Udemy - The Data Science Course Complete Data Science Bootcamp 2025 12 2024

1. 01. Part 1 Introduction/01. A Practical Example What You Will Learn in This Course.mp410.76MB
2. 01. Part 1 Introduction/02. What Does the Course Cover.mp49.56MB
3. 02. The Field of Data Science - The Various Data Science Disciplines/04. Continuing with BI, ML, and AI.mp447.55MB
4. 02. The Field of Data Science - The Various Data Science Disciplines/07. A Breakdown of our Data Science Infographic.mp445.37MB
5. 02. The Field of Data Science - The Various Data Science Disciplines/06. More Examples of Generative AI.mp430.54MB
6. 02. The Field of Data Science - The Various Data Science Disciplines/05. Traditional AI vs. Generative AI.mp424.51MB
7. 02. The Field of Data Science - The Various Data Science Disciplines/01. Data Science and Business Buzzwords Why are there so Many.mp415.59MB
8. 02. The Field of Data Science - The Various Data Science Disciplines/03. Business Analytics, Data Analytics, and Data Science An Introduction.mp414.6MB
9. 02. The Field of Data Science - The Various Data Science Disciplines/02. What is the difference between Analysis and Analytics.mp411.16MB
10. 03. The Field of Data Science - Connecting the Data Science Disciplines/01. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp483.53MB
11. 04. The Field of Data Science - The Benefits of Each Discipline/01. The Reason Behind These Disciplines.mp446.77MB
12. 05. The Field of Data Science - Popular Data Science Techniques/01. Techniques for Working with Traditional Data.mp4107.18MB
13. 05. The Field of Data Science - Popular Data Science Techniques/07. Techniques for Working with Traditional Methods.mp476.01MB
14. 05. The Field of Data Science - Popular Data Science Techniques/10. Types of Machine Learning.mp469.45MB
15. 05. The Field of Data Science - Popular Data Science Techniques/03. Techniques for Working with Big Data.mp462.12MB
16. 05. The Field of Data Science - Popular Data Science Techniques/05. Business Intelligence (BI) Techniques.mp452.91MB
17. 05. The Field of Data Science - Popular Data Science Techniques/09. Machine Learning (ML) Techniques.mp449.42MB
18. 05. The Field of Data Science - Popular Data Science Techniques/08. Real Life Examples of Traditional Methods.mp436.74MB
19. 05. The Field of Data Science - Popular Data Science Techniques/12. Real Life Examples of Machine Learning (ML).mp427.74MB
20. 05. The Field of Data Science - Popular Data Science Techniques/11. Evolution and Latest Trends of Machine Learning (ML).mp427.32MB
21. 05. The Field of Data Science - Popular Data Science Techniques/06. Real Life Examples of Business Intelligence (BI).mp424.62MB
22. 05. The Field of Data Science - Popular Data Science Techniques/02. Real Life Examples of Traditional Data.mp418.37MB
23. 05. The Field of Data Science - Popular Data Science Techniques/04. Real Life Examples of Big Data.mp413.05MB
24. 06. The Field of Data Science - Popular Data Science Tools/01. Necessary Programming Languages and Software Used in Data Science.mp482.38MB
25. 07. The Field of Data Science - Careers in Data Science/01. Finding the Job - What to Expect and What to Look for.mp440.03MB
26. 08. The Field of Data Science - Debunking Common Misconceptions/01. Debunking Common Misconceptions.mp458.86MB
27. 09. Part 2 Probability/02. Computing Expected Values.mp445.66MB
28. 09. Part 2 Probability/03. Frequency.mp437.37MB
29. 09. Part 2 Probability/01. The Basic Probability Formula.mp429.4MB
30. 09. Part 2 Probability/04. Events and Their Complements.mp425.84MB
31. 10. Probability - Combinatorics/11. A Practical Example of Combinatorics.mp480.69MB
32. 10. Probability - Combinatorics/06. Solving Combinations.mp423.64MB
33. 10. Probability - Combinatorics/08. Solving Combinations with Separate Sample Spaces.mp420.3MB
34. 10. Probability - Combinatorics/05. Solving Variations without Repetition.mp418.25MB
35. 10. Probability - Combinatorics/02. Permutations and How to Use Them.mp417.52MB
36. 10. Probability - Combinatorics/09. Combinatorics in Real-Life The Lottery.mp416.38MB
37. 10. Probability - Combinatorics/04. Solving Variations with Repetition.mp413.95MB
38. 10. Probability - Combinatorics/07. Symmetry of Combinations.mp413.74MB
39. 10. Probability - Combinatorics/10. A Recap of Combinatorics.mp412.1MB
40. 10. Probability - Combinatorics/03. Simple Operations with Factorials.mp410.51MB
41. 10. Probability - Combinatorics/01. Fundamentals of Combinatorics.mp45.94MB
42. 11. Probability - Bayesian Inference/12. A Practical Example of Bayesian Inference.mp4139.24MB
43. 11. Probability - Bayesian Inference/04. Union of Sets.mp424.18MB
44. 11. Probability - Bayesian Inference/11. Bayes' Law.mp421.35MB
45. 11. Probability - Bayesian Inference/10. The Multiplication Law.mp420.19MB
46. 11. Probability - Bayesian Inference/07. The Conditional Probability Formula.mp420.07MB
47. 11. Probability - Bayesian Inference/01. Sets and Events.mp417.67MB
48. 11. Probability - Bayesian Inference/06. Dependence and Independence of Sets.mp414.92MB
49. 11. Probability - Bayesian Inference/08. The Law of Total Probability.mp414.21MB
50. 11. Probability - Bayesian Inference/02. Ways Sets Can Interact.mp411.33MB
51. 11. Probability - Bayesian Inference/09. The Additive Rule.mp411.09MB
52. 11. Probability - Bayesian Inference/03. Intersection of Sets.mp411.02MB
53. 11. Probability - Bayesian Inference/05. Mutually Exclusive Sets.mp410.58MB
54. 12. Probability - Distributions/15. A Practical Example of Probability Distributions.mp4138.31MB
55. 12. Probability - Distributions/02. Types of Probability Distributions.mp435.59MB
56. 12. Probability - Distributions/06. Discrete Distributions The Binomial Distribution.mp430.6MB
57. 12. Probability - Distributions/07. Discrete Distributions The Poisson Distribution.mp423.92MB
58. 12. Probability - Distributions/08. Characteristics of Continuous Distributions.mp421.25MB
59. 12. Probability - Distributions/10. Continuous Distributions The Standard Normal Distribution.mp421.11MB
60. 12. Probability - Distributions/09. Continuous Distributions The Normal Distribution.mp420.01MB
61. 12. Probability - Distributions/01. Fundamentals of Probability Distributions.mp419.42MB
62. 12. Probability - Distributions/14. Continuous Distributions The Logistic Distribution.mp416.18MB
63. 12. Probability - Distributions/13. Continuous Distributions The Exponential Distribution.mp415.99MB
64. 12. Probability - Distributions/05. Discrete Distributions The Bernoulli Distribution.mp415.12MB
65. 12. Probability - Distributions/12. Continuous Distributions The Chi-Squared Distribution.mp411.16MB
66. 12. Probability - Distributions/04. Discrete Distributions The Uniform Distribution.mp410.3MB
67. 12. Probability - Distributions/03. Characteristics of Discrete Distributions.mp49.42MB
68. 12. Probability - Distributions/11. Continuous Distributions The Students' T Distribution.mp49.24MB
69. 13. Probability - Probability in Other Fields/01. Probability in Finance.mp440.35MB
70. 13. Probability - Probability in Other Fields/02. Probability in Statistics.mp431.6MB
71. 13. Probability - Probability in Other Fields/03. Probability in Data Science.mp414.24MB
72. 14. Part 3 Statistics/01. Population and Sample.mp435.1MB
73. 15. Statistics - Descriptive Statistics/01. Types of Data.mp443.19MB
74. 15. Statistics - Descriptive Statistics/02. Levels of Measurement.mp432.19MB
75. 15. Statistics - Descriptive Statistics/03. Categorical Variables - Visualization Techniques.mp427.49MB
76. 15. Statistics - Descriptive Statistics/11. Mean, median and mode.mp424.5MB
77. 15. Statistics - Descriptive Statistics/15. Variance.mp423.55MB
78. 15. Statistics - Descriptive Statistics/17. Standard Deviation and Coefficient of Variation.mp420.12MB
79. 15. Statistics - Descriptive Statistics/09. Cross Tables and Scatter Plots.mp419.7MB
80. 15. Statistics - Descriptive Statistics/21. Correlation Coefficient.mp419.32MB
81. 15. Statistics - Descriptive Statistics/19. Covariance.mp418.37MB
82. 15. Statistics - Descriptive Statistics/05. Numerical Variables - Frequency Distribution Table.mp417.7MB
83. 15. Statistics - Descriptive Statistics/13. Skewness.mp413.32MB
84. 15. Statistics - Descriptive Statistics/07. The Histogram.mp49.58MB
85. 16. Statistics - Practical Example Descriptive Statistics/01. Practical Example Descriptive Statistics.mp4130.53MB
86. 17. Statistics - Inferential Statistics Fundamentals/08. Estimators and Estimates.mp427.69MB
87. 17. Statistics - Inferential Statistics Fundamentals/06. Central Limit Theorem.mp423.22MB
88. 17. Statistics - Inferential Statistics Fundamentals/02. What is a Distribution.mp417.2MB
89. 17. Statistics - Inferential Statistics Fundamentals/07. Standard error.mp413.51MB
90. 17. Statistics - Inferential Statistics Fundamentals/03. The Normal Distribution.mp413.07MB
91. 17. Statistics - Inferential Statistics Fundamentals/04. The Standard Normal Distribution.mp48.6MB
92. 17. Statistics - Inferential Statistics Fundamentals/01. Introduction.mp43.06MB
93. 18. Statistics - Inferential Statistics Confidence Intervals/02. Confidence Intervals; Population Variance Known; Z-score.mp452.16MB
94. 18. Statistics - Inferential Statistics Confidence Intervals/09. Confidence intervals. Two means. Dependent samples.mp444.98MB
95. 18. Statistics - Inferential Statistics Confidence Intervals/01. What are Confidence Intervals.mp428.61MB
96. 18. Statistics - Inferential Statistics Confidence Intervals/08. Margin of Error.mp423.1MB
97. 18. Statistics - Inferential Statistics Confidence Intervals/04. Confidence Interval Clarifications.mp418.93MB
98. 18. Statistics - Inferential Statistics Confidence Intervals/13. Confidence intervals. Two means. Independent Samples (Part 2).mp414.62MB
99. 18. Statistics - Inferential Statistics Confidence Intervals/06. Confidence Intervals; Population Variance Unknown; T-score.mp413.69MB
100. 18. Statistics - Inferential Statistics Confidence Intervals/05. Student's T Distribution.mp413.68MB
101. 18. Statistics - Inferential Statistics Confidence Intervals/11. Confidence intervals. Two means. Independent Samples (Part 1).mp411.99MB
102. 18. Statistics - Inferential Statistics Confidence Intervals/15. Confidence intervals. Two means. Independent Samples (Part 3).mp46.88MB
103. 19. Statistics - Practical Example Inferential Statistics/01. Practical Example Inferential Statistics.mp469.01MB
104. 20. Statistics - Hypothesis Testing/03. Rejection Region and Significance Level.mp438.68MB
105. 20. Statistics - Hypothesis Testing/05. Test for the Mean. Population Variance Known.mp436.94MB
106. 20. Statistics - Hypothesis Testing/07. p-value.mp433.75MB
107. 20. Statistics - Hypothesis Testing/10. Test for the Mean. Dependent Samples.mp432.76MB
108. 20. Statistics - Hypothesis Testing/01. Null vs Alternative Hypothesis.mp431.94MB
109. 20. Statistics - Hypothesis Testing/14. Test for the mean. Independent Samples (Part 2).mp424.45MB
110. 20. Statistics - Hypothesis Testing/08. Test for the Mean. Population Variance Unknown.mp419.7MB
111. 20. Statistics - Hypothesis Testing/12. Test for the mean. Independent Samples (Part 1).mp415.42MB
112. 20. Statistics - Hypothesis Testing/04. Type I Error and Type II Error.mp415.29MB
113. 21. Statistics - Practical Example Hypothesis Testing/01. Practical Example Hypothesis Testing.mp445.83MB
114. 22. Part 4 Introduction to Python/06. Prerequisites for Coding in the Jupyter Notebooks.mp418.96MB
115. 22. Part 4 Introduction to Python/04. Installing Python and Jupyter.mp418.81MB
116. 22. Part 4 Introduction to Python/01. Introduction to Programming.mp414.87MB
117. 22. Part 4 Introduction to Python/02. Why Python.mp412.19MB
118. 22. Part 4 Introduction to Python/03. Why Jupyter.mp48MB
119. 22. Part 4 Introduction to Python/05. Understanding Jupyter's Interface - the Notebook Dashboard.mp46.07MB
120. 23. Python - Variables and Data Types/03. Python Strings.mp419.72MB
121. 23. Python - Variables and Data Types/01. Variables.mp48.94MB
122. 23. Python - Variables and Data Types/02. Numbers and Boolean Values in Python.mp46.57MB
123. 24. Python - Basic Python Syntax/01. Using Arithmetic Operators in Python.mp48.63MB
124. 24. Python - Basic Python Syntax/07. Structuring with Indentation.mp42.79MB
125. 24. Python - Basic Python Syntax/02. The Double Equality Sign.mp42.72MB
126. 24. Python - Basic Python Syntax/04. Add Comments.mp42.41MB
127. 24. Python - Basic Python Syntax/06. Indexing Elements.mp42.36MB
128. 24. Python - Basic Python Syntax/03. How to Reassign Values.mp41.86MB
129. 24. Python - Basic Python Syntax/05. Understanding Line Continuation.mp41.2MB
130. 25. Python - Other Python Operators/02. Logical and Identity Operators.mp419.01MB
131. 25. Python - Other Python Operators/01. Comparison Operators.mp44.16MB
132. 26. Python - Conditional Statements/03. The ELIF Statement.mp414.23MB
133. 26. Python - Conditional Statements/01. The IF Statement.mp46.71MB
134. 26. Python - Conditional Statements/02. The ELSE Statement.mp46.04MB
135. 26. Python - Conditional Statements/04. A Note on Boolean Values.mp44.24MB
136. 27. Python - Python Functions/07. Built-in Functions in Python.mp410.19MB
137. 27. Python - Python Functions/02. How to Create a Function with a Parameter.mp49.99MB
138. 27. Python - Python Functions/03. Defining a Function in Python - Part II.mp46.45MB
139. 27. Python - Python Functions/05. Conditional Statements and Functions.mp46.05MB
140. 27. Python - Python Functions/04. How to Use a Function within a Function.mp43.24MB
141. 27. Python - Python Functions/01. Defining a Function in Python.mp43.23MB
142. 27. Python - Python Functions/06. Functions Containing a Few Arguments.mp42.81MB
143. 28. Python - Sequences/05. Dictionaries.mp432.44MB
144. 28. Python - Sequences/02. Using Methods.mp430.36MB
145. 28. Python - Sequences/01. Lists.mp423.04MB
146. 28. Python - Sequences/03. List Slicing.mp419.17MB
147. 28. Python - Sequences/04. Tuples.mp418.21MB
148. 29. Python - Iterations/02. While Loops and Incrementing.mp420.18MB
149. 29. Python - Iterations/06. How to Iterate over Dictionaries.mp418.38MB
150. 29. Python - Iterations/04. Conditional Statements and Loops.mp417.31MB
151. 29. Python - Iterations/03. Lists with the range() Function.mp416.07MB
152. 29. Python - Iterations/01. For Loops.mp412.96MB
153. 29. Python - Iterations/05. Conditional Statements, Functions, and Loops.mp44.27MB
154. 30. Python - Advanced Python Tools/04. Importing Modules in Python.mp49.87MB
155. 30. Python - Advanced Python Tools/01. Object Oriented Programming.mp48.66MB
156. 30. Python - Advanced Python Tools/03. What is the Standard Library.mp45.05MB
157. 30. Python - Advanced Python Tools/02. Modules and Packages.mp42.08MB
158. 31. Part 5 Advanced Statistical Methods in Python/01. Introduction to Regression Analysis.mp43.59MB
159. 32. Advanced Statistical Methods - Linear Regression with StatsModels/05. First Regression in Python.mp429.6MB
160. 32. Advanced Statistical Methods - Linear Regression with StatsModels/08. How to Interpret the Regression Table.mp428.73MB
161. 32. Advanced Statistical Methods - Linear Regression with StatsModels/04. Python Packages Installation.mp423.67MB
162. 32. Advanced Statistical Methods - Linear Regression with StatsModels/10. What is the OLS.mp422.46MB
163. 32. Advanced Statistical Methods - Linear Regression with StatsModels/01. The Linear Regression Model.mp413.48MB
164. 32. Advanced Statistical Methods - Linear Regression with StatsModels/11. R-Squared.mp411.2MB
165. 32. Advanced Statistical Methods - Linear Regression with StatsModels/09. Decomposition of Variability.mp48.79MB
166. 32. Advanced Statistical Methods - Linear Regression with StatsModels/07. Using Seaborn for Graphs.mp47.38MB
167. 32. Advanced Statistical Methods - Linear Regression with StatsModels/02. Correlation vs Regression.mp43.84MB
168. 32. Advanced Statistical Methods - Linear Regression with StatsModels/03. Geometrical Representation of the Linear Regression Model.mp42.27MB
169. 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/02. Adjusted R-Squared.mp434.2MB
170. 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/08. A3 Normality and Homoscedasticity.mp427.36MB
171. 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/11. Dealing with Categorical Data - Dummy Variables.mp422.65MB
172. 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/13. Making Predictions with the Linear Regression.mp416.34MB
173. 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/07. A2 No Endogeneity.mp49.24MB
174. 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/09. A4 No Autocorrelation.mp47.9MB
175. 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/10. A5 No Multicollinearity.mp47.6MB
176. 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/04. Test for Significance of the Model (F-Test).mp47.18MB
177. 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/01. Multiple Linear Regression.mp45.68MB
178. 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/05. OLS Assumptions.mp45.26MB
179. 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/06. A1 Linearity.mp43.57MB
180. 34. Advanced Statistical Methods - Linear Regression with sklearn/19. Train - Test Split Explained.mp435.57MB
181. 34. Advanced Statistical Methods - Linear Regression with sklearn/03. Simple Linear Regression with sklearn.mp427.45MB
182. 34. Advanced Statistical Methods - Linear Regression with sklearn/15. Feature Selection through Standardization of Weights.mp424.46MB
183. 34. Advanced Statistical Methods - Linear Regression with sklearn/04. Simple Linear Regression with sklearn - A StatsModels-like Summary Table.mp422.29MB
184. 34. Advanced Statistical Methods - Linear Regression with sklearn/10. Feature Selection (F-regression).mp420.51MB
185. 34. Advanced Statistical Methods - Linear Regression with sklearn/16. Predicting with the Standardized Coefficients.mp420.44MB
186. 34. Advanced Statistical Methods - Linear Regression with sklearn/14. Feature Scaling (Standardization).mp420.37MB
187. 34. Advanced Statistical Methods - Linear Regression with sklearn/08. Calculating the Adjusted R-Squared in sklearn.mp416.9MB
188. 34. Advanced Statistical Methods - Linear Regression with sklearn/01. What is sklearn and How is it Different from Other Packages.mp48.48MB
189. 34. Advanced Statistical Methods - Linear Regression with sklearn/07. Multiple Linear Regression with sklearn.mp48.3MB
190. 34. Advanced Statistical Methods - Linear Regression with sklearn/12. Creating a Summary Table with P-values.mp46.45MB
191. 34. Advanced Statistical Methods - Linear Regression with sklearn/18. Underfitting and Overfitting.mp45.83MB
192. 34. Advanced Statistical Methods - Linear Regression with sklearn/02. How are we Going to Approach this Section.mp45.3MB
193. 35. Advanced Statistical Methods - Practical Example Linear Regression/01. Practical Example Linear Regression (Part 1).mp484.74MB
194. 35. Advanced Statistical Methods - Practical Example Linear Regression/08. Practical Example Linear Regression (Part 5).mp450.42MB
195. 35. Advanced Statistical Methods - Practical Example Linear Regression/06. Practical Example Linear Regression (Part 4).mp439.39MB
196. 35. Advanced Statistical Methods - Practical Example Linear Regression/02. Practical Example Linear Regression (Part 2).mp431.86MB
197. 35. Advanced Statistical Methods - Practical Example Linear Regression/04. Practical Example Linear Regression (Part 3).mp416.67MB
198. 36. Advanced Statistical Methods - Logistic Regression/10. Binary Predictors in a Logistic Regression.mp424.86MB
199. 36. Advanced Statistical Methods - Logistic Regression/03. Logistic vs Logit Function.mp423.74MB
200. 36. Advanced Statistical Methods - Logistic Regression/02. A Simple Example in Python.mp421.88MB
201. 36. Advanced Statistical Methods - Logistic Regression/15. Testing the Model.mp421.59MB
202. 36. Advanced Statistical Methods - Logistic Regression/12. Calculating the Accuracy of the Model.mp420.26MB
203. 36. Advanced Statistical Methods - Logistic Regression/06. An Invaluable Coding Tip.mp418.78MB
204. 36. Advanced Statistical Methods - Logistic Regression/07. Understanding Logistic Regression Tables.mp414.59MB
205. 36. Advanced Statistical Methods - Logistic Regression/09. What do the Odds Actually Mean.mp411.39MB
206. 36. Advanced Statistical Methods - Logistic Regression/04. Building a Logistic Regression.mp48.59MB
207. 36. Advanced Statistical Methods - Logistic Regression/14. Underfitting and Overfitting.mp47.49MB
208. 36. Advanced Statistical Methods - Logistic Regression/01. Introduction to Logistic Regression.mp45.87MB
209. 37. Advanced Statistical Methods - Cluster Analysis/02. Some Examples of Clusters.mp435.86MB
210. 37. Advanced Statistical Methods - Cluster Analysis/01. Introduction to Cluster Analysis.mp414.46MB
211. 37. Advanced Statistical Methods - Cluster Analysis/03. Difference between Classification and Clustering.mp49.67MB
212. 37. Advanced Statistical Methods - Cluster Analysis/04. Math Prerequisites.mp45.27MB
213. 38. Advanced Statistical Methods - K-Means Clustering/13. How is Clustering Useful.mp437.43MB
214. 38. Advanced Statistical Methods - K-Means Clustering/02. A Simple Example of Clustering.mp434.18MB
215. 38. Advanced Statistical Methods - K-Means Clustering/12. Market Segmentation with Cluster Analysis (Part 2).mp434.08MB
216. 38. Advanced Statistical Methods - K-Means Clustering/11. Market Segmentation with Cluster Analysis (Part 1).mp428.04MB
217. 38. Advanced Statistical Methods - K-Means Clustering/06. How to Choose the Number of Clusters.mp426.87MB
218. 38. Advanced Statistical Methods - K-Means Clustering/08. Pros and Cons of K-Means Clustering.mp411.12MB
219. 38. Advanced Statistical Methods - K-Means Clustering/09. To Standardize or not to Standardize.mp410.92MB
220. 38. Advanced Statistical Methods - K-Means Clustering/01. K-Means Clustering.mp410.82MB
221. 38. Advanced Statistical Methods - K-Means Clustering/04. Clustering Categorical Data.mp410.35MB
222. 38. Advanced Statistical Methods - K-Means Clustering/10. Relationship between Clustering and Regression.mp43.51MB
223. 39. Advanced Statistical Methods - Other Types of Clustering/03. Heatmaps.mp418.5MB
224. 39. Advanced Statistical Methods - Other Types of Clustering/02. Dendrogram.mp418.29MB
225. 39. Advanced Statistical Methods - Other Types of Clustering/01. Types of Clustering.mp49.01MB
226. 40. ChatGPT for Data Science/05. First attempt at machine learning with ChatGPT.mp436.72MB
227. 40. ChatGPT for Data Science/10. Exploratory data analysis (EDA) with ChatGPT - correlation matrix, outlier detec.mp433.71MB
228. 40. ChatGPT for Data Science/19. Using ChatGPT for ethical considerations.mp433.53MB
229. 40. ChatGPT for Data Science/14. Decoding comic book data Python Regular Expressions and ChatGPT.mp433.05MB
230. 40. ChatGPT for Data Science/04. Data Preprocessing with ChatGPT.mp428.73MB
231. 40. ChatGPT for Data Science/08. Analyzing a client database with ChatGPT in Python – analyzing top clients, RFM.mp427.19MB
232. 40. ChatGPT for Data Science/01. Traditional data science methods and the role of ChatGPT.mp426.16MB
233. 40. ChatGPT for Data Science/06. Analyzing a client database with ChatGPT in Python.mp421.62MB
234. 40. ChatGPT for Data Science/09. Exploratory data analysis (EDA) with ChatGPT - histogram and scatter plot.mp421.59MB
235. 40. ChatGPT for Data Science/17. Algorithm recommendation recommendation engine for movies with ChatGPT.mp417.85MB
236. 40. ChatGPT for Data Science/16. Algorithm recommendation Movie Database Analysis with ChatGPT.mp417.25MB
237. 40. ChatGPT for Data Science/07. Analyzing a client database with ChatGPT in Python – analyzing top products.mp415.17MB
238. 40. ChatGPT for Data Science/13. Marvels comic book database Intro to Regular Expressions (RegEx).mp414.98MB
239. 40. ChatGPT for Data Science/18. Ethical principles in data and AI utilization.mp414.73MB
240. 40. ChatGPT for Data Science/12. Hypothesis testing with ChatGPT.mp414.37MB
241. 40. ChatGPT for Data Science/03. How ChatGPT can boost your productivity.mp45.38MB
242. 40. ChatGPT for Data Science/02. How to install ChatGPT.mp45.22MB
243. 41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/02. The Naive Bayes Algorithm.mp442.06MB
244. 41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/10. Machine Learning with Naïve Bayes (First Attempt).mp428.11MB
245. 41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/12. Testing the Model on New Data.mp420.83MB
246. 41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/11. Machine Learning with Naïve Bayes – converting the problem to a binary one.mp418.89MB
247. 41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/07. Optimizing User Reviews Data Preprocessing & EDA.mp418.67MB
248. 41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/08. Reg Ex for Analyzing Text Review Data.mp416.18MB
249. 41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/03. Tokenization and Vectorization.mp415.81MB
250. 41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/06. Loading the Dataset and Preprocessing.mp414.81MB
251. 41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/05. Overcome Imbalanced Data in Machine Learning.mp414.59MB
252. 41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/09. Understanding Differences between Multinomial and Bernouilli Naive Bayes.mp413.86MB
253. 41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/01. Intro to the Case Study.mp410.42MB
254. 41. Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis/04. Imbalanced Data Sets.mp46.55MB
255. 42. Part 6 Mathematics/11. Why is Linear Algebra Useful.mp488.47MB
256. 42. Part 6 Mathematics/10. Dot Product of Matrices.mp434.31MB
257. 42. Part 6 Mathematics/06. Addition and Subtraction of Matrices.mp422.1MB
258. 42. Part 6 Mathematics/04. Arrays in Python - A Convenient Way To Represent Matrices.mp418.98MB
259. 42. Part 6 Mathematics/05. What is a Tensor.mp415.54MB
260. 42. Part 6 Mathematics/08. Transpose of a Matrix.mp414.21MB
261. 42. Part 6 Mathematics/03. Linear Algebra and Geometry.mp413.73MB
262. 42. Part 6 Mathematics/09. Dot Product.mp412.84MB
263. 42. Part 6 Mathematics/01. What is a Matrix.mp411.94MB
264. 42. Part 6 Mathematics/02. Scalars and Vectors.mp48.54MB
265. 42. Part 6 Mathematics/07. Errors when Adding Matrices.mp45.77MB
266. 43. Part 7 Deep Learning/01. What to Expect from this Part.mp411.72MB
267. 44. Deep Learning - Introduction to Neural Networks/11. Optimization Algorithm 1-Parameter Gradient Descent.mp423.57MB
268. 44. Deep Learning - Introduction to Neural Networks/12. Optimization Algorithm n-Parameter Gradient Descent.mp416.83MB
269. 44. Deep Learning - Introduction to Neural Networks/06. The Linear model with Multiple Inputs and Multiple Outputs.mp416.64MB
270. 44. Deep Learning - Introduction to Neural Networks/03. Types of Machine Learning.mp413.05MB
271. 44. Deep Learning - Introduction to Neural Networks/01. Introduction to Neural Networks.mp410.49MB
272. 44. Deep Learning - Introduction to Neural Networks/10. Common Objective Functions Cross-Entropy Loss.mp49.84MB
273. 44. Deep Learning - Introduction to Neural Networks/04. The Linear Model (Linear Algebraic Version).mp47.98MB
274. 44. Deep Learning - Introduction to Neural Networks/05. The Linear Model with Multiple Inputs.mp47.91MB
275. 44. Deep Learning - Introduction to Neural Networks/07. Graphical Representation of Simple Neural Networks.mp47.78MB
276. 44. Deep Learning - Introduction to Neural Networks/02. Training the Model.mp47.72MB
277. 44. Deep Learning - Introduction to Neural Networks/08. What is the Objective Function.mp46.18MB
278. 44. Deep Learning - Introduction to Neural Networks/09. Common Objective Functions L2-norm Loss.mp45.47MB
279. 45. Deep Learning - How to Build a Neural Network from Scratch with NumPy/04. Basic NN Example (Part 4).mp439.97MB
280. 45. Deep Learning - How to Build a Neural Network from Scratch with NumPy/03. Basic NN Example (Part 3).mp415.66MB
281. 45. Deep Learning - How to Build a Neural Network from Scratch with NumPy/02. Basic NN Example (Part 2).mp415.23MB
282. 45. Deep Learning - How to Build a Neural Network from Scratch with NumPy/01. Basic NN Example (Part 1).mp49.34MB
283. 46. Deep Learning - TensorFlow 2.0 Introduction/01. How to Install TensorFlow 2.0.mp427.34MB
284. 46. Deep Learning - TensorFlow 2.0 Introduction/06. Outlining the Model with TensorFlow 2.mp426.96MB
285. 46. Deep Learning - TensorFlow 2.0 Introduction/07. Interpreting the Result and Extracting the Weights and Bias.mp425.91MB
286. 46. Deep Learning - TensorFlow 2.0 Introduction/08. Customizing a TensorFlow 2 Model.mp416.76MB
287. 46. Deep Learning - TensorFlow 2.0 Introduction/03. TensorFlow 1 vs TensorFlow 2.mp415.29MB
288. 46. Deep Learning - TensorFlow 2.0 Introduction/02. TensorFlow Outline and Comparison with Other Libraries.mp415.29MB
289. 46. Deep Learning - TensorFlow 2.0 Introduction/05. Types of File Formats Supporting TensorFlow.mp48.86MB
290. 46. Deep Learning - TensorFlow 2.0 Introduction/04. A Note on TensorFlow 2 Syntax.mp44.64MB
291. 47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/03. Digging into a Deep Net.mp423.68MB
292. 47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/04. Non-Linearities and their Purpose.mp422.51MB
293. 47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/07. Backpropagation.mp420.34MB
294. 47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/02. What is a Deep Net.mp49.13MB
295. 47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/05. Activation Functions.mp48.85MB
296. 47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/06. Activation Functions Softmax Activation.mp48.74MB
297. 47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/08. Backpropagation Picture.mp48.06MB
298. 47. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/01. What is a Layer.mp45.17MB
299. 48. Deep Learning - Overfitting/02. Underfitting and Overfitting for Classification.mp414.01MB
300. 48. Deep Learning - Overfitting/01. What is Overfitting.mp410.81MB
301. 48. Deep Learning - Overfitting/06. Early Stopping or When to Stop Training.mp410.29MB
302. 48. Deep Learning - Overfitting/04. Training, Validation, and Test Datasets.mp49.4MB
303. 48. Deep Learning - Overfitting/03. What is Validation.mp48.38MB
304. 48. Deep Learning - Overfitting/05. N-Fold Cross Validation.mp46.24MB
305. 49. Deep Learning - Initialization/01. What is Initialization.mp48.9MB
306. 49. Deep Learning - Initialization/02. Types of Simple Initializations.mp45.73MB
307. 49. Deep Learning - Initialization/03. State-of-the-Art Method - (Xavier) Glorot Initialization.mp45.46MB
308. 50. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/04. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.mp417.52MB
309. 50. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/06. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).mp48.53MB
310. 50. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/01. Stochastic Gradient Descent.mp47.83MB
311. 50. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/07. Adam (Adaptive Moment Estimation).mp47.14MB
312. 50. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/03. Momentum.mp45.18MB
313. 50. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/02. Problems with Gradient Descent.mp43.65MB
314. 50. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/05. Learning Rate Schedules Visualized.mp43.17MB
315. 51. Deep Learning - Preprocessing/03. Standardization.mp412.07MB
316. 51. Deep Learning - Preprocessing/01. Preprocessing Introduction.mp49.23MB
317. 51. Deep Learning - Preprocessing/05. Binary and One-Hot Encoding.mp48.55MB
318. 51. Deep Learning - Preprocessing/04. Preprocessing Categorical Data.mp45.44MB
319. 51. Deep Learning - Preprocessing/02. Types of Basic Preprocessing.mp43.25MB
320. 52. Deep Learning - Classifying on the MNIST Dataset/06. MNIST Preprocess the Data - Shuffle and Batch.mp432.69MB
321. 52. Deep Learning - Classifying on the MNIST Dataset/10. MNIST Learning.mp430.99MB
322. 52. Deep Learning - Classifying on the MNIST Dataset/04. MNIST Preprocess the Data - Create a Validation Set and Scale It.mp422.9MB
323. 52. Deep Learning - Classifying on the MNIST Dataset/12. MNIST Testing the Model.mp422.61MB
324. 52. Deep Learning - Classifying on the MNIST Dataset/08. MNIST Outline the Model.mp422.07MB
325. 52. Deep Learning - Classifying on the MNIST Dataset/03. MNIST Importing the Relevant Packages and Loading the Data.mp412.23MB
326. 52. Deep Learning - Classifying on the MNIST Dataset/09. MNIST Select the Loss and the Optimizer.mp410.65MB
327. 52. Deep Learning - Classifying on the MNIST Dataset/02. MNIST How to Tackle the MNIST.mp47.94MB
328. 52. Deep Learning - Classifying on the MNIST Dataset/01. MNIST The Dataset.mp44.53MB
329. 53. Deep Learning - Business Case Example/04. Business Case Preprocessing the Data.mp473.85MB
330. 53. Deep Learning - Business Case Example/01. Business Case Exploring the Dataset and Identifying Predictors.mp451.29MB
331. 53. Deep Learning - Business Case Example/09. Business Case Setting an Early Stopping Mechanism.mp443.82MB
332. 53. Deep Learning - Business Case Example/08. Business Case Learning and Interpreting the Result.mp429.4MB
333. 53. Deep Learning - Business Case Example/03. Business Case Balancing the Dataset.mp422.32MB
334. 53. Deep Learning - Business Case Example/06. Business Case Load the Preprocessed Data.mp413.81MB
335. 53. Deep Learning - Business Case Example/11. Business Case Testing the Model.mp48.19MB
336. 53. Deep Learning - Business Case Example/02. Business Case Outlining the Solution.mp43.04MB
337. 54. Deep Learning - Conclusion/06. An Overview of non-NN Approaches.mp416.08MB
338. 54. Deep Learning - Conclusion/04. An overview of CNNs.mp413.39MB
339. 54. Deep Learning - Conclusion/01. Summary on What You've Learned.mp49.84MB
340. 54. Deep Learning - Conclusion/05. An Overview of RNNs.mp46.96MB
341. 54. Deep Learning - Conclusion/02. What's Further out there in terms of Machine Learning.mp44.79MB
342. 55. Appendix Deep Learning - TensorFlow 1 Introduction/07. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.mp417.69MB
343. 55. Appendix Deep Learning - TensorFlow 1 Introduction/09. Basic NN Example with TF Model Output.mp417.07MB
344. 55. Appendix Deep Learning - TensorFlow 1 Introduction/04. TensorFlow Intro.mp416.9MB
345. 55. Appendix Deep Learning - TensorFlow 1 Introduction/08. Basic NN Example with TF Loss Function and Gradient Descent.mp413.61MB
346. 55. Appendix Deep Learning - TensorFlow 1 Introduction/05. Actual Introduction to TensorFlow.mp49.05MB
347. 55. Appendix Deep Learning - TensorFlow 1 Introduction/06. Types of File Formats, supporting Tensors.mp48.9MB
348. 55. Appendix Deep Learning - TensorFlow 1 Introduction/02. How to Install TensorFlow 1.mp45MB
349. 56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/09. MNIST Results and Testing.mp438.11MB
350. 56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/04. MNIST Model Outline.mp434.7MB
351. 56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/08. MNIST Learning.mp431.83MB
352. 56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/06. Calculating the Accuracy of the Model.mp424.45MB
353. 56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/05. MNIST Loss and Optimization Algorithm.mp415.79MB
354. 56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/03. MNIST Relevant Packages.mp411.25MB
355. 56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/07. MNIST Batching and Early Stopping.mp49.48MB
356. 56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/02. MNIST How to Tackle the MNIST.mp48.01MB
357. 56. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/01. MNIST What is the MNIST Dataset.mp44.8MB
358. 57. Appendix Deep Learning - TensorFlow 1 Business Case/04. Business Case Preprocessing.mp474.41MB
359. 57. Appendix Deep Learning - TensorFlow 1 Business Case/01. Business Case Getting Acquainted with the Dataset.mp460.25MB
360. 57. Appendix Deep Learning - TensorFlow 1 Business Case/06. Creating a Data Provider.mp456.3MB
361. 57. Appendix Deep Learning - TensorFlow 1 Business Case/07. Business Case Model Outline.mp442.51MB
362. 57. Appendix Deep Learning - TensorFlow 1 Business Case/03. The Importance of Working with a Balanced Dataset.mp427.25MB
363. 57. Appendix Deep Learning - TensorFlow 1 Business Case/08. Business Case Optimization.mp426.94MB
364. 57. Appendix Deep Learning - TensorFlow 1 Business Case/11. Business Case A Comment on the Homework.mp420.58MB
365. 57. Appendix Deep Learning - TensorFlow 1 Business Case/09. Business Case Interpretation.mp418.63MB
366. 57. Appendix Deep Learning - TensorFlow 1 Business Case/10. Business Case Testing the Model.mp44.4MB
367. 57. Appendix Deep Learning - TensorFlow 1 Business Case/02. Business Case Outlining the Solution.mp44.16MB
368. 58. Software Integration/02. What are Data Connectivity, APIs, and Endpoints.mp460.22MB
369. 58. Software Integration/03. Taking a Closer Look at APIs.mp424.51MB
370. 58. Software Integration/01. What are Data, Servers, Clients, Requests, and Responses.mp419.51MB
371. 58. Software Integration/04. Communication between Software Products through Text Files.mp417.54MB
372. 58. Software Integration/05. Software Integration - Explained.mp416MB
373. 59. Case Study - What's Next in the Course/03. Introducing the Data Set.mp424.24MB
374. 59. Case Study - What's Next in the Course/01. Game Plan for this Python, SQL, and Tableau Business Exercise.mp419.67MB
375. 59. Case Study - What's Next in the Course/02. The Business Task.mp411.28MB
376. 60. Case Study - Preprocessing the 'Absenteeism_data'/11. Obtaining Dummies from a Single Feature.mp469.75MB
377. 60. Case Study - Preprocessing the 'Absenteeism_data'/03. Checking the Content of the Data Set.mp453.99MB
378. 60. Case Study - Preprocessing the 'Absenteeism_data'/16. Classifying the Various Reasons for Absence.mp451.32MB
379. 60. Case Study - Preprocessing the 'Absenteeism_data'/07. Dropping a Column from a DataFrame in Python.mp441.24MB
380. 60. Case Study - Preprocessing the 'Absenteeism_data'/26. Analyzing the Dates from the Initial Data Set.mp440.12MB
381. 60. Case Study - Preprocessing the 'Absenteeism_data'/27. Extracting the Month Value from the Date Column.mp433.9MB
382. 60. Case Study - Preprocessing the 'Absenteeism_data'/10. Analyzing the Reasons for Absence.mp427.63MB
383. 60. Case Study - Preprocessing the 'Absenteeism_data'/17. Using .concat() in Python.mp427.34MB
384. 60. Case Study - Preprocessing the 'Absenteeism_data'/31. Working on Education, Children, and Pets.mp427.03MB
385. 60. Case Study - Preprocessing the 'Absenteeism_data'/02. Importing the Absenteeism Data in Python.mp419.52MB
386. 60. Case Study - Preprocessing the 'Absenteeism_data'/28. Extracting the Day of the Week from the Date Column.mp419.14MB
387. 60. Case Study - Preprocessing the 'Absenteeism_data'/04. Introduction to Terms with Multiple Meanings.mp417.98MB
388. 60. Case Study - Preprocessing the 'Absenteeism_data'/23. Creating Checkpoints while Coding in Jupyter.mp417.33MB
389. 60. Case Study - Preprocessing the 'Absenteeism_data'/30. Analyzing Several Straightforward Columns for this Exercise.mp414.32MB
390. 60. Case Study - Preprocessing the 'Absenteeism_data'/32. Final Remarks of this Section.mp413.54MB
391. 60. Case Study - Preprocessing the 'Absenteeism_data'/20. Reordering Columns in a Pandas DataFrame in Python.mp410MB
392. 60. Case Study - Preprocessing the 'Absenteeism_data'/06. Using a Statistical Approach towards the Solution to the Exercise.mp49.9MB
393. 60. Case Study - Preprocessing the 'Absenteeism_data'/15. More on Dummy Variables A Statistical Perspective.mp45.82MB
394. 61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/08. Interpreting the Coefficients for Our Problem.mp441.14MB
395. 61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/05. Splitting the Data for Training and Testing.mp436.06MB
396. 61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/02. Creating the Targets for the Logistic Regression.mp432.44MB
397. 61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/11. Backward Elimination or How to Simplify Your Model.mp431.82MB
398. 61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/12. Testing the Model We Created.mp431.6MB
399. 61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/16. Preparing the Deployment of the Model through a Module.mp428.56MB
400. 61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/07. Creating a Summary Table with the Coefficients and Intercept.mp426.95MB
401. 61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/13. Saving the Model and Preparing it for Deployment.mp425.52MB
402. 61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/09. Standardizing only the Numerical Variables (Creating a Custom Scaler).mp416.86MB
403. 61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/06. Fitting the Model and Assessing its Accuracy.mp415.22MB
404. 61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/10. Interpreting the Coefficients of the Logistic Regression.mp415.21MB
405. 61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/04. Standardizing the Data.mp415.15MB
406. 61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/01. Exploring the Problem with a Machine Learning Mindset.mp412.96MB
407. 61. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/03. Selecting the Inputs for the Logistic Regression.mp48.67MB
408. 62. Case Study - Loading the 'absenteeism_module'/03. Deploying the 'absenteeism_module' - Part II.mp445.14MB
409. 62. Case Study - Loading the 'absenteeism_module'/02. Deploying the 'absenteeism_module' - Part I.mp419.67MB
410. 63. Case Study - Analyzing the Predicted Outputs in Tableau/04. Analyzing Reasons vs Probability in Tableau.mp440.25MB
411. 63. Case Study - Analyzing the Predicted Outputs in Tableau/02. Analyzing Age vs Probability in Tableau.mp438.68MB
412. 63. Case Study - Analyzing the Predicted Outputs in Tableau/06. Analyzing Transportation Expense vs Probability in Tableau.mp416.48MB
413. 64. Appendix - Additional Python Tools/05. List Comprehensions.mp443.21MB
414. 64. Appendix - Additional Python Tools/04. Triple Nested For Loops.mp433MB
415. 64. Appendix - Additional Python Tools/01. Using the .format() Method.mp425.69MB
416. 64. Appendix - Additional Python Tools/06. Anonymous (Lambda) Functions.mp422.78MB
417. 64. Appendix - Additional Python Tools/02. Iterating Over Range Objects.mp412.61MB
418. 64. Appendix - Additional Python Tools/03. Introduction to Nested For Loops.mp412.17MB
419. 65. Appendix - pandas Fundamentals/11. Data Selection in pandas DataFrames.mp437.28MB
420. 65. Appendix - pandas Fundamentals/12. pandas DataFrames - Indexing with .iloc[].mp432.21MB
421. 65. Appendix - pandas Fundamentals/10. pandas DataFrames - Common Attributes.mp425.65MB
422. 65. Appendix - pandas Fundamentals/01. Introduction to pandas Series.mp424.96MB
423. 65. Appendix - pandas Fundamentals/06. Using .unique() and .nunique().mp424.31MB
424. 65. Appendix - pandas Fundamentals/05. Parameters and Arguments in pandas.mp421.13MB
425. 65. Appendix - pandas Fundamentals/13. pandas DataFrames - Indexing with .loc[].mp420.69MB
426. 65. Appendix - pandas Fundamentals/09. Introduction to pandas DataFrames - Part II.mp417.83MB
427. 65. Appendix - pandas Fundamentals/07. Using .sort_values().mp415.23MB
428. 65. Appendix - pandas Fundamentals/03. Working with Methods in Python - Part I.mp413.21MB
429. 65. Appendix - pandas Fundamentals/08. Introduction to pandas DataFrames - Part I.mp412.46MB
430. 65. Appendix - pandas Fundamentals/04. Working with Methods in Python - Part II.mp49.01MB