Section 1 : Part 1 Introduction

 		lecture 1 A Practical Example What You Will Learn in This Co 5:6
Lecture 2 What Does the Course Cover 2:36
Lecture 3 Download All Resources and Text

Section 2 : The Field of Data Science - The Various Data Science Discip.

 		Lecture 4 Data Science and Business Buzzwords Why are there 5:11
Lecture 5 What is the difference between Analysis 3:40
Lecture 6 Business Analytics, Data Analytics 8:12
Lecture 7 Continuing with BI, ML, and AI 9:21
Lecture 8 A Breakdown of our Data Science Infographic 3:55

Section 3 : The Field of Data Science - Connecting the Data Science

 		Lecture 9 Applying Traditional Data, Big Data, BI, 7:7

Section 4 : The Field of Data Science - The Benefits of Each Discipline

 		Lecture 10 The Reason Behind These Disciplines 3:50

Section 5 : The Field of Data Science - Popular Data Science Techniques

 		Lecture 11 Techniques for Working with Traditional 8:9
Lecture 12 Real Life Examples of Traditional 1:37
Lecture 13 Techniques for Working with Big Data 4:17
Lecture 14 Real Life Examples of Big Data 1:19
Lecture 15 Business Intelligence (BI) Techniques 6:29
Lecture 16 Real Life Examples of Business Intelligence 1:32
Lecture 17 Techniques for Working with Traditional Methods
Lecture 18 Real Life Examples of Traditional Methods 2:24
Lecture 19 Machine Learning (ML) Techniques 6:21
Lecture 20 Types of Machine Learning
Lecture 21 Real Life Examples of Machine Learning (ML) 1:57

Section 6 : 6 The Field of Data Science - Popular Data Science Tools

 		Lecture 22 Necessary Programming Languages and Software Used 5:25

Section 7 : he Field of Data Science - Careers in Data Science

 		Lecture 23 Finding the Job - What to Expect and What to Look 3:11

Section 8 : The Field of Data Science - Debunking Common Misconceptions

 		Lecture 24 Debunking Common Misconceptions 3:49

Section 9 : Part 2 Probability

 		Lecture 25 The Basic Probability Formula 6:31
Lecture 26 Computing Expected Values 5:29
Lecture 27 Frequency 5:0
Lecture 28 Events and Their Complements 5:26

Section 10 : Probability - Combinatorics

 		Lecture 29 Fundamentals of Combinatorics
Lecture 30 Permutations and How to Use Them 3:21
Lecture 31 Simple Operations with Factorials 3:36
Lecture 32 Solving Variations with Repetition 3:0
Lecture 33 Solving Variations without Repetition 3:48
Lecture 34 Solving Combinations 4:52
Lecture 35 Symmetry of Combinations 3:27
Lecture 36 Solving Combinations with Separate Sample Spac 2:52
Lecture 37 Combinatorics in Real-Life The Lottery 3:13
Lecture 38 A Recap of Combinatorics 2:55
Lecture 39 A Practical Example of Combinatorics 10:53

Section 11 : Probability - Bayesian Inference

 		Lecture 40 Sets and Events 4:25
Lecture 41 Ways Sets Can Interact 3:45
Lecture 42 Intersection of Sets 2:7
Lecture 43 Union of Sets 4:51
Lecture 44 Mutually Exclusive Sets 2:10
Lecture 45 Dependence and Independence of Sets 3:2
Lecture 46 The Conditional Probability Formula 4:16
Lecture 47 The Law of Total Probability 3:4
Lecture 48 The Additive Rule 2:22
Lecture 49 The Multiplication Law 4:5
Lecture 50 Bayes' Law 5:44
Lecture 51 A Practical Example of Bayesian Inference 14:52

Section 12 : Probability - Distributions

 		Lecture 52 Fundamentals of Probability Distributions 6:29
Lecture 53 Types of Probability Distributions 7:32
Lecture 54 Characteristics of Discrete Distributions 2:0
Lecture 55 Discrete Distributions The Uniform Distributio 2:13
Lecture 56 Discrete Distributions The Bernoulli Distribut 3:27
Lecture 57 Discrete Distributions The Binomial Distributi 7:4
Lecture 58 Discrete Distributions The Poisson Distributio 5:27
Lecture 59 Characteristics of Continuous Distributions 7:12
Lecture 60 Continuous Distributions The Normal Distributi 4:8
Lecture 61 Continuous Distributions The Standard Normal D 4:25
Lecture 62 Continuous Distributions The Students' T Distr 2:30
Lecture 63 Continuous Distributions The Chi-Squared Distr 2:23
Lecture 64 Continuous Distributions The Exponential Distr 3:15
Lecture 65 Continuous Distributions The Logistic Distribu 4:7
Lecture 66 A Practical Example of Probability Distributio 15:3

Section 13 : Probability - Probability in Other Fields

 		Lecture 67 Probability in Finance 7:46
Lecture 68 Probability in Statistics 6:18
Lecture 69 Probability in Data Science 4:47

Section 14 : Part 3 Statistics

 		Lecture 70 Population and Sample 4:2

Section 15 : Statistics - Descriptive Statistics

 		Lecture 71 Types of Data 4:33
Lecture 72 Levels of Measurement 3:44
Lecture 73 Categorical Variables - Visualization Techniqu 4:53
Lecture 74 Statistics+-+PDF+with+Excel+Solutions+that+don Pdf
Lecture 75 Numerical Variables - Frequency Distribution T 3:10
Lecture 76 2.4.Numerical-variables.Frequency-distribution
Lecture 77 The Histogram 2:14
Lecture 78 2.5.The-Histogram-exercise
Lecture 79 Cross Tables and Scatter Plots 4:44
Lecture 80 2.6.+Cross+table+and+scatter+plot_exercise
Lecture 81 Mean, median and mode 4:20
Lecture 82 2.7.+Mean,+median+and+mode_exercise_solution
Lecture 83 Skewness 2:38
Lecture 84 2.8.+Skewness_exercise_solution
Lecture 85 Variance 5:55
Lecture 86 2.9.+Variance_exercise_solution
Lecture 87 Standard Deviation and Coefficient of Variatio 4:41
Lecture 88 2.10.Standard-deviation-and-coefficient-of-var
Lecture 89 Covariance 3:24
Lecture 90 2.11.+Covariance_exercise_solution
Lecture 91 Correlation Coefficient 3:18
Lecture 92 2.12.+Correlation_exercise_solution

Section 16 : Statistics - Practical Example Descriptive Statis

 		Lecture 93 Practical Example Descriptive Statistics 16:16
Lecture 94 2 2.13.Practical-example.Descriptive-statistic

Section 17 : Statistics - Inferential Statistics Fundamentals

 		Lecture 95 Introduction 1:1
Lecture 96 What is a Distribution 4:33
Lecture 97 The Normal Distribution 3:54
Lecture 98 The Standard Normal Distribution 3:31
Lecture 99 2 3.4.Standard-normal-distribution-exercise-so
Lecture 100 Central Limit Theorem 4:20
Lecture 101 Standard error 1:27
Lecture 102 Estimators and Estimates 3:7

Section 18 : Statistics - Inferential Statistics Confidence In

 		Lecture 103 What are Confidence Intervals 2:42
Lecture 104 Confidence Intervals; Population Variance Kno 8:2
Lecture 105 3 3.9.The-z-table
Lecture 106 Confidence Interval Clarifications 4:39
Lecture 107 Student's T Distribution 3:23
Lecture 108 Confidence Intervals; Population Variance Unk 4:37
Lecture 109 3 3.11.The-t-table
Lecture 110 Margin of Error 4:53
Lecture 111 Confidence intervals. Two means. Dependent sa 6:4
Lecture 112 2 3.13.+Confidence+intervals.+Two+means.+Depe
Lecture 113 Confidence intervals. Two means. Independent 4:31
Lecture 114 2 3.14.+Confidence+intervals.+Two+means.+Inde
Lecture 115 Confidence intervals. Two means. Independent 3:57
Lecture 116 2 3.15.+Confidence+intervals.+Two+means.+Inde
Lecture 117 Confidence intervals. Two means. Independent 1:27

Section 19 : Statistics - Practical Example Inferential Statis

 		Lecture 118 Practical Example Inferential Statistics 10:6
Lecture 119 2 3.17.Practical-example.Confidence-intervals

Section 20 : Statistics - Hypothesis Testing

 		Lecture 120 Null vs Alternative Hypothesis 5:52
Lecture 121 Further Reading on Null and Alternative Hypot Text
Lecture 122 Rejection Region and Significance Level 7:5
Lecture 123 Type I Error and Type II Error 4:14
Lecture 124 Test for the Mean. Population Variance Known 6:34
Lecture 125 2 4.4.+Test+for+the+mean.+Population+variance+
Lecture 126 p-value 4:13
Lecture 127 Test for the Mean. Population Variance Unknow 4:49
Lecture 128 2 4.6.Test-for-the-mean.Population-variance-u
Lecture 129 Test for the Mean. Dependent Samples 5:18
Lecture 130 2 4.7.+Test+for+the+mean.+Dependent+samples_ex
Lecture 131 Test for the mean. Independent Samples (Part 4:22
Lecture 132 2 4.8.Test-for-the-mean.Independent-samples-P
Lecture 133 Test for the mean. Independent Samples (Part 4:27
Lecture 134 2 4.9.Test-for-the-mean.Independent-samples-P

Section 21 : Statistics - Practical Example Hypothesis Testing

 		Lecture 135 Practical Example Hypothesis Testing 7:16
Lecture 136 2 4.10.Hypothesis-testing-section-practical-e

Section 22 : Part 4 Introduction to Python

 		Lecture 137 Introduction to Programming 5:4
Lecture 138 Why Python 5:11
Lecture 139 Why Jupyter 3:29
Lecture 140 Installing Python and Jupyter 6:49
Lecture 141 Understanding Jupyter's Interface - the Noteb 3:16
Lecture 142 Prerequisites for Coding in the Jupyter Noteb 6:15

Section 23 : Python - Variables and Data Types

 		Lecture 143 Variables 4:52
Lecture 144 Numbers and Boolean Values in Python 3:6
Lecture 145 Python Strings 11:20

Section 24 : Python - Basic Python Syntax

 		Lecture 146 Using Arithmetic Operators in Python 3:24
Lecture 147 The Double Equality Sign 1:34
Lecture 148 How to Reassign Values 1:8
Lecture 149 Add Comments 3:20
Lecture 150 Understanding Line Continuation 0:50
Lecture 151 Indexing Elements 1:18
Lecture 152 Structuring with Indentation 3:42

Section 25 : Python - Other Python Operators

 		Lecture 153 Comparison Operators 2:10
Lecture 154 Logical and Identity Operators 5:36

Section 26 : Python - Conditional Statements

 		Lecture 155 The IF Statement 6:14
Lecture 156 The ELSE Statement 6:14
Lecture 157 The ELIF Statement 11:16
Lecture 158 A Note on Boolean Values 4:39

Section 27 : Python - Python Functions

 		Lecture 159 Defining a Function in Python 4:20
Lecture 160 How to Create a Function with a Parameter 7:58
Lecture 161 Defining a Function in Python - Part II 5:29
Lecture 162 How to Use a Function within a Function 1:49
Lecture 163 Conditional Statements and Functions 3:7
Lecture 164 Functions Containing a Few Arguments 2:48
Lecture 165 Built-in Functions in Python 3:56

Section 28 : Python - Sequences

 		Lecture 166 Lists 8:18
Lecture 167 Using Methods 4:31
Lecture 168 List Slicing 4:31
Lecture 169 Tuples 6:40
Lecture 170 Dictionaries 8:27

Section 29 : Python - Iterations

 		Lecture 171 For Loops 5:40
Lecture 172 While Loops and Incrementing 5:11
Lecture 173 Lists with the range() Function 6:22
Lecture 174 Conditional Statements and Loops 6:30
Lecture 175 Conditional Statements, Functions, and Loops 2:27
Lecture 176 How to Iterate over Dictionaries 6:22

Section 30 : Python - Advanced Python Tools

 		Lecture 177 Object Oriented Programming 5:0
Lecture 178 Modules and Packages 1:6
Lecture 179 What is the Standard Library 2:47
Lecture 180 Importing Modules in Python 4:4

Section 31 : Part 5 Advanced Statistical Methods in Pytho

 		Lecture 181 Introduction to Regression Analysis 1:28

Section 32 : Advanced Statistical Methods - Linear Regression

Lecture 182 The Linear Regression Model 5:50
Lecture 183 Correlation vs Regression 1:44
Lecture 184 Geometrical Representation of the Linear Regr 1:26
Lecture 185 Python Packages Installation 4:40
Lecture 186 First Regression in Python 7:11
Lecture 187 First Regression in Python Exercise Text
Lecture 188 Using Seaborn for Graphs 1:22
Lecture 189 How to Interpret the Regression Table 5:47
Lecture 190 Decomposition of Variability 3:38
Lecture 191 What is the OLS 3:14
Lecture 192 R-Squared 5:30

Section 33 : Advanced Statistical Methods - Multiple Linear Re

 		Lecture 193 Multiple Linear Regression 2:56
Lecture 194 Adjusted R-Squared 6:1
Lecture 195 Remove - INTRODUCTION TO BRAINMEASURES PROCTO Pdf
Lecture 196 Test for Significance of the Model (F-Test) 2:1
Lecture 197 OLS Assumptions 2:21
Lecture 198 A1 Linearity 1:51
Lecture 199 A2 No Endogeneity 4:10
Lecture 200 A3 Normality and Homoscedasticity 5:48
Lecture 201 A4 No Autocorrelation 3:31
Lecture 202 A5 No Multicollinearity 3:26
Lecture 203 Dealing with Categorical Data - Dummy Variabl 6:44
lecture 204 Dealing with Categorical Data DummyVariables
lecture 205 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf

Section 34 : Advanced Statistical Methods - Linear Regression

Lecture 206 What is sklearn and How is it Different from 2:15
Lecture 207 How are we Going to Approach this Section 1:56
Lecture 208 Simple Linear Regression with sklearn 5:38
Lecture 209 Simple Linear Regression with sklearn - A Sta 4:49
Lecture 210 A Note on Normalization Text
Lecture 211 Multiple Linear Regression with sklearn 3:11
Lecture 212 Multiple Linear Regression with sklearn 3:11
Lecture 213 Calculating the Adjusted R-Squared in sklearn 4:46
Lecture 214 Feature Selection (F-regression) 4:41
Lecture 215 Feature Selection (F-regression) 4:46
Lecture 216 A Note on Calculation of P-values with sklear Text
Lecture 217 Creating a Summary Table with P-values 2:10
Lecture 218 Feature Scaling (Standardization) 5:38
Lecture 219 Feature Scaling (Standardization) 5:38
Lecture 220 Feature Selection through Standardization of 5:23
Lecture 221 Predicting with the Standardized Coefficients 3:53
Lecture 222 Underfitting and Overfitting 2:42
Lecture 223 Underfitting and Overfitting 2:42
Lecture 224 Train - Test Split Explained 6:54

Section 35 : Advanced Statistical Methods - Practical Example

Lecture 225 Practical Example Linear Regression 12:0
Lecture 226 Practical Example Linear Regression 6:12
Lecture 227 A Note on Multicollinearity Text
Lecture 228 Practical Example Linear Regression 3:16
Lecture 229 Practical Example Linear Regression 8:10
Lecture 230 Practical Example Linear Regression 8:10
Lecture 231 Dummy Variables Text
Lecture 232 Practical Example Linear Regression 7:35
Lecture 233 Linear Regression - Exercise Text

Section 36 : Advanced Statistical Methods - Logistic Regressio

 		Lecture 234 Introduction to Logistic Regression 1:20
Lecture 235 A Simple Example in Python 4:42
Lecture 236 Logistic vs Logit Function 4:0
Lecture 237 Building a Logistic Regression 2:48
Lecture 238 Example_bank_data
Lecture 239 An Invaluable Coding Tip 2:27
Lecture 240 Understanding Logistic Regression Tables Pdf
Lecture 241 Bank_data
Lecture 242 What do the Odds Actually Mean 4:30
Lecture 243 Binary Predictors in a Logistic Regression 4:32
Lecture 244 Bank_data
Lecture 245 Calculating the Accuracy of the Model 3:22
Lecture 246 Bank_data
Lecture 247 Underfitting and Overfitting 3:43
Lecture 248 Testing the Model 5:5
Lecture 249 Bank_data

Section 37 : Advanced Statistical Methods - Cluster Analysis

 		Lecture 250 Introduction to Cluster Analysis 3:41
Lecture 251 Some Examples of Clusters 4:32
Lecture 252 Difference between Classification and Cluster 2:32
Lecture 253 Math Prerequisites 3:20

Section 38 : Advanced Statistical Methods - K-Means Clustering

 		Lecture 254 K-Means Clustering 4:41
Lecture 255 A Simple Example of Clustering 1:47
Lecture 256 Countries_exercise
Lecture 257 Clustering Categorical Data 2:50
Lecture 258 Categorical
Lecture 259 How to Choose the Number of Clusters 6:11
Lecture 260 Countries_exercise
Lecture 261 Pros and Cons of K-Means Clustering 3:24
Lecture 262 To Standardize or not to Standardize 4:33
Lecture 263 Relationship between Clustering and Regressio 1:32
Lecture 264 Market Segmentation with Cluster Analysis (Pa 6:4
Lecture 265 Market Segmentation with Cluster Analysis (Pa 6:59
Lecture 266 How is Clustering Useful 4:48
Lecture 267 EXERCISE: Species Segmentation with Cluster A
Lecture 268 EXERCISE: Species Segmentation with Cluster A

Section 39 : Advanced Statistical Methods - Other Types of Clustering

 		lecture 269 Types of Clustering 3:40
lecture 270 Dendrogram 5:21
lecture 271 Heatmaps 4:34

Section 40 : Part 6 Mathematics

 		lecture 272 What is a Matrix 3:40
lecture 273 Scalars and Vectors 2:59
lecture 274 Linear Algebra and Geometry 3:6
lecture 275 Arrays in Python - A Convenient Way To Represent M 5:9
lecture 276 What is a Tensor 3:0
lecture 277 Addition and Subtraction of Matrices 3:36
lecture 278 Errors when Adding Matrices 2:1
lecture 279 Transpose of a Matrix 5:13
lecture 280 Dot Product 3:48
lecture 281 Dot Product of Matrices 8:23
lecture 282 Why is Linear Algebra Useful 10:10