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[云计算] 10套 国外顶级 数据分析视频 全英文：
课程介绍:
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047_Model Thinking_模型思维\/ {* O# A# y’ J
data analysis and statistical inference\
Data Visualization\
Dino 101 Dinosaur Paleobiology\6 | l n3 {0 t” U1 o
Getting and Cleaning Data\* i7 C” a$ c, R* e6 b( Z( C- J’ J$ c
Mining Massive Datasets\
Model Thinking _ Scott Page\
modelthinkingzh-001\7 v’ r: f” e0 [! ^& q1 D
R Programming\9 E* C8 d6 Y9 ~. L4 L- M
Stanford Statistical Learning 2014\
The Data Scientist’s Toolbox\6 V( H; \5 _0 {
详细目录:) W; K A’ f. Y/ Z
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├─047_Model Thinking_模型思维# i+ a3 b% [2 F
│ ├─Model Thinking$ W’ i” g7 s! v1 w& l’ B
│ │ 1 – 1 – Why Model (853).mp42 C0 }( z/ O1 W# V# O, V8 }/ i
│ │ 1 – 2 – Intelligent Citizens of the World (1131).mp4
│ │ 1 – 3 – Thinking More Clearly (1050).mp4 E+ L. G2 B( k* k0 |5 `& q
│ │ 1 – 4 – Using and Understanding Data (1014).mp40 J( Y’ n* ^( B2 u
│ │ 1 – 5 – Using Models to Decide Strategize and Design (1526).mp4
│ │ 10 – 1 – Markov Models (426).mp4
│ │ 10 – 2 – A Simple Markov Model (1127).mp45 I# h* `4 H0 u7 c
│ │ 10 – 3 – Markov Model of Democratization (821).mp47 j, M2 q# I( g2 m& U% N
│ │ 10 – 4 – Markov Convergence Theorem (1033).mp4” E; l& x9 ?, t
│ │ 10 – 5 – Exapting the Markov Model (1011).mp4
│ │ 11 – 1 – Lyapunov Functions (913).mp4; V; |3 L6 @- t1 \’ v$ U’ w
│ │ 11 – 2 – The Organization of Cities (1214).mp4
│ │ 11 – 3 – Exchange Economies and Externalities (918).mp4! n8 ~- H0 }& ~, k7 V! B
│ │ 11 – 4 – Time to Convergence and Optimality (804).mp44 ^ F* Z8 l% ~7 G- L. R# |2 b0 o
│ │ 11 – 5 – Lyapunov Fun and Deep (840).mp4
│ │ 11 – 6 – Lyapunov or Markov (724).mp4
│ │ 12 – 1 – Coordination and Culture (337).mp44 K: {+ y @1 ?” b6 v* {
│ │ 12 – 2 – What Is Culture And Why Do We Care (1543).mp4
│ │ 12 – 3 – Pure Coordination Game (1348).mp4
│ │ 12 – 4 – Emergence of Culture (1101).mp4. \, g8 g3 A/ w$ [
│ │ 12 – 5 – Coordination and Consistency (1703).mp4! c+ x i9 d d9 H( \
│ │ 13 – 1 – Path Dependence (723).mp4
│ │ 13 – 2 – Urn Models (1626).mp48 C7 V2 Q$ G/ {5 B( {
│ │ 13 – 3 – Mathematics on Urn Models (1446).mp49 x3 A$ g. y5 Z+ ^: w1 w” v7 g! k
│ │ 13 – 4 – Path Dependence and Chaos (1108).mp4! F: z4 O5 G8 T6 s
│ │ 13 – 5 – Path Dependence and Increasing Returns (1231).mp4
│ │ 13 – 6 – Path Dependent or Tipping Point (952).mp4
│ │ 14 – 1 – Networks (704).mp4‘ l2 D1 d, {- [2 ?+ k5 Z2 M8 [
│ │ 14 – 2 – The Structure of Networks (1930).mp4
│ │ 14 – 3 – The Logic of Network Formation (1003).mp4
│ │ 14 – 4 – Network Function (1310).mp4
│ │ 15 – 1 – Randomness and Random Walk Models (305).mp4
│ │ 15 – 2 – Sources of Randomness (515).mp4: Z& ^) q” I6 I* \
│ │ 15 – 3 – Skill and Luck (828).mp4
│ │ 15 – 4 – Random Walks (1229).mp4– Y7 u1 w% F/ X* s( W3 @’ Q/ Z
│ │ 15 – 5 – Random Walks and Wall Street (751).mp47 T, X1 K3 B# L
│ │ 15 – 6 – FInite Memory Random Walks (818).mp4$ \” X6 h3 N+ w; V
│ │ 16 – 1 – Colonel Blotto Game (153).mp4
│ │ 16 – 2 – Blotto No Best Strategy (727).mp4
│ │ 16 – 3 – Applications of Colonel Blotto (708).mp4! J9 A& t: R% j* x! ~. g, Q
│ │ 16 – 4 – Blotto Troop Advantages (627).mp4
│ │ 16 – 5 – Blotto and Competition (1041).mp4. e9 e4 r- ]; V9 X$ n# `1 G6 M
│ │ 17 – 1 – Intro The Prisoners Dilemma and Collective Action (344).mp4
│ │ 17 – 2 – The Prisoners Dilemma Game (1345).mp4
│ │ 17 – 3 – Seven Ways To Cooperation (1520).mp4” r/ z7 X N+ E$ O2 r/ Q
│ │ 17 – 4 – Collective Action and Common Pool Resource Problems (723).mp4
│ │ 17 – 5 – No Panacea (603).mp4
│ │ 18 – 1 – Mechanism Design (400).mp4) q3 U; ~, I. f4 `
│ │ 18 – 2 – Hidden Action and Hidden Information (953).mp4
│ │ 18 – 3 – Auctions (1959).mp4
│ │ 18 – 4 – Public Projects (1221).mp4# Z4 T9 K3 k& d+ z6 P
│ │ 19 – 1 – Replicator Dynamics (437).mp4/ {- C8 N0 N1 h- ~+ J” Y8 C
│ │ 19 – 2 – The Replicator Equation (1329).mp4
│ │ 19 – 3 – Fishers Theorem (1157).mp4
│ │ 19 – 4 – Variation or Six Sigma (539).mp4
│ │ 2 – 1 – Sorting and Peer Effects Introduction (511).mp4
│ │ 2 – 2 – Schellings Segregation Model (1130) (1).mp4‘ b* o3 r6 b. U) r6 V
│ │ 2 – 3 – Measuring Segregation (1130).mp4% j6 f# J. d4 V; E. T
│ │ 2 – 4 – Peer Effects (658).mp4
│ │ 2 – 5 – The Standing Ovation Model (1805).mp4
│ │ 2 – 6 – The Identification Problem (1018).mp4 o- B% \” ~& E+ w. q1 M7 r
│ │ 20 – 1 – Prediction (225).mp4% O, `% b$ _( [” w( s
│ │ 20 – 2 – Linear Models (502).mp4
│ │ 20 – 3 – Diversity Prediction Theorem (1154).mp4
│ │ 20 – 4 – The Many Model Thinker (711).mp4
│ │ 3 – 1 – Aggregation (1015).mp4
│ │ 3 – 2 – Central Limit Theorem (1852).mp4% m3 `7 I& G0 w0 W- S9 X
│ │ 3 – 3 – Six Sigma (511).mp4‘ q; W5 s, D# v* h& W” h8 ]2 B( Q$ V’ E
│ │ 3 – 4 – Game of Life (1436).mp4
│ │ 3 – 5 – Cellular Automata (1807).mp4
│ │ 3 – 6 – Preference Aggregation (1219).mp4# c6 @8 {! b) W’ l, [9 v6 j: X; G
│ │ 4 – 1 – Introduction to Decision Making (537).mp4
│ │ 4 – 2 – Multi-Criterion Decision Making (818).mp4
│ │ 4 – 3 – Spatial Choice Models (1108).mp4# J5 w) K9 ~1 ^* `, s5 a4 `( o1 o
│ │ 4 – 4 – Probability The Basics (1006).mp4$ ?8 \( T+ q9 Y
│ │ 4 – 5 – Decision Trees (1438).mp4+ [9 o7 x3 |, r
│ │ 4 – 6 – Value of Information (841).mp4: \- F( j( R5 V# B’ i4 w
│ │ 5 – 1 – Thinking Electrons Modeling People (629).mp4
│ │ 5 – 2 – Rational Actor Models (1609).mp4
│ │ 5 – 3 – Behavioral Models (1249).mp4% S5 `1 p0 @” t8 a5 V
│ │ 5 – 4 – Rule Based Models (1230).mp4
│ │ 5 – 5 – When Does Behavior Matter (1240).mp48 {9 y’ p0 h0 w’ ]
│ │ 6 – 1 – Introduction to Linear Models (427).mp4: Z” M- U* X! V: c! l* E- ~
│ │ 6 – 2 – Categorical Models (1513).mp4” _( s% N o, j” P% c1 A j: V0 c
│ │ 6 – 3 – Linear Models (810).mp4
│ │ 6 – 4 – Fitting Lines to Data (1148).mp4
│ │ 6 – 5 – Reading Regression Output (1144).mp4
│ │ 6 – 6 – From Linear to Nonlinear (611).mp4& f* E1 K3 f( m
│ │ 6 – 7 – The Big Coefficient vs The New Reality (1126).mp4
│ │ 7 – 1 – Tipping Points (558).mp4+ ^$ A’ p0 Y5 w% E
│ │ 7 – 2 – Percolation Models (1148).mp4
│ │ 7 – 3 – Contagion Models 1 Diffusion (724).mp4
│ │ 7 – 4 – Contagion Models 2 SIS Model (912).mp4# a) \9 A; n/ S2 X. L
│ │ 7 – 5 – Classifying Tipping Points (826).mp4
│ │ 7 – 6 – Measuring Tips (1339).mp4
│ │ 8 – 1 – Introduction To Growth (643).mp4
│ │ 8 – 2 – Exponential Growth (1053).mp45 e3 c# z5 z’ ]6 ? Y3 H’ z, c+ {
│ │ 8 – 3 – Basic Growth Model (1359).mp4) `) f& a+ g0 \” w’ v) w3 m
│ │ 8 – 4 – Solow Growth Model (1141).mp4
│ │ 8 – 5 – WIll China Continue to Grow (1155).mp4/ }# T+ B: ]1 ` c
│ │ 8 – 6 – Why Do Some Countries Not Grow (1130).mp4
│ │ 9 – 1 – Problem Solving and Innovation (506).mp4+ m h o’ o7 r8 `% H( m
│ │ 9 – 2 – Perspectives and Innovation (1722).mp4
│ │ 9 – 3 – Heuristics (929).mp4
│ │ 9 – 4 – Teams and Problem Solving (1105).mp4
│ │ 9 – 5 – Recombination (1102).mp44 G# b H+ n/ m& \- D4 N/ h4 r
│ │
│ └─modelthinkingzh-001
│ │ Model Thinking Resources.pdf% {0 T5 n7 B’ S. U, y3 B+ {
│ │ Model Thinking Resources_2.pdf
│ │ modelthinking.01.01.PPT.pdf
│ │ modelthinking.01.02.PPT.pdf
│ │ modelthinking.08.07.PPT.pdf
│ │
│ ├─week01
│ │ 1 – 1 – 1.1 欢迎和致谢 Welcome & Thanks (3-58).mp48 A( R7 e) G/ l
│ │ 1 – 2 – 1.2 一对多和多对一 One to Many & Many to One (8-59).mp4
│ │ 1 – 3 – 1.3 为什么要运用模型 Why Model- (8-53).mp4
│ │ 1 – 4 – 1.4 睿智的世界公民 Intelligent Citizens of the World (11-31).mp4
│ │ 1 – 5 – 1.5 思考更清晰 Thinking More Clearly (10-50).mp43 l9 z3 T2 v# H* \5 r8 I1 O
│ │ 1 – 6 – 1.6 使用和理解数据 Using & Understanding Data (10-14).mp4+ f9 j” s- t: e& i
│ │ 1 – 7 – 1.7 使用模型做决定、策略和设计 Using Models to Decide, Strategize & Design (15-26).mp47 N& W- f* }2 K; v# q6 |. H
│ │
│ ├─week02
│ │ 2 – 1 – 2.1 分类和同群效应简介 Sorting & Peer Effects Introduction (5-11).mp48 U( k, L9 K& j* p1 A) r
│ │ 2 – 2 – 2.2 谢林的隔离模型 Schelling-‘s Segregation Model (11-30).mp4
│ │ 2 – 3 – 2.3 测量隔离 Measuring Segregation (11-30).mp4; E) z$ `& C! H: E: J: S
│ │ 2 – 4 – 2.4 同群效应 Peer Effects (6-58).mp4
│ │ 2 – 5 – 2.5 起立鼓掌模型 The Standing Ovation Model (18-05).mp4( E: A5 ]6 t8 c+ n, T& x& ] v
│ │ 2 – 6 – 2.6 识别问题 The Identification Problem (10-18).mp44 I’ Q4 H- C* n’ T2 V
│ │
│ ├─week03
│ │ 3 – 1 – 3.1) 聚合 Aggregation (10-15).mp42 |( {8 Z: T4 t$ M/ G. S5 E, ~3 d
│ │ 3 – 2 – 3.2) 中心极限定理 Central Limit Theorem (18-52).mp48 W$ W$ Y8 r$ V. X6 N* l- A
│ │ 3 – 3 – 3.3) 六西格玛 Six Sigma (5-11).mp46 y7 m Y7 _1 B n& C
│ │ 3 – 4 – 3.4) 生命游戏 Game of Life (14-36).mp4: Z! g% c7 R9 `* j/ M* M
│ │ 3 – 5 – 3.5) 细胞自动机 Cellular Automata (18-07).mp4
│ │ 3 – 6 – 3.6) 偏好聚合 Preference Aggregation (12-19).mp4
│ │
│ ├─week04
│ │ 4 – 1 – 4.1) 决策模型介绍 Introduction to Decision Making (5-37).mp4
│ │ 4 – 2 – 4.2) 多准则决策 Multi-Criterion Decision Making (8-18).mp4
│ │ 4 – 3 – 4.3) 空间投票模型 Spatial Choice Models (11-08).mp4
│ │ 4 – 4 – 4.4) 概率基础 Probability- The Basics (10-06).mp4
│ │ 4 – 5 – 4.5) 决策树 Decision Trees (14-38).mp4
│ │ 4 – 6 – 4.6) 信息的价值 Value of Information (8-41).mp4# T5 N9 a3 M: G: n0 {& ?) H
│ │
│ ├─week05
│ │ 5 – 1 – 5.1) 人类模型：电子思维 Thinking Electrons- Modeling People (6-29).mp4
│ │ 5 – 2 – 5.2) 理性行为者模型 Rational Actor Models (16-09).mp4
│ │ 5 – 3 – 5.3) 行为模型 Behavioral Models (12-49).mp4
│ │ 5 – 4 – 5.4) 基于规则的模型 Rule Based Models (12-30).mp4. [; }7 X) ]7 o! p) }7 A4 Y
│ │ 5 – 5 – 5.5) 行为什么时候重要？When Does Behavior Matter- (12-40).mp4
│ │ ! p7 A4 {3 V) ~ `$ S; Z
│ ├─week06* E3 D8 ^’ W# \: y3 e
│ │ 6 – 1 – 6.1) 线性模型介绍 Introduction to Linear Models (4-27).mp4; X P# S+ j: g2 E; a# S3 P7 N
│ │ 6 – 2 – 6.2) 分类模型 Categorical Models (15-13).mp4
│ │ 6 – 3 – 6.3) 线性模型 Linear Models (8-10).mp4
│ │ 6 – 4 – 6.4) 拟合数据 Fitting Lines to Data (11-48).mp40 A5 {2 ]) }+ H y
│ │ 6 – 5 – 6.5) 读取回归输出 Reading Regression Output (11-44).mp4
│ │ 6 – 6 – 6.6) 从线性到非线性 From Linear to Nonlinear (6-11).mp4
│ │ 6 – 7 – 6.7) 大系数和新现实思维 The Big Coefficient vs The New Reality (11-26).mp4– k4 L& R/ F8 X- T- R# I$ l
│ │
│ ├─week076 R* b) D/ J3 ?7 }
│ │ 7 – 1 – 7.1) 临界点 Tipping Points (5-58).mp4
│ │ 7 – 2 – 7.2) 渗透模型 Percolation Models (11-48).mp43 M$ A2 Z* s” ~5 j7 U
│ │ 7 – 3 – 7.3) 传染病模型 1- 扩散 Contagion Models 1- Diffusion (7-24).mp4! A6 D” ~% j9 x2 {6 ~7 w6 E7 f
│ │ 7 – 4 – 7.4) 传染病模型 2- SIS模型 Contagion Models 2- SIS Model (9-12).mp4
│ │ 7 – 5 – 7.5) 划分临界点 Classifying Tipping Points (8-26).mp4
│ │ 7 – 6 – 7.6) 测量建议 Measuring Tips (13-39).mp4
│ │
│ ├─week08
│ │ 8 – 1 – 8.1) 增长介绍 Introduction To Growth (6-43).mp4) X, I V+ T x( L* K4 @
│ │ 8 – 2 – 8.2) 指数增长 Exponential Growth (10-53).mp4
│ │ 8 – 3 – 8.3) 基础增长模型 Basic Growth Model (13-59).mp4
│ │ 8 – 4 – 8.4) 索洛增长模型 Solow Growth Model (11-41).mp46 u# g8 D1 v7 e3 Q* z2 t
│ │ 8 – 5 – 8.5) 中国会持续增长吗？WIll China Continue to Grow- (11-55).mp4
│ │ 8 – 6 – 8.6) 为何一些国家没有增长？Why Do Some Countries Not Grow- (11-30).mp45 ^6 @- f1 q0 S5 u9 a) M” J” i1 R
│ │ 8 – 7 – 8.7) 皮凯蒂的资本论- 一个简单模型的力量 Piketty-‘s Capital- The Power of a Simple Model (8-41).mp4& ~9 y, q9 }8 p: h0 n
│ │
│ ├─week09
│ │ 9 – 1 – 9.1) 问题解决和创新 Problem Solving and Innovation (5-06).mp4
│ │ 9 – 2 – 9.2) 视角与创新 Perspectives and Innovation (16-57).mp4$ m4 M9 k* R: Z, e
│ │ 9 – 3 – 9.3) 启发式探索 Heuristics (9-29).mp4
│ │ 9 – 4 – 9.4) 团队与问题解决 Teams and Problem Solving (11-05).mp4( u4 n+ T& C, s9 Z
│ │ 9 – 5 – 9.5) 重组 Recombination (11-02).mp4$ Z1 @- j! h. v, r3 k
│ │ 4 p! L’ i+ }5 O
│ ├─week10; y0 Q l+ j8 \8 B- t: \3 v# ? N6 o
│ │ 10 – 1 – 10.1) 马尔科夫模型 Markov Models (4-26).mp4– M3 L2 r” r3 @/ Y) G4 L7 l
│ │ 10 – 2 – 10.2) 一个简单的马尔科夫模型 A Simple Markov Model (11-27).mp4
│ │ 10 – 3 – 10.3) 马尔科夫民主化模型 Markov Model of Democratization (8-21).mp4– E” u! w2 y! e- h/ i+ V
│ │ 10 – 4 – 10.4) 马尔科夫收敛定理 Markov Convergence Theorem (10-33).mp4$ w, q( B# Q) ?’ O. a
│ │ 10 – 5 – 10.5) 马尔科夫模型延伸 Exapting the Markov Model (10-11).mp4
│ │
│ ├─week11
│ │ 11 – 1 – 11.1) 李雅普诺夫函数 Lyapunov Functions (9-13).mp4
│ │ 11 – 2 – 11.2) 城市的组织 The Organization of Cities (12-14).mp46 l’ S0 [‘ X$ |9 p
│ │ 11 – 3 – 11.3) 交换经济与外部效应 Exchange Economies and Externalities (9-18).mp4
│ │ 11 – 4 – 11.4) 达到收敛与最优的时间 Time to Convergence and Optimality (8-04).mp40 x6 J |* n+ Y6 x3 \ D( b
│ │ 11 – 5 – 11.5) 李雅普诺夫函数深入 Lyapunov- Fun and Deep (8-40).mp4
│ │ 11 – 6 – 11.6) 李雅普诺夫或马尔科夫函数 Lyapunov or Markov (7-24).mp48 f; K3 R2 d& v- [1 e( x
│ │ % k$ c0 } Q+ N- ~8 j5 X, |2 L3 o
│ ├─week12
│ │ 12 – 1 – 12.1) 协调与文化 Coordination and Culture (3-37).mp4
│ │ 12 – 2 – 12.2) 什么是文化，我们为什么要关注 What Is Culture And Why Do We Care
│ │ 12 – 2 – 12.2) 什么是文化，我们为什么要关注 What Is Culture And Why Do We Care- (15-43).mp4
│ │ 12 – 3 – 12.3) 纯协调博弈 Pure Coordination Game (13-48).mp4/ S3 |- X W! G
│ │ 12 – 4 – 12.4) 文化的兴起 Emergence of Culture (11-01).mp43 o! E$ M, L+ v’ o, M$ S
│ │ 12 – 5 – 12.5) 协调与一致 Coordination and Consistency (17-03).mp4
│ │
│ ├─week138 M* W! {‘ ]2 o! p. M6 N
│ │ 13 – 1 – 13.1) 路径依赖 Path Dependence (7-23).mp4+ R4 ]- b/ _ T8 T% P6 k
│ │ 13 – 2 – 13.2) 瓮模型 Urn Models (16-26).mp4* S1 I# Z0 T3 \9 Y7 M1 `2 [
│ │ 13 – 3 – 13.3) 瓮模型中的数学 Mathematics on Urn Models (14-46).mp4
│ │ 13 – 4 – 13.4) 路径依赖与混乱 Path Dependence and Chaos (11-08).mp4
│ │ 13 – 5 – 13.5) 路径依赖与收益递增 Path Dependence and Increasing Returns (12-31).mp4. l8 f1 ^2 G, v$ C# ]: a
│ │ 13 – 6 – 13.6) 路径依赖或临界点 Path Dependent or Tipping Point (9-52).mp43 o, l, p* e( ]7 d: t0 M1 u! t1 T
│ │
│ ├─week14/ h” \/ f7 E9 N/ }/ v
│ │ 14 – 1 – 14.1) 网络 Networks (7-04).mp4
│ │ 14 – 2 – 14.2) 网络的结构 The Structure of Networks (19-30).mp43 o% E2 E- E, B’ Q- d% k” a
│ │ 14 – 3 – 14.3) 网络形成的逻辑 The Logic of Network Formation (10-03).mp4
│ │ 14 – 4 – 14.4) 网络函数 Network Function (13-10).mp4
│ │ – k% N8 Y. q/ I0 P) M! R* U
│ ├─week15* d7 X: u* q2 B8 O2 `( S” H1 a
│ │ 15 – 1 – 15.1) 随机性和随机游走模型 Randomness and Random Walk Models (3-05).mp4
│ │ 15 – 2 – 15.2) 随机性的来源 Sources of Randomness (5-15).mp4– V* ?$ F6 N. T6 B7 v/ y+ c
│ │ 15 – 3 – 15.3) 技能和运气 Skill and Luck (8-28).mp4% Y! b* z& Y& R+ J6 D
│ │ 15 – 4 – 15.4) 随机游走 Random Walks (12-29).mp4
│ │ 15 – 5 – 15.5) 随机游走和华尔街 Random Walks and Wall Street (7-51).mp4& z) F# [! S( @* o7 z
│ │ 15 – 6 – 15.6) 有限记忆随机游走 Finite Memory Random Walks (8-18).mp4
│ │ ) S+ S! R2 P) t ?7 n- x% u9 O) H
│ ├─week16) [. ^; P, e( H+ ?
│ │ 16 – 1 – 16.1) 上校赛局博弈 Colonel Blotto Game (1-53).mp4
│ │ 16 – 2 – 16.2) 上校赛局：无最佳策略 Blotto- No Best Strategy (7-27).mp4” M, T( f, @+ Y0 D
│ │ 16 – 3 – 16.3) Blotto上校赛局的应用 Applications of Colonel Blotto (7-08).mp4
│ │ 16 – 4 – 16.4) Blotto上校赛局：军队优势 Blotto- Troop Advantages (6-27).mp4
│ │ 16 – 5 – 16.5) 上校赛局和竞争 Blotto and Competition (10-41).mp4( f% h, m. w’ R
│ │ * V. j” G’ P- J; [+ X/ G
│ ├─week176 r* n! x- L0 U; @: Y’ ?2 X$ k
│ │ 17 – 1 – 17.1) 简介：囚徒困境和集体行动 Intro- The Prisoners-‘ Dilemma and Collective Action (3-44).mp4
│ │ 17 – 2 – 17.2) 囚徒困境博弈 The Prisoners-‘ Dilemma Game (13-45).mp4: X, s {4 A3 C+ a( ~& M
│ │ 17 – 3 – 17.3) 合作的七种方式 Seven Ways To Cooperation (15-20).mp4; H Q- i$ S5 E; \% ]1 v! G
│ │ 17 – 4 – 17.4) 集体行动和公共资源问题 Collective Action and Common Pool Resource Problems (7-23).mp4
│ │ 17 – 5 – 17.5) 没有万灵药 No Panacea (6-03).mp4
│ │ x7 d7 n% I6 [
│ └─week18# t9 a: Q- j; p’ |
│ 18 – 1 – 18.1) 机制设计 Mechanism Design (4-00).mp4# q2 Z0 b! X- S I: E. t- M
│ 18 – 2 – 18.2) 隐藏行动和隐藏信息 Hidden Action and Hidden Information (9-53).mp4‘ u+ b. q) h: x/ s! I- J3 `” k# A
│ 18 – 3 – 18.3) 拍卖 Auctions (19-59).mp4
│ 18 – 4 – 18.4) 公众项目 Public Projects (12-21).mp4; ~1 D4 c” ^7 J, u
│
├─data analysis and statistical inference
│ 8 – 1 – Review – Frequentist vs. Bayesian Inference (28-27).mp4
│ Unit 6.zip: D- J8 [0 o9 H8 b# \8 m* S/ o
│ unit 7.zip
│ Week 1.zip
│ week 2.zip‘ F2 ?( P% c4 ^’ M
│ week 3.zip
│ week 4.zip
│ week 5.zip& l. u( j5 V( ^’ W# G
│ 6 j3 v, Z# n+ I/ C6 o
├─Data Visualization
│ ├─01_Week_1
│ │ 01_1.1.1._Introduction_00-11-58.mp4
│ │ 02_1.1.2._Some_Books_on_Data_Visualization_00-03-21.mp4
│ │ 03_1.1.3._Overview_of_Visualization_00-11-02.mp4” \, s, ~: c) U7 \0 b, T
│ │ 04_1.2.1._2-D_Graphics_00-10-09.mp4
│ │ 05_SVG-example_00-01-34.mp4: Z3 U( J% Y/ m! x
│ │ 06_1.2.2._2-D_Drawing_00-09-11.mp4* D/ W2 k( C- Z F7 Y! N; C
│ │ 07_1.2.3._3-D_Graphics_00-08-39.mp4
│ │ 08_1.2.4._Photorealism_00-10-05.mp4 x4 {8 c& p. @& B
│ │ 09_1.2.5._Non-Photorealism_00-06-09.mp47 m* V$ V* z6 U+ W! h3 Y$ n/ t. i
│ │ 10_1.3.1._The_Human_00-11-08.mp4
│ │ 11_1.3.2._Memory_00-12-16.mp4
│ │ 12_1.3.3._Reasoning_00-07-24.mp4
│ │ 13_1.3.4._The_Human_Retina_00-10-22.mp4
│ │ 14_1.3.5._Perceiving_Two_Dimensions_00-08-23.mp4
│ │ 15_1.3.6._Perceiving_Perspective_00-08-36.mp4
│ │
│ ├─02_Week_2( n’ |- F2 \& r1 L$ G4 x
│ │ 01_2.1.0._Module_2_Introduction_00-02-49.mp4” {# F) _5 R) e# x
│ │ 02_2.1.1._Data_00-07-44.mp44 | `+ ~3 a3 C1 H
│ │ 03_2.1.2._Mapping_00-09-04.mp49 ?6 w3 V5 `, v
│ │ 04_2.1.3._Charts_00-09-24.mp4
│ │ 05_2.2.1._Glyphs_Part_1_00-04-32.mp4; M% o# X” D2 M+ k% V
│ │ 06_2.2.1._Glyphs_Part_2_00-06-30.mp4$ Z. l3 W3 q’ C, G/ ^
│ │ 07_2.2.2._Parallel_Coordinates_00-08-34.mp4: }4 ^) `5 l# P2 x
│ │ 08_2.2.3._Stacked_Graphs_Part_1_00-05-56.mp4. E( E; d- P7 D* T M. {8 o
│ │ 09_2.2.3._Stacked_Graphs_Part_2_00-06-30.mp4+ G3 a. @7 x1 w: z
│ │ 10_2.3.1._Tuftes_Design_Rules_00-12-14.mp4
│ │ 11_2.3.2._Using_Color_00-11-28.mp4
│ │
│ ├─03_Week_3
│ │ 01_3.1.0_Module_3_Introduction_00-01-15.mp4, ^+ o” e5 K0 p0 \4 C” T
│ │ 02_3.1.1._Graphs_and_Networks_00-08-16.mp4” @’ [+ }( V2 L3 y* v! E
│ │ 03_3.1.2._Embedding_Planar_Graphs_00-11-37.mp4
│ │ 04_3.1.3._Graph_Visualization_00-13-50.mp4– b3 f) f5 p$ c’ p( n! }4 d4 q
│ │ 05_3.1.4._Tree_Maps_00-09-21.mp4
│ │ 06_3.2.1._Principal_Component_Analysis_00-08-04.mp4; d5 ~! p% l& A) V- y6 I( r
│ │ 07_3.2.2._Multidimensional_Scaling_00-06-48.mp4
│ │ 08_3.3.1._Packing_00-12-52.mp4! B’ E* `, L7 @: M
│ │
│ └─04_Week_4
│ 01_4.1.0._Module_4_Introduction_00-00-55.mp4
│ 02_4.1.1._Visualization_Systems_00-03-20.mp48 R: f+ f7 x5 B+ K; C’ _” m# O: `
│ 03_4.1.2._The_Information_Visualization_Mantra-_Part_1_00-09-05.mp4
│ 04_4.1.2._The_Information_Visualization_Mantra-_Part_2_00-09-07.mp4$ y9 t& c% m2 A* _# U6 z8 e
│ 05_4.1.2._The_Information_Visualization_Mantra-_Part_3_00-05-49.mp4
│ 06_4.1.3._Database_Visualization_Part-_1_00-12-26.mp4
│ 07_4.1.3._Database_Visualization_Part-_2_00-08-10.mp4
│ 08_4.1.3._Database_Visualization_Part-_3_00-09-46.mp4. A% x* i) R+ s” R0 J. L3 s
│ 09_4.2.1._Visualization_System_Design_00-14-26.mp4
│
├─Dino 101 Dinosaur Paleobiology: y6 p5 @# w S’ o3 j/ ?
│ │ coursedeｓｃｒｉｐｔions.pdf3 |8 H% z) X5 o
│ │ dino101-course-outline.pdf$ ~9 G/ ^/ k” D3 n _! \
│ │ dino101-course-teaching-outcomes.pdf( Q* Q# L. e8 b2 J0 I
│ │ Glossary V2.pdf
│ │
│ ├─Lesson 1 Appearance and Anatomy( s- Q6 L- c; x+ ]+ B
│ │ 1 – 1 – Introduction (7_31).mp4
│ │ 1 – 2 – Size (4_33).mp4, I6 M( s’ W* b3 `9 }4 O
│ │ 1 – 3 – Skeleton (12_46).mp43 M, [5 ^% C6 }1 w6 o0 O$ J4 `
│ │ 1 – 4 – Saurischians (7_28).mp4: e’ r7 Q. _( H’ L% X0 C’ \; y7 Y
│ │ 1 – 5 – Ornithischians (10_03).mp4& O; Y* a. b’ O# R: l
│ │ 1 – 6 – Appearance (13_11).mp4
│ │ 1 – 7 – Muscles (4_58).mp4
│ │ Lesson 1 the Skeleton V2.pdf‘ |& O# y7 b# j’ g) r( e8 P
│ &n