Section 1 : Part 1 Introduction
Lecture 1 A Practical Example What You Will Learn in This Co 00:05:06 Duration
Lecture 2 What Does the Course Cover 00:02:36 Duration
Lecture 3 Download All Resources and

Section 2 : The Field of Data Science - The Various Data Science Discip.
Lecture 1 Data Science and Business Buzzwords Why are there 00:05:11 Duration
Lecture 2 What is the difference between Analysis 00:03:40 Duration
Lecture 3 Business Analytics, Data Analytics 00:08:12 Duration
Lecture 4 Continuing with BI, ML, and AI 00:09:21 Duration
Lecture 5 A Breakdown of our Data Science Infographic 00:03:55 Duration

Section 3 : The Field of Data Science - Connecting the Data Science
Lecture 1 Applying Traditional Data, Big Data, BI, 00:07:07 Duration

Section 4 : The Field of Data Science - The Benefits of Each Discipline
Lecture 1 The Reason Behind These Disciplines 00:03:50 Duration

Section 5 : The Field of Data Science - Popular Data Science Techniques
Lecture 1 Techniques for Working with Traditional 00:08:09 Duration
Lecture 2 Real Life Examples of Traditional 00:01:37 Duration
Lecture 3 Techniques for Working with Big Data 00:04:17 Duration
Lecture 4 Real Life Examples of Big Data 00:01:19 Duration
Lecture 5 Business Intelligence (BI) Techniques 00:06:29 Duration
Lecture 6 Real Life Examples of Business Intelligence 00:01:32 Duration
Lecture 7 Techniques for Working with Traditional Methods
Lecture 8 Real Life Examples of Traditional Methods 00:02:24 Duration
Lecture 9 Machine Learning (ML) Techniques 00:06:21 Duration
Lecture 10 Types of Machine Learning
Lecture 11 Real Life Examples of Machine Learning (ML) 00:01:57 Duration

Section 6 : 6 The Field of Data Science - Popular Data Science Tools
Lecture 1 Necessary Programming Languages and Software Used 00:05:25 Duration

Section 7 : he Field of Data Science - Careers in Data Science
Lecture 1 Finding the Job - What to Expect and What to Look 00:03:11 Duration

Section 8 : The Field of Data Science - Debunking Common Misconceptions
Lecture 1 Debunking Common Misconceptions 00:03:49 Duration

Section 9 : Part 2 Probability
Lecture 1 The Basic Probability Formula 00:06:31 Duration
Lecture 2 Computing Expected Values 00:05:29 Duration
Lecture 3 Frequency 00:05:00 Duration
Lecture 4 Events and Their Complements 00:05:26 Duration

Section 10 : Probability - Combinatorics
Lecture 1 Fundamentals of Combinatorics
Lecture 2 Permutations and How to Use Them 00:03:21 Duration
Lecture 3 Simple Operations with Factorials 00:03:36 Duration
Lecture 4 Solving Variations with Repetition 00:03:00 Duration
Lecture 5 Solving Variations without Repetition 00:03:48 Duration
Lecture 6 Solving Combinations 00:04:52 Duration
Lecture 7 Symmetry of Combinations 00:03:27 Duration
Lecture 8 Solving Combinations with Separate Sample Spac 00:02:52 Duration
Lecture 9 Combinatorics in Real-Life The Lottery 00:03:13 Duration
Lecture 10 A Recap of Combinatorics 00:02:55 Duration
Lecture 11 A Practical Example of Combinatorics 00:10:53 Duration

Section 11 : Probability - Bayesian Inference
Lecture 1 Sets and Events 00:04:25 Duration
Lecture 2 Ways Sets Can Interact 00:03:45 Duration
Lecture 3 Intersection of Sets 00:02:07 Duration
Lecture 4 Union of Sets 00:04:51 Duration
Lecture 5 Mutually Exclusive Sets 00:02:10 Duration
Lecture 6 Dependence and Independence of Sets 00:03:02 Duration
Lecture 7 The Conditional Probability Formula 00:04:16 Duration
Lecture 8 The Law of Total Probability 00:03:04 Duration
Lecture 9 The Additive Rule 00:02:22 Duration
Lecture 10 The Multiplication Law 00:04:05 Duration
Lecture 11 Bayes' Law 00:05:44 Duration
Lecture 12 A Practical Example of Bayesian Inference 00:14:52 Duration

Section 12 : Probability - Distributions
Lecture 1 Fundamentals of Probability Distributions 00:06:29 Duration
Lecture 2 Types of Probability Distributions 00:07:32 Duration
Lecture 3 Characteristics of Discrete Distributions 00:02:00 Duration
Lecture 4 Discrete Distributions The Uniform Distributio 00:02:13 Duration
Lecture 5 Discrete Distributions The Bernoulli Distribut 00:03:27 Duration
Lecture 6 Discrete Distributions The Binomial Distributi 00:07:04 Duration
Lecture 7 Discrete Distributions The Poisson Distributio 00:05:27 Duration
Lecture 8 Characteristics of Continuous Distributions 00:07:12 Duration
Lecture 9 Continuous Distributions The Normal Distributi 00:04:08 Duration
Lecture 10 Continuous Distributions The Standard Normal D 00:04:25 Duration
Lecture 11 Continuous Distributions The Students' T Distr 00:02:30 Duration
Lecture 12 Continuous Distributions The Chi-Squared Distr 00:02:23 Duration
Lecture 13 Continuous Distributions The Exponential Distr 00:03:15 Duration
Lecture 14 Continuous Distributions The Logistic Distribu 00:04:07 Duration
Lecture 15 A Practical Example of Probability Distributio 00:15:03 Duration

Section 13 : Probability - Probability in Other Fields
Lecture 1 Probability in Finance 00:07:46 Duration
Lecture 2 Probability in Statistics 00:06:18 Duration
Lecture 3 Probability in Data Science 00:04:47 Duration

Section 14 : Part 3 Statistics
Lecture 1 Population and Sample 00:04:02 Duration

Section 15 : Statistics - Descriptive Statistics
Lecture 1 Types of Data 00:04:33 Duration
Lecture 2 Levels of Measurement 00:03:44 Duration
Lecture 3 Categorical Variables - Visualization Techniqu 00:04:53 Duration
Lecture 4 Statistics+-+PDF+with+Excel+Solutions+that+don
Lecture 5 Numerical Variables - Frequency Distribution T 00:03:10 Duration
Lecture 6 2.4.Numerical-variables.Frequency-distribution
Lecture 7 The Histogram 00:02:14 Duration
Lecture 8 2.5.The-Histogram-exercise
Lecture 9 Cross Tables and Scatter Plots 00:04:44 Duration
Lecture 10 2.6.+Cross+table+and+scatter+plot_exercise
Lecture 11 Mean, median and mode 00:04:20 Duration
Lecture 12 2.7.+Mean,+median+and+mode_exercise_solution
Lecture 13 Skewness 00:02:38 Duration
Lecture 14 2.8.+Skewness_exercise_solution
Lecture 15 Variance 00:05:55 Duration
Lecture 16 2.9.+Variance_exercise_solution
Lecture 17 Standard Deviation and Coefficient of Variatio 00:04:41 Duration
Lecture 18 2.10.Standard-deviation-and-coefficient-of-var
Lecture 19 Covariance 00:03:24 Duration
Lecture 20 2.11.+Covariance_exercise_solution
Lecture 21 Correlation Coefficient 00:03:18 Duration
Lecture 22 2.12.+Correlation_exercise_solution

Section 16 : Statistics - Practical Example Descriptive Statis
Lecture 1 Practical Example Descriptive Statistics 00:16:16 Duration
Lecture 2 2 2.13.Practical-example.Descriptive-statistic

Section 17 : Statistics - Inferential Statistics Fundamentals
Lecture 1 Introduction 00:01:01 Duration
Lecture 2 What is a Distribution 00:04:33 Duration
Lecture 3 The Normal Distribution 00:03:54 Duration
Lecture 4 The Standard Normal Distribution 00:03:31 Duration
Lecture 5 2 3.4.Standard-normal-distribution-exercise-so
Lecture 6 Central Limit Theorem 00:04:20 Duration
Lecture 7 Standard error 00:01:27 Duration
Lecture 8 Estimators and Estimates 00:03:07 Duration

Section 18 : Statistics - Inferential Statistics Confidence In
Lecture 1 What are Confidence Intervals 00:02:42 Duration
Lecture 2 Confidence Intervals; Population Variance Kno 00:08:02 Duration
Lecture 3 3 3.9.The-z-table
Lecture 4 Confidence Interval Clarifications 00:04:39 Duration
Lecture 5 Student's T Distribution 00:03:23 Duration
Lecture 6 Confidence Intervals; Population Variance Unk 00:04:37 Duration
Lecture 7 3 3.11.The-t-table
Lecture 8 Margin of Error 00:04:53 Duration
Lecture 9 Confidence intervals. Two means. Dependent sa 00:06:04 Duration
Lecture 10 2 3.13.+Confidence+intervals.+Two+means.+Depe
Lecture 11 Confidence intervals. Two means. Independent 00:04:31 Duration
Lecture 12 2 3.14.+Confidence+intervals.+Two+means.+Inde
Lecture 13 Confidence intervals. Two means. Independent 00:03:57 Duration
Lecture 14 2 3.15.+Confidence+intervals.+Two+means.+Inde
Lecture 15 Confidence intervals. Two means. Independent 00:01:27 Duration

Section 19 : Statistics - Practical Example Inferential Statis
Lecture 1 Practical Example Inferential Statistics 00:10:06 Duration
Lecture 2 2 3.17.Practical-example.Confidence-intervals

Section 20 : Statistics - Hypothesis Testing
Lecture 1 Null vs Alternative Hypothesis 00:05:52 Duration
Lecture 2 Further Reading on Null and Alternative Hypot
Lecture 3 Rejection Region and Significance Level 00:07:05 Duration
Lecture 4 Type I Error and Type II Error 00:04:14 Duration
Lecture 5 Test for the Mean. Population Variance Known 00:06:34 Duration
Lecture 6 2 4.4.+Test+for+the+mean.+Population+variance+
Lecture 7 p-value 00:04:13 Duration
Lecture 8 Test for the Mean. Population Variance Unknow 00:04:49 Duration
Lecture 9 2 4.6.Test-for-the-mean.Population-variance-u
Lecture 10 Test for the Mean. Dependent Samples 00:05:18 Duration
Lecture 11 2 4.7.+Test+for+the+mean.+Dependent+samples_ex
Lecture 12 Test for the mean. Independent Samples (Part 00:04:22 Duration
Lecture 13 2 4.8.Test-for-the-mean.Independent-samples-P
Lecture 14 Test for the mean. Independent Samples (Part 00:04:27 Duration
Lecture 15 2 4.9.Test-for-the-mean.Independent-samples-P

Section 21 : Statistics - Practical Example Hypothesis Testing
Lecture 1 Practical Example Hypothesis Testing 00:07:16 Duration
Lecture 2 2 4.10.Hypothesis-testing-section-practical-e

Section 22 : Part 4 Introduction to Python
Lecture 1 Introduction to Programming 00:05:04 Duration
Lecture 2 Why Python 00:05:11 Duration
Lecture 3 Why Jupyter 00:03:29 Duration
Lecture 4 Installing Python and Jupyter 00:06:49 Duration
Lecture 5 Understanding Jupyter's Interface - the Noteb 00:03:16 Duration
Lecture 6 Prerequisites for Coding in the Jupyter Noteb 00:06:15 Duration

Section 23 : Python - Variables and Data Types
Lecture 1 Variables 00:04:52 Duration
Lecture 2 Numbers and Boolean Values in Python 00:03:06 Duration
Lecture 3 Python Strings 00:11:20 Duration

Section 24 : Python - Basic Python Syntax
Lecture 1 Using Arithmetic Operators in Python 00:03:24 Duration
Lecture 2 The Double Equality Sign 00:01:34 Duration
Lecture 3 How to Reassign Values 00:01:08 Duration
Lecture 4 Add Comments 00:03:20 Duration
Lecture 5 Understanding Line Continuation 00:00:50 Duration
Lecture 6 Indexing Elements 00:01:18 Duration
Lecture 7 Structuring with Indentation 00:03:42 Duration

Section 25 : Python - Other Python Operators
Lecture 1 Comparison Operators 00:02:10 Duration
Lecture 2 Logical and Identity Operators 00:05:36 Duration

Section 26 : Python - Conditional Statements
Lecture 1 The IF Statement 00:06:14 Duration
Lecture 2 The ELSE Statement 00:06:14 Duration
Lecture 3 The ELIF Statement 00:11:16 Duration
Lecture 4 A Note on Boolean Values 00:04:39 Duration

Section 27 : Python - Python Functions
Lecture 1 Defining a Function in Python 00:04:20 Duration
Lecture 2 How to Create a Function with a Parameter 00:07:58 Duration
Lecture 3 Defining a Function in Python - Part II 00:05:29 Duration
Lecture 4 How to Use a Function within a Function 00:01:49 Duration
Lecture 5 Conditional Statements and Functions 00:03:07 Duration
Lecture 6 Functions Containing a Few Arguments 00:02:48 Duration
Lecture 7 Built-in Functions in Python 00:03:56 Duration

Section 28 : Python - Sequences
Lecture 1 Lists 00:08:18 Duration
Lecture 2 Using Methods 00:04:31 Duration
Lecture 3 List Slicing 00:04:31 Duration
Lecture 4 Tuples 00:06:40 Duration
Lecture 5 Dictionaries 00:08:27 Duration

Section 29 : Python - Iterations
Lecture 1 For Loops 00:05:40 Duration
Lecture 2 While Loops and Incrementing 00:05:11 Duration
Lecture 3 Lists with the range() Function 00:06:22 Duration
Lecture 4 Conditional Statements and Loops 00:06:30 Duration
Lecture 5 Conditional Statements, Functions, and Loops 00:02:27 Duration
Lecture 6 How to Iterate over Dictionaries 00:06:22 Duration

Section 30 : Python - Advanced Python Tools
Lecture 1 Object Oriented Programming 00:05:00 Duration
Lecture 2 Modules and Packages 00:01:06 Duration
Lecture 3 What is the Standard Library 00:02:47 Duration
Lecture 4 Importing Modules in Python 00:04:04 Duration

Section 31 : Part 5 Advanced Statistical Methods in Pytho
Lecture 1 Introduction to Regression Analysis 00:01:28 Duration

Section 32 : Advanced Statistical Methods - Linear Regression

Lecture 1 The Linear Regression Model 00:05:50 Duration
Lecture 2 Correlation vs Regression 00:01:44 Duration
Lecture 3 Geometrical Representation of the Linear Regr 00:01:26 Duration
Lecture 4 Python Packages Installation 00:04:40 Duration
Lecture 5 First Regression in Python 00:07:11 Duration
Lecture 6 First Regression in Python Exercise
Lecture 7 Using Seaborn for Graphs 00:01:22 Duration
Lecture 8 How to Interpret the Regression Table 00:05:47 Duration
Lecture 9 Decomposition of Variability 00:03:38 Duration
Lecture 10 What is the OLS 00:03:14 Duration
Lecture 11 R-Squared 00:05:30 Duration

Section 33 : Advanced Statistical Methods - Multiple Linear Re
Lecture 1 Multiple Linear Regression 00:02:56 Duration
Lecture 2 Adjusted R-Squared 00:06:01 Duration
Lecture 3 Remove - INTRODUCTION TO BRAINMEASURES PROCTO
Lecture 4 Test for Significance of the Model (F-Test) 00:02:01 Duration
Lecture 5 OLS Assumptions 00:02:21 Duration
Lecture 6 A1 Linearity 00:01:51 Duration
Lecture 7 A2 No Endogeneity 00:04:10 Duration
Lecture 8 A3 Normality and Homoscedasticity 00:05:48 Duration
Lecture 9 A4 No Autocorrelation 00:03:31 Duration
Lecture 10 A5 No Multicollinearity 00:03:26 Duration
Lecture 11 Dealing with Categorical Data - Dummy Variabl 00:06:44 Duration
Lecture 12 Dealing with Categorical Data DummyVariables
Lecture 13 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM

Section 34 : Advanced Statistical Methods - Linear Regression

Lecture 1 What is sklearn and How is it Different from 00:02:15 Duration
Lecture 2 How are we Going to Approach this Section 00:01:56 Duration
Lecture 3 Simple Linear Regression with sklearn 00:05:38 Duration
Lecture 4 Simple Linear Regression with sklearn - A Sta 00:04:49 Duration
Lecture 5 A Note on Normalization
Lecture 6 Multiple Linear Regression with sklearn 00:03:11 Duration
Lecture 7 Multiple Linear Regression with sklearn 00:03:11 Duration
Lecture 8 Calculating the Adjusted R-Squared in sklearn 00:04:46 Duration
Lecture 9 Feature Selection (F-regression) 00:04:41 Duration
Lecture 10 Feature Selection (F-regression) 00:04:46 Duration
Lecture 11 A Note on Calculation of P-values with sklear
Lecture 12 Creating a Summary Table with P-values 00:02:10 Duration
Lecture 13 Feature Scaling (Standardization) 00:05:38 Duration
Lecture 14 Feature Scaling (Standardization) 00:05:38 Duration
Lecture 15 Feature Selection through Standardization of 00:05:23 Duration
Lecture 16 Predicting with the Standardized Coefficients 00:03:53 Duration
Lecture 17 Underfitting and Overfitting 00:02:42 Duration
Lecture 18 Underfitting and Overfitting 00:02:42 Duration
Lecture 19 Train - Test Split Explained 00:06:54 Duration

Section 35 : Advanced Statistical Methods - Practical Example

Lecture 1 Practical Example Linear Regression 00:12:00 Duration
Lecture 2 Practical Example Linear Regression 00:06:12 Duration
Lecture 3 A Note on Multicollinearity
Lecture 4 Practical Example Linear Regression 00:03:16 Duration
Lecture 5 Practical Example Linear Regression 00:08:10 Duration
Lecture 6 Practical Example Linear Regression 00:08:10 Duration
Lecture 7 Dummy Variables
Lecture 8 Practical Example Linear Regression 00:07:35 Duration
Lecture 9 Linear Regression - Exercise

Section 36 : Advanced Statistical Methods - Logistic Regressio
Lecture 1 Introduction to Logistic Regression 00:01:20 Duration
Lecture 2 A Simple Example in Python 00:04:42 Duration
Lecture 3 Logistic vs Logit Function 00:04:00 Duration
Lecture 4 Building a Logistic Regression 00:02:48 Duration
Lecture 5 Example_bank_data
Lecture 6 An Invaluable Coding Tip 00:02:27 Duration
Lecture 7 Understanding Logistic Regression Tables
Lecture 8 Bank_data
Lecture 9 What do the Odds Actually Mean 00:04:30 Duration
Lecture 10 Binary Predictors in a Logistic Regression 00:04:32 Duration
Lecture 11 Bank_data
Lecture 12 Calculating the Accuracy of the Model 00:03:22 Duration
Lecture 13 Bank_data
Lecture 14 Underfitting and Overfitting 00:03:43 Duration
Lecture 15 Testing the Model 00:05:05 Duration
Lecture 16 Bank_data

Section 37 : Advanced Statistical Methods - Cluster Analysis
Lecture 1 Introduction to Cluster Analysis 00:03:41 Duration
Lecture 2 Some Examples of Clusters 00:04:32 Duration
Lecture 3 Difference between Classification and Cluster 00:02:32 Duration
Lecture 4 Math Prerequisites 00:03:20 Duration

Section 38 : Advanced Statistical Methods - K-Means Clustering
Lecture 1 K-Means Clustering 00:04:41 Duration
Lecture 2 A Simple Example of Clustering 00:01:47 Duration
Lecture 3 Countries_exercise
Lecture 4 Clustering Categorical Data 00:02:50 Duration
Lecture 5 Categorical
Lecture 6 How to Choose the Number of Clusters 00:06:11 Duration
Lecture 7 Countries_exercise
Lecture 8 Pros and Cons of K-Means Clustering 00:03:24 Duration
Lecture 9 To Standardize or not to Standardize 00:04:33 Duration
Lecture 10 Relationship between Clustering and Regressio 00:01:32 Duration
Lecture 11 Market Segmentation with Cluster Analysis (Pa 00:06:04 Duration
Lecture 12 Market Segmentation with Cluster Analysis (Pa 00:06:59 Duration
Lecture 13 How is Clustering Useful 00:04:48 Duration
Lecture 14 EXERCISE: Species Segmentation with Cluster A
Lecture 15 EXERCISE: Species Segmentation with Cluster A

Section 39 : Advanced Statistical Methods - Other Types of Clustering
Lecture 1 Types of Clustering 00:03:40 Duration
Lecture 2 Dendrogram 00:05:21 Duration
Lecture 3 Heatmaps 00:04:34 Duration

Section 40 : Part 6 Mathematics
Lecture 1 What is a Matrix 00:03:40 Duration
Lecture 2 Scalars and Vectors 00:02:59 Duration
Lecture 3 Linear Algebra and Geometry 00:03:06 Duration
Lecture 4 Arrays in Python - A Convenient Way To Represent M 00:05:09 Duration
Lecture 5 What is a Tensor 00:03:00 Duration
Lecture 6 Addition and Subtraction of Matrices 00:03:36 Duration
Lecture 7 Errors when Adding Matrices 00:02:01 Duration
Lecture 8 Transpose of a Matrix 00:05:13 Duration
Lecture 9 Dot Product 00:03:48 Duration
Lecture 10 Dot Product of Matrices 00:08:23 Duration
Lecture 11 Why is Linear Algebra Useful 00:10:10 Duration