CBTNuggets - Certified Entry-Level Data Analyst with Python (PCED) (1.2025)

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CBTNuggets - Certified Entry-Level Data Analyst with Python (PCED) (1.2025)

磁力链接/BT种子简介

种子哈希：d61bf64420fcce0822f9df308baea8119c49db64
文件大小： 7.5G
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收录时间：2025-08-26
最近下载：2025-08-31

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文件列表

  • 20. Introduction to Web Scraping with BeautifulSoup/4. Scraping a Website with BeautifulSoup.mp4 69.4 MB
  • 23. CSS Selectors For Web Scraping/4. CSS Attribute Selectors.mp4 66.4 MB
  • 15. Intermediate Dataset Grouping/1. Introduction to Pandas Categories.mp4 64.6 MB
  • 14. The Basics of Pandas Dataset Grouping/1. Setting Up a Dataset.mp4 60.0 MB
  • 13. Dataset Cleaning in Pandas/3. Handling Missing Values.mp4 59.1 MB
  • 19. Sorting Pandas Data Structures/3. DataFrame Sorting Settings.mp4 56.7 MB
  • 18. Applying Functions in Pandas/1. Applying Functions to Entire DataFrames.mp4 56.3 MB
  • 57. SQL Subqueries/2. Single-Row Subqueries.mp4 54.4 MB
  • 22. Finding Elements with BeautifulSoup/3. Finding Sibling Elements.mp4 51.7 MB
  • 21. BeautifulSoup Types In-Depth/2. The BeautifulSoup Tag Type.mp4 51.1 MB
  • 22. Finding Elements with BeautifulSoup/1. The Basics of Finding Elements.mp4 49.8 MB
  • 28. XPath Predicates Basics/3. Filtering With Non-Attribute Properties.mp4 49.1 MB
  • 33. Working with Pie Charts in Matplotlib/3. Drawing Different Types of Pie Charts.mp4 47.6 MB
  • 29. Advanced XPath Predicates/3. Using Axes in Predicates.mp4 47.3 MB
  • 54. Updating and Deleting Data in SQL/2. Advanced UPDATE Usage.mp4 47.3 MB
  • 23. CSS Selectors For Web Scraping/3. CSS Class and ID Selectors.mp4 47.0 MB
  • 12. Working with Datasets in Pandas/4. Renaming DataFrame Columns.mp4 47.0 MB
  • 12. Working with Datasets in Pandas/1. Loading and Viewing CSV Datasets.mp4 46.1 MB
  • 54. Updating and Deleting Data in SQL/1. Updating SQL Rows.mp4 45.8 MB
  • 27. Intermediate XPath Concepts/1. Selecting Text and Attributes from Elements.mp4 45.7 MB
  • 52. SQL Query Fundamentals/3. Other WHERE Clause Situations.mp4 45.4 MB
  • 13. Dataset Cleaning in Pandas/4. Retyping DataFrame Columns.mp4 44.8 MB
  • 24. CSS Combinators for Web-Scraping/3. The Descendant Combinator.mp4 43.8 MB
  • 19. Sorting Pandas Data Structures/4. Reordering DataFrame Columns.mp4 43.5 MB
  • 29. Advanced XPath Predicates/1. Path-Related XPath Predicates.mp4 43.2 MB
  • 52. SQL Query Fundamentals/1. The SELECT Statement.mp4 42.7 MB
  • 28. XPath Predicates Basics/2. Other Ways of Filtering By Attributes.mp4 42.6 MB
  • 56. Aggregates and Grouping in SQL/4. The HAVING and DISTINCT Keywords.mp4 42.6 MB
  • 54. Updating and Deleting Data in SQL/4. Deleting and Altering SQL Tables.mp4 42.0 MB
  • 7. Basic Data Analysis with NumPy Arrays/1. Using Toy Datasets.mp4 41.9 MB
  • 16. Filtering Data in Pandas/2. Filtering Pandas DataFrames.mp4 41.5 MB
  • 20. Introduction to Web Scraping with BeautifulSoup/2. Loading and Parsing HTML.mp4 41.2 MB
  • 36. Adding Interactivity with Matplotlib Widgets/2. Responding to Widget Updates.mp4 41.2 MB
  • 12. Working with Datasets in Pandas/2. Basic Data Exploration.mp4 40.9 MB
  • 17. Transforming Data in Pandas/6. Solution.mp4 40.3 MB
  • 24. CSS Combinators for Web-Scraping/4. The Next-Sibling and Subsequent-Sibling Combinators.mp4 40.0 MB
  • 25. CSS Pseudo-Classes for Web-Scraping/3. The not Pseudo-Class.mp4 39.8 MB
  • 13. Dataset Cleaning in Pandas/1. Finding Missing Values in DataFrames.mp4 39.7 MB
  • 16. Filtering Data in Pandas/1. Filtering Pandas Series.mp4 39.4 MB
  • 40. Working with Seaborn's Categorical Plots/2. Changing Datapoint Appearances.mp4 38.7 MB
  • 23. CSS Selectors For Web Scraping/2. CSS Tag Selectors.mp4 38.3 MB
  • 52. SQL Query Fundamentals/2. The WHERE Clause.mp4 38.3 MB
  • 25. CSS Pseudo-Classes for Web-Scraping/4. The has Pseudo-Class.mp4 38.0 MB
  • 14. The Basics of Pandas Dataset Grouping/5. Solution.mp4 37.6 MB
  • 39. Working with Seaborn's Relational Plots/3. Separating Data Into Multiple Plots.mp4 37.2 MB
  • 55. SQL Relationships and Joins/2. One-to-One Relationships.mp4 37.2 MB
  • 39. Working with Seaborn's Relational Plots/2. Changing Datapoint Appearances.mp4 37.0 MB
  • 44. Machine Learning Algorithms Neural Networks/3. Training a Neural Network.mp4 36.9 MB
  • 53. Managing Data in SQL Tables/3. Inserting Data Into SQL Tables.mp4 36.8 MB
  • 22. Finding Elements with BeautifulSoup/2. Finding Multiple Elements.mp4 36.7 MB
  • 15. Intermediate Dataset Grouping/2. Aggregation Functions.mp4 36.7 MB
  • 6. NumPy Array Broadcasting In-Depth/2. Broadcasting with Arrays of Different Sizes.mp4 36.4 MB
  • 6. NumPy Array Broadcasting In-Depth/3. Resizing and Reshaping with Broadcasting.mp4 36.4 MB
  • 14. The Basics of Pandas Dataset Grouping/3. Grouping By Multiple Columns.mp4 36.3 MB
  • 30. Basics of Data Visualization with Matplotlib/3. Customizing Plots.mp4 36.0 MB
  • 14. The Basics of Pandas Dataset Grouping/2. Grouping DataFrames By Column.mp4 35.9 MB
  • 50. Bootstrapping and Other Inferential Strategies/2. A Central Limit Theorem Demonstration.mp4 35.9 MB
  • 18. Applying Functions in Pandas/4. Applying Functions to Cells.mp4 35.8 MB
  • 31. Working with Scatterplots in Matplotlib/6. Solution.mp4 35.4 MB
  • 51. Introduction to SQL For Data Analysts/4. Making Queries with Magics.mp4 35.4 MB
  • 25. CSS Pseudo-Classes for Web-Scraping/1. The First- and Last-Child Pseudo-Classes.mp4 35.4 MB
  • 42. Machine Learning Algorithms Linear Models/3. Fitting a Line with Scikit Learn.mp4 35.2 MB
  • 37. More Matplotlib Widgets/1. The Button Widget.mp4 35.1 MB
  • 38. Introduction to Seaborn/2. Seaborn's Built-in Datasets.mp4 34.8 MB
  • 45. Machine Learning Algorithms Classification Trees/4. Splitting Recursively.mp4 34.7 MB
  • 53. Managing Data in SQL Tables/4. SQLite's Data Types and Constraints.mp4 34.7 MB
  • 19. Sorting Pandas Data Structures/5. Getting the Largest and Smallest Values.mp4 34.1 MB
  • 21. BeautifulSoup Types In-Depth/3. The NavigableString Type.mp4 33.8 MB
  • 26. Introduction to XPath for Web-Scraping/4. Selecting Elements by Attribute Value.mp4 33.7 MB
  • 49. Inferential Statistics Fundamentals/1. What is Inferential Statistics.mp4 33.6 MB
  • 48. Correlation In Statistics/3. Calculating Correlation in Datasets.mp4 33.4 MB
  • 56. Aggregates and Grouping in SQL/3. The GROUP BY Clause.mp4 33.2 MB
  • 41. Machine Learning Algorithms K-Nearest Neighbor/4. Using Training and Test Sets.mp4 33.0 MB
  • 10. NumPy's Data Types In-Depth/1. NumPy Integer Types.mp4 32.8 MB
  • 15. Intermediate Dataset Grouping/3. Grouping Non-Categorical Data.mp4 32.8 MB
  • 29. Advanced XPath Predicates/2. Case-Insensitive Comparisons.mp4 32.7 MB
  • 56. Aggregates and Grouping in SQL/2. Using Aggregate Functions in Queries.mp4 32.7 MB
  • 39. Working with Seaborn's Relational Plots/1. The .relplot Function.mp4 32.6 MB
  • 25. CSS Pseudo-Classes for Web-Scraping/2. The First- and Last-of-Type Pseudo-Classes.mp4 32.5 MB
  • 37. More Matplotlib Widgets/3. The RectangleSelector Widget.mp4 32.4 MB
  • 8. Basics of Pandas Series/2. Creating Pandas Series.mp4 32.2 MB
  • 27. Intermediate XPath Concepts/3. The Basics of XPath Axes.mp4 32.2 MB
  • 49. Inferential Statistics Fundamentals/3. Hypothesis Testing in Jupyter.mp4 32.1 MB
  • 47. Descriptive Statistics In-Depth/1. The Normal Distribution In-Depth.mp4 31.9 MB
  • 34. Statistical Visualization Basics with Matplotlib/2. Customizing Histograms.mp4 31.8 MB
  • 51. Introduction to SQL For Data Analysts/1. The SQL Landscape.mp4 31.7 MB
  • 36. Adding Interactivity with Matplotlib Widgets/5. Solution.mp4 31.7 MB
  • 58. SQL Transactions and Rollbacks/2. Performing SQL Transactions.mp4 31.7 MB
  • 11. Generating Random Numbers with NumPy and Pandas/2. Generating Random Integers.mp4 31.2 MB
  • 27. Intermediate XPath Concepts/2. Selecting Elements by Position.mp4 31.0 MB
  • 17. Transforming Data in Pandas/4. Transforming Groups.mp4 31.0 MB
  • 38. Introduction to Seaborn/4. A Basic Demonstration.mp4 30.8 MB
  • 22. Finding Elements with BeautifulSoup/6. Solution.mp4 30.8 MB
  • 28. XPath Predicates Basics/1. What are Predicates.mp4 30.8 MB
  • 16. Filtering Data in Pandas/3. Filtering Groups of Series.mp4 30.7 MB
  • 3. Working with Multidimensional NumPy Arrays/2. Accessing Multi-Dimensional Array Values.mp4 30.6 MB
  • 31. Working with Scatterplots in Matplotlib/1. Scatterplot Basics.mp4 30.6 MB
  • 43. Machine Learning Algorithms K-Means Clustering/3. Additional K-Means Details.mp4 30.5 MB
  • 12. Working with Datasets in Pandas/3. Viewing Unique Values.mp4 30.3 MB
  • 36. Adding Interactivity with Matplotlib Widgets/3. The Range Slider Widget.mp4 30.2 MB
  • 40. Working with Seaborn's Categorical Plots/4. Using Specific Categorical Plot Functions.mp4 30.1 MB
  • 13. Dataset Cleaning in Pandas/6. Solution.mp4 29.8 MB
  • 9. Basics of Pandas DataFrames/3. Adding and Removing Columns.mp4 29.7 MB
  • 20. Introduction to Web Scraping with BeautifulSoup/3. Basic Parsing with BeautifulSoup.mp4 29.5 MB
  • 58. SQL Transactions and Rollbacks/3. SQL Transactions with the sqlite3 Module.mp4 29.2 MB
  • 18. Applying Functions in Pandas/2. Applying Functions to Columns.mp4 29.2 MB
  • 9. Basics of Pandas DataFrames/1. Creating DataFrames.mp4 29.0 MB
  • 48. Correlation In Statistics/4. Correlation Heat Maps.mp4 29.0 MB
  • 3. Working with Multidimensional NumPy Arrays/1. Multi-Dimensional Arrays.mp4 28.9 MB
  • 57. SQL Subqueries/1. Basics of Subqueries.mp4 28.8 MB
  • 32. Working with Bar Charts in Matplotlib/2. Customizing Bar Chart Appearances.mp4 28.7 MB
  • 44. Machine Learning Algorithms Neural Networks/2. Constructing Neural Networks.mp4 28.7 MB
  • 19. Sorting Pandas Data Structures/2. Sorting DataFrames by Column.mp4 28.5 MB
  • 5. Introduction to Fancy Indexing in NumPy/1. Integer Array Indexing.mp4 28.4 MB
  • 23. CSS Selectors For Web Scraping/1. The Basics of Using CSS Selectors with BeautifulSoup.mp4 28.3 MB
  • 33. Working with Pie Charts in Matplotlib/2. Customizing Pie Chart Appearances.mp4 28.3 MB
  • 26. Introduction to XPath for Web-Scraping/6. Solution.mp4 28.2 MB
  • 52. SQL Query Fundamentals/6. Solution.mp4 28.1 MB
  • 38. Introduction to Seaborn/1. Seaborn Basics.mp4 27.9 MB
  • 24. CSS Combinators for Web-Scraping/1. Additional Attribute Value Selectors.mp4 27.8 MB
  • 7. Basic Data Analysis with NumPy Arrays/2. Manipulating Toy Datasets with NumPy.mp4 27.8 MB
  • 48. Correlation In Statistics/2. Common Correlation Misconceptions.mp4 27.7 MB
  • 5. Introduction to Fancy Indexing in NumPy/5. Solution.mp4 27.5 MB
  • 26. Introduction to XPath for Web-Scraping/3. Absolute and Relative XPaths.mp4 27.5 MB
  • 27. Intermediate XPath Concepts/4. More XPath Axes.mp4 27.5 MB
  • 45. Machine Learning Algorithms Classification Trees/3. Deciding on the Best Split.mp4 27.4 MB
  • 8. Basics of Pandas Series/4. Useful Series-Creation Functions.mp4 27.2 MB
  • 50. Bootstrapping and Other Inferential Strategies/4. A Bootstrapping Demonstration.mp4 27.1 MB
  • 28. XPath Predicates Basics/5. Solution.mp4 27.1 MB
  • 21. BeautifulSoup Types In-Depth/1. The 4 BeautifulSoup Types.mp4 27.0 MB
  • 56. Aggregates and Grouping in SQL/1. Aggregate Functions.mp4 27.0 MB
  • 1. Basics of Jupyter Notebooks/2. Getting Started with Jupyter Notebooks.mp4 27.0 MB
  • 36. Adding Interactivity with Matplotlib Widgets/1. The Slider Widget.mp4 26.9 MB
  • 58. SQL Transactions and Rollbacks/4. Rolling Back Transactions.mp4 26.8 MB
  • 19. Sorting Pandas Data Structures/1. Sorting Series.mp4 26.8 MB
  • 34. Statistical Visualization Basics with Matplotlib/1. Displaying Histograms.mp4 26.6 MB
  • 16. Filtering Data in Pandas/4. Filtering Groups of DataFrames.mp4 26.5 MB
  • 34. Statistical Visualization Basics with Matplotlib/3. Drawing Boxplots.mp4 26.4 MB
  • 30. Basics of Data Visualization with Matplotlib/5. Solution.mp4 26.3 MB
  • 46. Basics of Descriptive Statistics/2. Measures of Central Tendency.mp4 26.1 MB
  • 5. Introduction to Fancy Indexing in NumPy/3. Boolean Array Indexing.mp4 26.1 MB
  • 5. Introduction to Fancy Indexing in NumPy/2. Combining Integer Indexing with Slicing.mp4 26.1 MB
  • 4. NumPy Array Operations/1. Sorting NumPy Arrays.mp4 26.1 MB
  • 8. Basics of Pandas Series/3. Accessing and Manipulating Pandas Series.mp4 26.1 MB
  • 7. Basic Data Analysis with NumPy Arrays/3. Using NumPy's .mean Method.mp4 25.9 MB
  • 51. Introduction to SQL For Data Analysts/2. Connecting to SQL Databases in Jupyter.mp4 25.9 MB
  • 40. Working with Seaborn's Categorical Plots/6. Solution.mp4 25.9 MB
  • 26. Introduction to XPath for Web-Scraping/1. What is XPath.mp4 25.8 MB
  • 56. Aggregates and Grouping in SQL/6. Solution.mp4 25.7 MB
  • 55. SQL Relationships and Joins/7. Solution.mp4 25.7 MB
  • 49. Inferential Statistics Fundamentals/2. Basics of Hypothesis Testing.mp4 25.6 MB
  • 37. More Matplotlib Widgets/2. The RadioButtons Widget.mp4 25.6 MB
  • 6. NumPy Array Broadcasting In-Depth/1. Array Broadcasting Basics.mp4 25.4 MB
  • 18. Applying Functions in Pandas/3. Applying Functions to Rows.mp4 25.3 MB
  • 46. Basics of Descriptive Statistics/4. Different Types of Distributions.mp4 25.3 MB
  • 35. Matplotlib Figures In-Depth/3. Another Way to Display Multiple Plots.mp4 25.2 MB
  • 47. Descriptive Statistics In-Depth/3. Z-Scores and How to Use Them.mp4 25.1 MB
  • 35. Matplotlib Figures In-Depth/6. Solution.mp4 25.0 MB
  • 10. NumPy's Data Types In-Depth/3. Other Common Data Types.mp4 25.0 MB
  • 32. Working with Bar Charts in Matplotlib/1. Displaying Basic Bar Charts.mp4 24.7 MB
  • 9. Basics of Pandas DataFrames/2. Accessing Rows and Columns on DataFrames.mp4 24.6 MB
  • 17. Transforming Data in Pandas/1. Transforming Series.mp4 24.6 MB
  • 40. Working with Seaborn's Categorical Plots/1. The .catplot Function.mp4 24.3 MB
  • 53. Managing Data in SQL Tables/1. Basics of SQL Tables.mp4 24.3 MB
  • 1. Basics of Jupyter Notebooks/3. The Basic Jupyter Interface.mp4 24.1 MB
  • 17. Transforming Data in Pandas/3. Transforming DataFrames.mp4 24.1 MB
  • 44. Machine Learning Algorithms Neural Networks/5. Solution.mp4 23.9 MB
  • 45. Machine Learning Algorithms Classification Trees/1. The Basics of Classification Trees.mp4 23.9 MB
  • 3. Working with Multidimensional NumPy Arrays/3. Slicing Multi-Dimensional Arrays.mp4 23.8 MB
  • 4. NumPy Array Operations/3. Reshaping and Resizing Arrays.mp4 23.7 MB
  • 1. Basics of Jupyter Notebooks/4. Essential Jupyter Hotkeys.mp4 23.6 MB
  • 48. Correlation In Statistics/1. What is Correlation.mp4 23.6 MB
  • 2. Introduction to NumPy/3. Accessing and Slicing NumPy Arrays.mp4 23.4 MB
  • 38. Introduction to Seaborn/3. The Different Plot Types.mp4 23.3 MB
  • 54. Updating and Deleting Data in SQL/3. Deleting SQL Rows.mp4 23.1 MB
  • 30. Basics of Data Visualization with Matplotlib/1. Displaying a Line Plot.mp4 23.1 MB
  • 43. Machine Learning Algorithms K-Means Clustering/2. Running the K-Means Algorithm.mp4 23.0 MB
  • 45. Machine Learning Algorithms Classification Trees/2. Finding Possible Splits.mp4 23.0 MB
  • 22. Finding Elements with BeautifulSoup/4. Finding Parent Elements.mp4 22.9 MB
  • 32. Working with Bar Charts in Matplotlib/4. Drawing Different Bar Chart Types.mp4 22.8 MB
  • 35. Matplotlib Figures In-Depth/2. Displaying Multiple Plots.mp4 22.7 MB
  • 50. Bootstrapping and Other Inferential Strategies/6. Solution.mp4 22.7 MB
  • 10. NumPy's Data Types In-Depth/2. NumPy Float Types.mp4 22.7 MB
  • 7. Basic Data Analysis with NumPy Arrays/4. Using NumPy's Descriptive Statistics Functions.mp4 22.6 MB
  • 2. Introduction to NumPy/1. Basic NumPy Concepts.mp4 22.6 MB
  • 20. Introduction to Web Scraping with BeautifulSoup/6. Solution.mp4 22.6 MB
  • 57. SQL Subqueries/3. Multi-Row Subqueries.mp4 22.6 MB
  • 1. Basics of Jupyter Notebooks/5. Hotkey Challenge.mp4 22.5 MB
  • 46. Basics of Descriptive Statistics/3. Measures of Variability.mp4 22.4 MB
  • 58. SQL Transactions and Rollbacks/1. Basics of Transactions.mp4 22.1 MB
  • 55. SQL Relationships and Joins/1. What Are Table Relationships.mp4 22.0 MB
  • 11. Generating Random Numbers with NumPy and Pandas/4. Generating Random Numbers from Distributions.mp4 21.8 MB
  • 31. Working with Scatterplots in Matplotlib/4. Plotting Multiple Datasets.mp4 21.8 MB
  • 2. Introduction to NumPy/2. Working with NumPy Arrays.mp4 21.7 MB
  • 31. Working with Scatterplots in Matplotlib/3. Color-Mapping Scatterplots.mp4 21.3 MB
  • 30. Basics of Data Visualization with Matplotlib/2. Plotting Multiple Lines.mp4 21.3 MB
  • 35. Matplotlib Figures In-Depth/4. Controlling Axes Layouts.mp4 21.2 MB
  • 35. Matplotlib Figures In-Depth/1. The Different Parts of a Figure.mp4 20.8 MB
  • 11. Generating Random Numbers with NumPy and Pandas/3. Making Random Selections from Arrays.mp4 20.8 MB
  • 42. Machine Learning Algorithms Linear Models/2. Creating Test Data with Scikit Learn.mp4 20.7 MB
  • 50. Bootstrapping and Other Inferential Strategies/1. The Central Limit Theorem.mp4 20.6 MB
  • 55. SQL Relationships and Joins/4. One-to-Many Relationships.mp4 20.6 MB
  • 32. Working with Bar Charts in Matplotlib/3. Adding Annotations to Bars.mp4 20.6 MB
  • 11. Generating Random Numbers with NumPy and Pandas/1. Generating Random Floats.mp4 20.5 MB
  • 13. Dataset Cleaning in Pandas/2. Viewing Rows with Missing Values.mp4 20.4 MB
  • 55. SQL Relationships and Joins/3. Performing Joins in SQL.mp4 20.4 MB
  • 43. Machine Learning Algorithms K-Means Clustering/4. Choosing the Optimal Number of Clusters.mp4 20.4 MB
  • 23. CSS Selectors For Web Scraping/6. Solution.mp4 20.3 MB
  • 52. SQL Query Fundamentals/4. The ORDER BY Clause.mp4 20.3 MB
  • 17. Transforming Data in Pandas/2. Transforming Series Using Lists and Dictionaries.mp4 20.3 MB
  • 31. Working with Scatterplots in Matplotlib/2. Customizing Scatterplot Appearance.mp4 20.2 MB
  • 26. Introduction to XPath for Web-Scraping/2. Using XPath in Jupyter.mp4 20.2 MB
  • 41. Machine Learning Algorithms K-Nearest Neighbor/3. Predicting a Single Data Point.mp4 20.2 MB
  • 42. Machine Learning Algorithms Linear Models/4. Making Predictions.mp4 20.2 MB
  • 3. Working with Multidimensional NumPy Arrays/4. Challenge & Solution 4D Arrays.mp4 20.2 MB
  • 57. SQL Subqueries/4. Correlated Subqueries.mp4 20.1 MB
  • 42. Machine Learning Algorithms Linear Models/1. Intro.mp4 20.1 MB
  • 37. More Matplotlib Widgets/5. Solution.mp4 20.0 MB
  • 42. Machine Learning Algorithms Linear Models/6. Solution.mp4 19.9 MB
  • 7. Basic Data Analysis with NumPy Arrays/6. Solution.mp4 19.9 MB
  • 46. Basics of Descriptive Statistics/1. What are Descriptive Statistics.mp4 19.8 MB
  • 9. Basics of Pandas DataFrames/5. Solution.mp4 19.7 MB
  • 34. Statistical Visualization Basics with Matplotlib/5. Solution.mp4 19.4 MB
  • 2. Introduction to NumPy/4. Other Ways to Create NumPy Arrays.mp4 19.3 MB
  • 15. Intermediate Dataset Grouping/5. Solution.mp4 19.3 MB
  • 6. NumPy Array Broadcasting In-Depth/5. Solution.mp4 19.2 MB
  • 11. Generating Random Numbers with NumPy and Pandas/5. Challenge.mp4 19.0 MB
  • 53. Managing Data in SQL Tables/2. Creating SQL Tables.mp4 18.9 MB
  • 41. Machine Learning Algorithms K-Nearest Neighbor/1. Basics of the K-Nearest Neighbor Algorithm.mp4 18.9 MB
  • 51. Introduction to SQL For Data Analysts/3. Making SQL Queries.mp4 18.9 MB
  • 44. Machine Learning Algorithms Neural Networks/1. The Basics of Neural Networks.mp4 18.7 MB
  • 4. NumPy Array Operations/2. Concatenating and Removing Array Elements.mp4 18.5 MB
  • 1. Basics of Jupyter Notebooks/1. What are Jupyter Notebooks.mp4 18.5 MB
  • 20. Introduction to Web Scraping with BeautifulSoup/1. The Basic Web Scraping Process.mp4 18.3 MB
  • 24. CSS Combinators for Web-Scraping/2. Selector Lists and Child Combinators.mp4 18.2 MB
  • 33. Working with Pie Charts in Matplotlib/1. Displaying Basic Pie Charts.mp4 17.7 MB
  • 47. Descriptive Statistics In-Depth/2. Useful Properties of the Normal Distribution.mp4 17.6 MB
  • 8. Basics of Pandas Series/1. Introduction to Pandas.mp4 17.3 MB
  • 51. Introduction to SQL For Data Analysts/6. Solution.mp4 17.2 MB
  • 41. Machine Learning Algorithms K-Nearest Neighbor/6. Solution.mp4 17.1 MB
  • 58. SQL Transactions and Rollbacks/6. Solution.mp4 17.1 MB
  • 33. Working with Pie Charts in Matplotlib/5. Solution.mp4 16.7 MB
  • 43. Machine Learning Algorithms K-Means Clustering/1. The Basics of K-Means Clustering.mp4 16.5 MB
  • 27. Intermediate XPath Concepts/5. Challenge.mp4 16.2 MB
  • 27. Intermediate XPath Concepts/6. Solution.mp4 16.0 MB
  • 29. Advanced XPath Predicates/5. Solution.mp4 15.9 MB
  • 11. Generating Random Numbers with NumPy and Pandas/6. Solution.mp4 15.9 MB
  • 47. Descriptive Statistics In-Depth/6. Solution.mp4 15.9 MB
  • 45. Machine Learning Algorithms Classification Trees/6. Solution.mp4 15.9 MB
  • 54. Updating and Deleting Data in SQL/6. Solution.mp4 15.8 MB
  • 3. Working with Multidimensional NumPy Arrays/5. Solution.mp4 15.7 MB
  • 50. Bootstrapping and Other Inferential Strategies/3. Bootstrapping Basics.mp4 15.5 MB
  • 36. Adding Interactivity with Matplotlib Widgets/4. Challenge.mp4 15.3 MB
  • 12. Working with Datasets in Pandas/5. Challenge Your Turn.mp4 15.2 MB
  • 57. SQL Subqueries/6. Solution.mp4 15.2 MB
  • 10. NumPy's Data Types In-Depth/4. Challenge.mp4 15.1 MB
  • 49. Inferential Statistics Fundamentals/5. Solution.mp4 15.1 MB
  • 5. Introduction to Fancy Indexing in NumPy/4. Challenge & Solution Combining Integer and Boolean Indexing.mp4 14.9 MB
  • 4. NumPy Array Operations/5. Challenge & Solution np.lexsort.mp4 14.5 MB
  • 4. NumPy Array Operations/4. NumPy Array Attributes.mp4 13.9 MB
  • 39. Working with Seaborn's Relational Plots/5. Solution.mp4 13.8 MB
  • 47. Descriptive Statistics In-Depth/4. Samples vs. Populations in Statistics.mp4 13.4 MB
  • 14. The Basics of Pandas Dataset Grouping/4. Challenge.mp4 13.4 MB
  • 55. SQL Relationships and Joins/5. Many-to-Many Relationships.mp4 13.2 MB
  • 26. Introduction to XPath for Web-Scraping/5. Challenge.mp4 12.4 MB
  • 46. Basics of Descriptive Statistics/6. Solution.mp4 12.0 MB
  • 41. Machine Learning Algorithms K-Nearest Neighbor/2. Creating Test Data with Scikit Learn.mp4 11.5 MB
  • 19. Sorting Pandas Data Structures/6. Challenge.mp4 11.3 MB
  • 53. Managing Data in SQL Tables/6. Solution.mp4 11.2 MB
  • 23. CSS Selectors For Web Scraping/5. Challenge.mp4 11.0 MB
  • 56. Aggregates and Grouping in SQL/5. Challenge.mp4 11.0 MB
  • 40. Working with Seaborn's Categorical Plots/3. The Different Types of Categorical Plots.mp4 10.7 MB
  • 2. Introduction to NumPy/6. Solution.mp4 10.7 MB
  • 22. Finding Elements with BeautifulSoup/5. Challenge.mp4 10.7 MB
  • 41. Machine Learning Algorithms K-Nearest Neighbor/5. Challenge.mp4 10.6 MB
  • 4. NumPy Array Operations/6. Solution.mp4 10.3 MB
  • 43. Machine Learning Algorithms K-Means Clustering/5. Challenge.mp4 10.1 MB
  • 52. SQL Query Fundamentals/5. Challenge.mp4 10.1 MB
  • 21. BeautifulSoup Types In-Depth/5. Solution.mp4 10.0 MB
  • 37. More Matplotlib Widgets/4. Challenge.mp4 9.3 MB
  • 28. XPath Predicates Basics/4. Challenge More Recipe Scraping.mp4 9.3 MB
  • 32. Working with Bar Charts in Matplotlib/6. Solution.mp4 9.3 MB
  • 9. Basics of Pandas DataFrames/4. Challenge.mp4 9.2 MB
  • 17. Transforming Data in Pandas/5. Challenge.mp4 9.2 MB
  • 18. Applying Functions in Pandas/6. Solution.mp4 9.0 MB
  • 21. BeautifulSoup Types In-Depth/4. Challenge.mp4 8.9 MB
  • 16. Filtering Data in Pandas/6. Solution.mp4 8.7 MB
  • 10. NumPy's Data Types In-Depth/5. Solution.mp4 8.3 MB
  • 47. Descriptive Statistics In-Depth/5. Challenge.mp4 8.3 MB
  • 29. Advanced XPath Predicates/4. Challenge.mp4 8.3 MB
  • 8. Basics of Pandas Series/6. Solution.mp4 8.1 MB
  • 48. Correlation In Statistics/5. Challenge Correlation of Real-World Data.mp4 8.1 MB
  • 54. Updating and Deleting Data in SQL/5. Challenge.mp4 8.0 MB
  • 45. Machine Learning Algorithms Classification Trees/5. Challenge.mp4 7.7 MB
  • 39. Working with Seaborn's Relational Plots/4. Challenge.mp4 7.4 MB
  • 6. NumPy Array Broadcasting In-Depth/4. Challenge.mp4 7.3 MB
  • 20. Introduction to Web Scraping with BeautifulSoup/5. Challenge.mp4 7.3 MB
  • 2. Introduction to NumPy/5. Challenge & Solution NumPy Array Practice.mp4 7.0 MB
  • 31. Working with Scatterplots in Matplotlib/5. Challenge.mp4 6.9 MB
  • 30. Basics of Data Visualization with Matplotlib/4. Challenge.mp4 6.7 MB
  • 7. Basic Data Analysis with NumPy Arrays/5. Challenge.mp4 6.4 MB
  • 34. Statistical Visualization Basics with Matplotlib/4. Challenge.mp4 6.3 MB
  • 44. Machine Learning Algorithms Neural Networks/4. Challenge.mp4 6.2 MB
  • 43. Machine Learning Algorithms K-Means Clustering/6. Solution.mp4 6.2 MB
  • 15. Intermediate Dataset Grouping/4. Challenge.mp4 6.2 MB
  • 19. Sorting Pandas Data Structures/7. Solution.mp4 6.1 MB
  • 16. Filtering Data in Pandas/5. Challenge.mp4 5.8 MB
  • 42. Machine Learning Algorithms Linear Models/5. Challenge.mp4 5.8 MB
  • 13. Dataset Cleaning in Pandas/5. Challenge.mp4 5.7 MB
  • 18. Applying Functions in Pandas/5. Challenge.mp4 5.5 MB
  • 35. Matplotlib Figures In-Depth/5. Challenge.mp4 5.4 MB
  • 51. Introduction to SQL For Data Analysts/5. Challenge.mp4 5.1 MB
  • 8. Basics of Pandas Series/5. Challenge.mp4 5.1 MB
  • 58. SQL Transactions and Rollbacks/5. Challenge.mp4 4.8 MB
  • 33. Working with Pie Charts in Matplotlib/4. Challenge.mp4 3.7 MB
  • 49. Inferential Statistics Fundamentals/4. Challenge.mp4 3.6 MB
  • 55. SQL Relationships and Joins/6. Challenge.mp4 3.5 MB
  • 57. SQL Subqueries/5. Challenge.mp4 3.5 MB
  • 40. Working with Seaborn's Categorical Plots/5. Challenge.mp4 3.5 MB
  • 32. Working with Bar Charts in Matplotlib/5. Challenge.mp4 3.5 MB
  • 38. Introduction to Seaborn/5. Challenge Your Turn!.mp4 2.8 MB
  • 53. Managing Data in SQL Tables/5. Challenge.mp4 2.3 MB
  • 50. Bootstrapping and Other Inferential Strategies/5. Challenge.mp4 2.2 MB
  • 46. Basics of Descriptive Statistics/5. Challenge.mp4 2.0 MB

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