ecnh b\u00e0y \u0111\u01a1n gi\u1ea3n khi\u1ebfn b\u1ea1n hi\u1ec3u \u201cm\u00f4 h\u00ecnh n\u00e0y d\u00f9ng khi n\u00e0o\u201d m\u00e0 kh\u00f4ng b\u1ecb ng\u1ee3p b\u1edfi l\u00fd thuy\u1ebft.<\/p>\n\n\n\n

Ch\u01b0\u01a1ng 7 \u2013 Unsupervised Learning<\/h3>\n\n\n\n

Ch\u01b0\u01a1ng n\u00e0y bao g\u1ed3m clustering (k-means, hierarchical) v\u00e0 PCA. B\u1ea1n s\u1ebd bi\u1ebft v\u00ec sao c\u1ea7n chu\u1ea9n h\u00f3a d\u1eef li\u1ec7u, c\u00e1ch ch\u1ecdn s\u1ed1 c\u1ee5m h\u1ee3p l\u00fd, ho\u1eb7c c\u00e1ch PCA gi\u00fap gi\u1ea3m nhi\u1ec5u v\u00e0 tr\u1ef1c quan h\u00f3a d\u1eef li\u1ec7u t\u1ed1t h\u01a1n.<\/p>\n\n\n\n

T\u1ed5ng k\u1ebft:<\/strong>
M\u1ed7i ch\u01b0\u01a1ng \u0111\u1ec1u theo m\u1ed9t m\u1ea1ch r\u1ea5t d\u1ec5 theo: gi\u1ea3i th\u00edch \u2192 v\u00ed d\u1ee5 \u2192 code \u2192 \u1ee9ng d\u1ee5ng \u2192 t\u00f3m t\u1eaft nhanh<\/em>. \u0110i\u1ec1u n\u00e0y khi\u1ebfn cu\u1ed1n s\u00e1ch tr\u1edf th\u00e0nh t\u00e0i li\u1ec7u c\u1ef1c k\u1ef3 ph\u00f9 h\u1ee3p cho ng\u01b0\u1eddi m\u1edbi b\u01b0\u1edbc v\u00e0o data science ho\u1eb7c nh\u1eefng ai mu\u1ed1n c\u1ee7ng c\u1ed1 l\u1ea1i n\u1ec1n t\u1ea3ng m\u1ed9t c\u00e1ch nh\u1eb9 nh\u00e0ng m\u00e0 v\u1eabn \u0111\u1ea7y \u0111\u1ee7.<\/p>\n\n\n\n

3. Cu\u1ed1n s\u00e1ch n\u00e0y d\u00e0nh cho ai?<\/h2>\n\n\n\n

Cu\u1ed1n Practical Statistics for Data Scientists<\/em> ph\u00f9 h\u1ee3p v\u1edbi r\u1ea5t nhi\u1ec1u nh\u00f3m ng\u01b0\u1eddi \u0111\u1ecdc, \u0111\u1eb7c bi\u1ec7t l\u00e0 nh\u1eefng ai \u0111ang mu\u1ed1n x\u00e2y d\u1ef1ng n\u1ec1n t\u1ea3ng th\u1ed1ng k\u00ea v\u1eefng ch\u1eafc cho khoa h\u1ecdc d\u1eef li\u1ec7u.<\/p>\n\n\n\n

Ng\u01b0\u1eddi m\u1edbi h\u1ecdc Data Science<\/strong>
\u0110\u00e2y l\u00e0 nh\u00f3m \u0111\u1ed1i t\u01b0\u1ee3ng ch\u00ednh m\u00e0 cu\u1ed1n s\u00e1ch h\u01b0\u1edbng t\u1edbi. C\u00e1c kh\u00e1i ni\u1ec7m th\u1ed1ng k\u00ea \u0111\u01b0\u1ee3c tr\u00ecnh b\u00e0y theo c\u00e1ch d\u1ec5 hi\u1ec3u, \u0111i k\u00e8m v\u00ed d\u1ee5 th\u1ef1c t\u1ebf, gi\u00fap ng\u01b0\u1eddi m\u1edbi kh\u00f4ng b\u1ecb cho\u00e1ng ng\u1ee3p b\u1edfi l\u00fd thuy\u1ebft hay c\u00f4ng th\u1ee9c.<\/p>\n\n\n\n

Nh\u1eefng ai \u0111\u00e3 bi\u1ebft Python ho\u1eb7c R v\u00e0 mu\u1ed1n c\u1ee7ng c\u1ed1 th\u1ed1ng k\u00ea<\/strong>
N\u1ebfu b\u1ea1n \u0111\u00e3 quen v\u1edbi pandas, NumPy hay scikit-learn nh\u01b0ng c\u1ea3m th\u1ea5y thi\u1ebfu n\u1ec1n t\u1ea3ng th\u1ed1ng k\u00ea \u0111\u1ec3 th\u1ef1c s\u1ef1 hi\u1ec3u m\u00f4 h\u00ecnh ho\u1ea1t \u0111\u1ed9ng ra sao, cu\u1ed1n s\u00e1ch n\u00e0y s\u1ebd gi\u00fap b\u1ea1n l\u1ea5p \u0111\u1ea7y kho\u1ea3ng tr\u1ed1ng \u0111\u00f3.<\/p>\n\n\n\n

Sinh vi\u00ean ng\u00e0nh d\u1eef li\u1ec7u, AI, to\u00e1n \u2013 th\u1ed1ng k\u00ea<\/strong>
N\u1ed9i dung trong s\u00e1ch \u0111\u01b0\u1ee3c tr\u00ecnh b\u00e0y theo h\u01b0\u1edbng th\u1ef1c h\u00e0nh, hi\u1ec7n \u0111\u1ea1i v\u00e0 s\u00e1t v\u1edbi nhu c\u1ea7u c\u1ee7a ng\u00e0nh c\u00f4ng nghi\u1ec7p, ph\u00f9 h\u1ee3p \u0111\u1ec3 b\u1ed5 sung ho\u1eb7c n\u00e2ng c\u1ea5p so v\u1edbi ki\u1ebfn th\u1ee9c h\u1ecdc thu\u1eadt truy\u1ec1n th\u1ed1ng.<\/p>\n\n\n\n

Data Analyst mu\u1ed1n n\u00e2ng c\u1ea5p l\u00ean Data Scientist<\/strong>
Cu\u1ed1n s\u00e1ch \u0111\u1eb7c bi\u1ec7t h\u1eefu \u00edch n\u1ebfu b\u1ea1n \u0111ang g\u1eb7p kh\u00f3 kh\u0103n v\u1edbi c\u00e1c kh\u00e1i ni\u1ec7m nh\u01b0 sampling, \u0111\u1ed9 tin c\u1eady, A\/B testing hay c\u00e1c ph\u01b0\u01a1ng ph\u00e1p \u0111\u00e1nh gi\u00e1 m\u00f4 h\u00ecnh.<\/p>\n\n\n\n

Ng\u01b0\u1eddi l\u00e0m marketing, product ho\u1eb7c business<\/strong>
Ngay c\u1ea3 khi kh\u00f4ng ph\u1ea3i l\u1eadp tr\u00ecnh vi\u00ean, b\u1ea1n v\u1eabn c\u00f3 th\u1ec3 hi\u1ec3u ph\u1ea7n l\u1edbn n\u1ed9i dung s\u00e1ch. C\u00e1c kh\u00e1i ni\u1ec7m \u0111\u01b0\u1ee3c gi\u1ea3i th\u00edch b\u1eb1ng v\u00ed d\u1ee5 tr\u1ef1c quan, gi\u00fap b\u1ea1n \u0111\u1ecdc hi\u1ec3u b\u00e1o c\u00e1o, \u0111\u00e1nh gi\u00e1 d\u1eef li\u1ec7u v\u00e0 \u0111\u01b0a ra quy\u1ebft \u0111\u1ecbnh ch\u00ednh x\u00e1c h\u01a1n.<\/p>\n\n\n\n

Engineer v\u00e0 developer mu\u1ed1n b\u01b0\u1edbc v\u00e0o l\u0129nh v\u1ef1c Machine Learning<\/strong>
\u0110\u1ed1i v\u1edbi l\u1eadp tr\u00ecnh vi\u00ean \u0111ang mu\u1ed1n chuy\u1ec3n h\u01b0\u1edbng sang ML ho\u1eb7c AI, \u0111\u00e2y l\u00e0 cu\u1ed1n s\u00e1ch n\u1ec1n t\u1ea3ng \u0111\u1ec3 hi\u1ec3u \u0111\u00fang b\u1ea3n ch\u1ea5t th\u1ed1ng k\u00ea tr\u01b0\u1edbc khi h\u1ecdc \u0111\u1ebfn thu\u1eadt to\u00e1n n\u00e2ng cao h\u01a1n.<\/p>\n\n\n\n

4. V\u00ec sao b\u1ea1n n\u00ean \u0111\u1ecdc cu\u1ed1n s\u00e1ch n\u00e0y?<\/h2>\n\n\n\n

C\u00f3 r\u1ea5t nhi\u1ec1u s\u00e1ch v\u1ec1 th\u1ed1ng k\u00ea, nh\u01b0ng Practical Statistics for Data Scientists<\/em> n\u1ed5i b\u1eadt nh\u1edd c\u00e1ch ti\u1ebfp c\u1eadn r\u1ea5t th\u1ef1c ti\u1ec5n v\u00e0 ph\u00f9 h\u1ee3p cho nh\u1eefng ai l\u00e0m vi\u1ec7c v\u1edbi d\u1eef li\u1ec7u.<\/p>\n\n\n\n

Kh\u00f4ng sa \u0111\u00e0 v\u00e0o to\u00e1n h\u1ecdc ph\u1ee9c t\u1ea1p<\/strong>
Thay v\u00ec t\u1eadp trung v\u00e0o c\u00f4ng th\u1ee9c, s\u00e1ch gi\u1ea3i th\u00edch r\u00f5 kh\u00e1i ni\u1ec7m d\u00f9ng \u0111\u1ec3 l\u00e0m g\u00ec, khi n\u00e0o n\u00ean \u00e1p d\u1ee5ng, khi n\u00e0o c\u1ea7n tr\u00e1nh v\u00e0 c\u00e1c l\u1ed7i th\u01b0\u1eddng g\u1eb7p. M\u1ecdi ph\u1ea7n \u0111\u1ec1u \u0111i k\u00e8m v\u00ed d\u1ee5 v\u00e0 m\u00e3 R\/Python, gi\u00fap b\u1ea1n hi\u1ec3u b\u1ea3n ch\u1ea5t v\u00e0 s\u1eed d\u1ee5ng \u0111\u00fang trong th\u1ef1c t\u1ebf.<\/p>\n\n\n\n

\u1ee8ng d\u1ee5ng ngay v\u00e0o c\u00f4ng vi\u1ec7c<\/strong>
C\u00e1c v\u00ed d\u1ee5 \u0111\u1ec1u \u0111\u1ebfn t\u1eeb c\u00e1c b\u00e0i to\u00e1n \u0111\u1eddi th\u1ef1c nh\u01b0 ph\u00e2n t\u00edch d\u00e2n s\u1ed1, \u0111\u00e1nh gi\u00e1 d\u1eef li\u1ec7u ti\u1ec3u bang, m\u00f4 h\u00ecnh h\u1ed3i quy hay ph\u00e2n lo\u1ea1i. Nh\u1edd v\u1eady, n\u1ed9i dung kh\u00f4ng h\u1ec1 kh\u00f4 khan v\u00e0 r\u1ea5t d\u1ec5 chuy\u1ec3n th\u00e0nh k\u1ef9 n\u0103ng th\u1ef1c h\u00e0nh.<\/p>\n\n\n\n

H\u1ed7 tr\u1ee3 c\u1ea3 R v\u00e0 Python<\/strong>
\u0110i\u1ec3m \u0111\u1eb7c bi\u1ec7t c\u1ee7a cu\u1ed1n s\u00e1ch l\u00e0 tr\u00ecnh b\u00e0y song song hai ng\u00f4n ng\u1eef ph\u1ed5 bi\u1ebfn nh\u1ea5t trong l\u0129nh v\u1ef1c d\u1eef li\u1ec7u, gi\u00fap ng\u01b0\u1eddi \u0111\u1ecdc d\u1ec5 so s\u00e1nh c\u00e1ch l\u00e0m v\u00e0 ch\u1ecdn c\u00f4ng c\u1ee5 ph\u00f9 h\u1ee3p.<\/p>\n\n\n\n

C\u00e1ch gi\u1ea3i th\u00edch \u0111\u00fang tinh th\u1ea7n \u201ckhoa h\u1ecdc d\u1eef li\u1ec7u\u201d<\/strong>
T\u00e1c gi\u1ea3 kh\u00f4ng ch\u1ec9 n\u00f3i \u201cmean l\u00e0 trung b\u00ecnh\u201d, m\u00e0 gi\u1ea3i th\u00edch th\u00eam mean d\u1ec5 b\u1ecb \u1ea3nh h\u01b0\u1edfng b\u1edfi outlier; IQR t\u1ed1t h\u01a1n range trong d\u1eef li\u1ec7u nhi\u1ec5u; hay t\u1ea1i sao MAD l\u00e0 l\u1ef1a ch\u1ecdn m\u1ea1nh m\u1ebd h\u01a1n trong nhi\u1ec1u tr\u01b0\u1eddng h\u1ee3p. Ng\u01b0\u1eddi \u0111\u1ecdc kh\u00f4ng ch\u1ec9 bi\u1ebft kh\u00e1i ni\u1ec7m m\u00e0 c\u00f2n bi\u1ebft c\u00e1ch \u00e1p d\u1ee5ng ch\u00ednh x\u00e1c.<\/p>\n\n\n\n

Ph\u00f9 h\u1ee3p cho ph\u1ecfng v\u1ea5n v\u00e0 c\u00f4ng vi\u1ec7c th\u1ef1c t\u1ebf<\/strong>
H\u1ea7u nh\u01b0 m\u1ecdi c\u00e2u h\u1ecfi th\u1ed1ng k\u00ea c\u01a1 b\u1ea3n trong ph\u1ecfng v\u1ea5n Data Science\u2014bias v\u00e0 variance, p-value, multicollinearity, overfitting, underfitting hay \u0111\u00e1nh gi\u00e1 m\u00f4 h\u00ecnh\u2014\u0111\u1ec1u \u0111\u01b0\u1ee3c tr\u00ecnh b\u00e0y r\u00f5 r\u00e0ng trong s\u00e1ch.<\/p>\n\n\n\n

T\u00f3m g\u1ecdn nh\u01b0ng bao qu\u00e1t<\/strong>
Cu\u1ed1n s\u00e1ch nh\u1ecf nh\u01b0ng bao ph\u1ee7 to\u00e0n b\u1ed9 n\u1ec1n t\u1ea3ng th\u1ed1ng k\u00ea c\u1ed1t l\u00f5i c\u1ee7a Data Science, gi\u00fap ng\u01b0\u1eddi \u0111\u1ecdc h\u1ecdc t\u1eadp c\u00f3 h\u1ec7 th\u1ed1ng thay v\u00ec t\u00ecm hi\u1ec3u r\u1eddi r\u1ea1c.<\/p>\n\n\n\n

5. T\u1ea3i xu\u1ed1ng, tr\u1ea3i nghi\u1ec7m<\/h2>\n\n\n\n

B\u1ea1n c\u00f3 th\u1ec3 d\u1ec5 d\u00e0ng t\u1ea3i xu\u1ed1ng ho\u1eb7c \u0111\u1ecdc tr\u1ef1c tuy\u1ebfn cu\u1ed1n s\u00e1ch n\u00e0y tr\u00ean nhi\u1ec1u n\u1ec1n t\u1ea3ng kh\u00e1c nhau nh\u01b0 SlideShare, Scribd, Issuu hay Studylid. M\u1ed7i n\u1ec1n t\u1ea3ng \u0111\u1ec1u h\u1ed7 tr\u1ee3 \u0111\u1ecdc tr\u1ef1c ti\u1ebfp, l\u01b0u l\u1ea1i \u0111\u1ec3 xem sau v\u00e0 t\u1ea3i v\u1ec1 khi c\u1ea7n, r\u1ea5t ti\u1ec7n cho c\u1ea3 m\u00e1y t\u00ednh l\u1eabn \u0111i\u1ec7n tho\u1ea1i. H\u00e3y ch\u1ecdn n\u01a1i ph\u00f9 h\u1ee3p nh\u1ea5t v\u1edbi th\u00f3i quen s\u1eed d\u1ee5ng c\u1ee7a b\u1ea1n \u0111\u1ec3 tr\u1ea3i nghi\u1ec7m tr\u1ecdn v\u1eb9n n\u1ed9i dung cu\u1ed1n s\u00e1ch.<\/p>\n\n\n\n