Course Content
Subtitle Guide – Hướng dẫn thêm phụ đề
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01 – Part 1 Introduction
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001 A Practical Example What You Will Learn in This Course
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002 What Does the Course Cover
03:34
02 – The Field of Data Science – The Various Data Science Disciplines
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004 Data Science and Business Buzzwords Why are there so Many
05:21
006 What is the difference between Analysis and Analytics
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008 Business Analytics, Data Analytics, and Data Science An Introduction
08:26
010 Continuing with BI, ML, and AI
09:31
012 A Breakdown of our Data Science Infographic
04:03
03 – The Field of Data Science – Connecting the Data Science Disciplines
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014 Applying Traditional Data, Big Data, BI, Traditional Data Science and ML_part1
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014 Applying Traditional Data, Big Data, BI, Traditional Data Science and ML_part2
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04 – The Field of Data Science – The Benefits of Each Discipline
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015 The Reason Behind These Disciplines
04:44
05 – The Field of Data Science – Popular Data Science Techniques
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017 Techniques for Working with Traditional Data_part1
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017 Techniques for Working with Traditional Data_part2
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019 Real Life Examples of Traditional Data
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020 Techniques for Working with Big Data
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022 Real Life Examples of Big Data
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023 Business Intelligence (BI) Techniques
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025 Real Life Examples of Business Intelligence (BI)
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026 Techniques for Working with Traditional Methods
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028 Real Life Examples of Traditional Methods
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029 Machine Learning (ML) Techniques
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031 Types of Machine Learning
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033 Real Life Examples of Machine Learning (ML)
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06 – The Field of Data Science – Popular Data Science Tools
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035 Necessary Programming Languages and Software Used in Data Science
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07 – The Field of Data Science – Careers in Data Science
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037 Finding the Job – What to Expect and What to Look for
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08 – The Field of Data Science – Debunking Common Misconceptions
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039 Debunking Common Misconceptions
04:10
09 – Part 2 Probability
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041 The Basic Probability Formula
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043 Computing Expected Values
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045 Frequency
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047 Events and Their Complements
05:26
10 – Probability – Combinatorics
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049 Fundamentals of Combinatorics
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051 Permutations and How to Use Them
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053 Simple Operations with Factorials
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055 Solving Variations with Repetition
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057 Solving Variations without Repetition
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059 Solving Combinations
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061 Symmetry of Combinations
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063 Solving Combinations with Separate Sample Spaces
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065 Combinatorics in Real-Life The Lottery
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067 A Recap of Combinatorics
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068 A Practical Example of Combinatorics_part1
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068 A Practical Example of Combinatorics_part2
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11 – Probability – Bayesian Inference
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069 Sets and Events
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071 Ways Sets Can Interact
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073 Intersection of Sets
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075 Union of Sets
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077 Mutually Exclusive Sets
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079 Dependence and Independence of Sets
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081 The Conditional Probability Formula
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083 The Law of Total Probability
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084 The Additive Rule
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086 The Multiplication Law
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088 Bayes’ Law
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090 A Practical Example of Bayesian Inference_part1
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090 A Practical Example of Bayesian Inference_part2
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12 – Probability – Distributions
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091 Fundamentals of Probability Distributions
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093 Types of Probability Distributions
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095 Characteristics of Discrete Distributions
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097 Discrete Distributions The Uniform Distribution
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099 Discrete Distributions The Bernoulli Distribution
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101 Discrete Distributions The Binomial Distribution
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103 Discrete Distributions The Poisson Distribution
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105 Characteristics of Continuous Distributions
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107 Continuous Distributions The Normal Distribution
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109 Continuous Distributions The Standard Normal Distribution
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111 Continuous Distributions The Students’ T Distribution
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113 Continuous Distributions The Chi-Squared Distribution
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115 Continuous Distributions The Exponential Distribution
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117 Continuous Distributions The Logistic Distribution
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119 A Practical Example of Probability Distributions_part1
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119 A Practical Example of Probability Distributions_part2
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13 – Probability – Probability in Other Fields
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120 Probability in Finance
07:46
121 Probability in Statistics
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122 Probability in Data Science
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14 – Part 3 Statistics
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123 Population and Sample
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15 – Statistics – Descriptive Statistics
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125 Types of Data
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127 Levels of Measurement
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129 Categorical Variables – Visualization Techniques
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132 Numerical Variables – Frequency Distribution Table
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135 The Histogram
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138 Cross Tables and Scatter Plots
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141 Mean, median and mode
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143 Skewness
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146 Variance
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148 Standard Deviation and Coefficient of Variation
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151 Covariance
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154 Correlation Coefficient
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16 – Statistics – Practical Example Descriptive Statistics
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157 Practical Example Descriptive Statistics_part1
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157 Practical Example Descriptive Statistics_part2
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17 – Statistics – Inferential Statistics Fundamentals
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159 Introduction
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160 What is a Distribution
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162 The Normal Distribution
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164 The Standard Normal Distribution
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167 Central Limit Theorem
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169 Standard error
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171 Estimators and Estimates
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18 – Statistics – Inferential Statistics Confidence Intervals
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173 What are Confidence Intervals
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175 Confidence Intervals; Population Variance Known; Z-score
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177 Confidence Interval Clarifications
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178 Student’s T Distribution
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180 Confidence Intervals; Population Variance Unknown; T-score
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182 Margin of Error
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184 Confidence intervals. Two means. Dependent samples
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186 Confidence intervals. Two means. Independent Samples (Part 1)
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188 Confidence intervals. Two means. Independent Samples (Part 2)
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190 Confidence intervals. Two means. Independent Samples (Part 3)
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19 – Statistics – Practical Example Inferential Statistics
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191 Practical Example Inferential Statistics
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20 – Statistics – Hypothesis Testing
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193 Null vs Alternative Hypothesis
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196 Rejection Region and Significance Level
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198 Type I Error and Type II Error
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200 Test for the Mean. Population Variance Known
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202 p-value
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204 Test for the Mean. Population Variance Unknown
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206 Test for the Mean. Dependent Samples
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208 Test for the mean. Independent Samples (Part 1)
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210 Test for the mean. Independent Samples (Part 2)
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21 – Statistics – Practical Example Hypothesis Testing
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213 Practical Example Hypothesis Testing
07:16
22 – Part 4 Introduction to Python
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215 Introduction to Programming
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217 Why Python
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219 Why Jupyter
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221 Installing Python and Jupyter
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222 Understanding Jupyter’s Interface – the Notebook Dashboard
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223 Prerequisites for Coding in the Jupyter Notebooks
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23 – Python – Variables and Data Types
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225 Variables
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231 Numbers and Boolean Values in Python
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238 Python Strings
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24 – Python – Basic Python Syntax
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245 Using Arithmetic Operators in Python
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255 The Double Equality Sign
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258 How to Reassign Values
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264 Add Comments
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266 Understanding Line Continuation
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268 Indexing Elements
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272 Structuring with Indentation
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25 – Python – Other Python Operators
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275 Comparison Operators
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281 Logical and Identity Operators
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26 – Python – Conditional Statements
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289 The IF Statement
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293 The ELSE Statement
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295 The ELIF Statement
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298 A Note on Boolean Values
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27 – Python – Python Functions
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300 Defining a Function in Python
02:02
301 How to Create a Function with a Parameter
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304 Defining a Function in Python – Part II
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306 How to Use a Function within a Function
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308 Conditional Statements and Functions
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310 Functions Containing a Few Arguments
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311 Built-in Functions in Python
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28 – Python – Sequences
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322 Lists
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329 Using Methods
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334 List Slicing
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342 Tuples
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347 Dictionaries
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29 – Python – Iterations
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355 For Loops
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359 While Loops and Incrementing
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361 Lists with the range() Function
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366 Conditional Statements and Loops
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370 Conditional Statements, Functions, and Loops
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372 How to Iterate over Dictionaries
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30 – Python – Advanced Python Tools
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375 Object Oriented Programming
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377 Modules and Packages
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379 What is the Standard Library
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381 Importing Modules in Python
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31 – Part 5 Advanced Statistical Methods in Python
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383 Introduction to Regression Analysis
01:27
32 – Advanced Statistical Methods – Linear Regression with StatsModels
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385 The Linear Regression Model
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387 Correlation vs Regression
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389 Geometrical Representation of the Linear Regression Model
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391 Python Packages Installation
04:39
392 First Regression in Python
07:11
33 – Advanced Statistical Methods – Multiple Linear Regression with StatsModels
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34 – Advanced Statistical Methods – Linear Regression with sklearn
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35 – Advanced Statistical Methods – Practical Example Linear Regression
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36 – Advanced Statistical Methods – Logistic Regression
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37 – Advanced Statistical Methods – Cluster Analysis
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38 – Advanced Statistical Methods – K-Means Clustering
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39 – Advanced Statistical Methods – Other Types of Clustering
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40 – ChatGPT for Data Science
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41 – Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis
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42 – Part 6 Mathematics
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43 – Part 7 Deep Learning
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44 – Deep Learning – Introduction to Neural Networks
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45 – Deep Learning – How to Build a Neural Network from Scratch with NumPy
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46 – Deep Learning – TensorFlow 2.0 Introduction
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47 – Deep Learning – Digging Deeper into NNs Introducing Deep Neural Networks
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576 What is a Deep Net
02:18
577 Digging into a Deep Net
04:58
578 Non-Linearities and their Purpose
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579 Activation Functions
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580 Activation Functions Softmax Activation
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581 Backpropagation
03:12
582 Backpropagation Picture
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48 – Deep Learning – Overfitting
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584 What is Overfitting
03:51
585 Underfitting and Overfitting for Classification
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586 What is Validation
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587 Training, Validation, and Test Datasets
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588 N-Fold Cross Validation
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589 Early Stopping or When to Stop Training
04:54
49 – Deep Learning – Initialization
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590 What is Initialization
02:32
591 Types of Simple Initializations
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592 State-of-the-Art Method – (Xavier) Glorot Initialization
02:45
50 – Deep Learning – Digging into Gradient Descent and Learning Rate Schedules
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593 Stochastic Gradient Descent
03:24
594 Problems with Gradient Descent
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595 Momentum
02:30
596 Learning Rate Schedules, or How to Choose the Optimal Learning Rate
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597 Learning Rate Schedules Visualized
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598 Adaptive Learning Rate Schedules (AdaGrad and RMSprop )
04:08
599 Adam (Adaptive Moment Estimation)
02:39
51 – Deep Learning – Preprocessing
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600 Preprocessing Introduction
02:51
601 Types of Basic Preprocessing
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602 Standardization
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603 Preprocessing Categorical Data
02:15
604 Binary and One-Hot Encoding
03:39
52 – Deep Learning – Classifying on the MNIST Dataset
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605 MNIST The Dataset
02:25
606 MNIST How to Tackle the MNIST
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607 MNIST Importing the Relevant Packages and Loading the Data
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608 MNIST Preprocess the Data – Create a Validation Set and Scale It
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610 MNIST Preprocess the Data – Shuffle and Batch
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612 MNIST Outline the Model
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613 MNIST Select the Loss and the Optimizer
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614 MNIST Learning
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616 MNIST Testing the Model
03:56
53 – Deep Learning – Business Case Example
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617 Business Case Exploring the Dataset and Identifying Predictors
07:54
618 Business Case Outlining the Solution
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619 Business Case Balancing the Dataset
03:39
620 Business Case Preprocessing the Data
11:32
622 Business Case Load the Preprocessed Data
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624 Business Case Learning and Interpreting the Result
04:15
625 Business Case Setting an Early Stopping Mechanism
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627 Business Case Testing the Model
01:23
54 – Deep Learning – Conclusion
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629 Summary on What You’ve Learned
03:41
630 What’s Further out there in terms of Machine Learning
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632 An overview of CNNs
04:55
633 An Overview of RNNs
02:50
634 An Overview of non-NN Approaches
03:52
55 – Appendix Deep Learning – TensorFlow 1 Introduction
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636 How to Install TensorFlow 1
02:20
638 TensorFlow Intro
03:46
639 Actual Introduction to TensorFlow
01:40
640 Types of File Formats, supporting Tensors
02:38
641 Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases
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642 Basic NN Example with TF Loss Function and Gradient Descent
03:41
643 Basic NN Example with TF Model Output
06:05
56 – Appendix Deep Learning – TensorFlow 1 Classifying on the MNIST Dataset
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645 MNIST What is the MNIST Dataset
02:26
646 MNIST How to Tackle the MNIST
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647 MNIST Relevant Packages
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648 MNIST Model Outline
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649 MNIST Loss and Optimization Algorithm
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650 Calculating the Accuracy of the Model
04:18
651 MNIST Batching and Early Stopping
02:08
652 MNIST Learning
07:35
653 MNIST Results and Testing
06:11
The Data Science Course: Complete Data Science Bootcamp 2024
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