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CBT Nuggets - Programming for Data Science

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视频 2023-11-5 09:48 2024-11-15 15:56 167 12.25 GB 155
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CBT Nuggets - Programming for Data Science

文件列表
  1. 1. Explore Data Science Domains and Roles/1. Explore Data Science Domains and Roles .mp424.12MB
  2. 1. Explore Data Science Domains and Roles/2. What is Data Science .mp495.39MB
  3. 1. Explore Data Science Domains and Roles/3. Data Science Tools .mp4102.34MB
  4. 1. Explore Data Science Domains and Roles/4. Data Science Development Environments .mp483.58MB
  5. 1. Explore Data Science Domains and Roles/5. What is Anaconda .mp448.42MB
  6. 1. Explore Data Science Domains and Roles/6. Data Science Roles .mp443.44MB
  7. 1. Explore Data Science Domains and Roles/7. The Data Science Roadmap .mp461.05MB
  8. 10. Write Code using OOP Concepts for Data Science/1. Introduction .mp4125.08MB
  9. 10. Write Code using OOP Concepts for Data Science/2. Programming Styles .mp4123.81MB
  10. 10. Write Code using OOP Concepts for Data Science/3. Python Class Objects .mp4169.35MB
  11. 10. Write Code using OOP Concepts for Data Science/4. EDA Dimensions .mp466.48MB
  12. 10. Write Code using OOP Concepts for Data Science/5. EDA Summary Statistics .mp474.5MB
  13. 10. Write Code using OOP Concepts for Data Science/6. EDA Complete with Histograms .mp460.17MB
  14. 11. Wrangling Data with Pandas for Data Science/1. Introduction .mp475.41MB
  15. 11. Wrangling Data with Pandas for Data Science/2. What is Pandas Part 1 .mp479.08MB
  16. 11. Wrangling Data with Pandas for Data Science/3. What is Pandas Part 2 .mp471.63MB
  17. 11. Wrangling Data with Pandas for Data Science/4. EDA (Exploratory Data Analysis) .mp485.57MB
  18. 11. Wrangling Data with Pandas for Data Science/5. Clean and Manipulate Data .mp496.36MB
  19. 11. Wrangling Data with Pandas for Data Science/6. Data Visualization with Pandas (it does that also!) .mp4103.29MB
  20. 12. Work with Arrays Using Numpy Data Science Library/1. Introduction -3.mp499.73MB
  21. 12. Work with Arrays Using Numpy Data Science Library/2. What is Numpy .mp452.86MB
  22. 12. Work with Arrays Using Numpy Data Science Library/3. Numpy Vs Pandas .mp494.97MB
  23. 12. Work with Arrays Using Numpy Data Science Library/4. Creating and Manipulating Arrays .mp463.17MB
  24. 12. Work with Arrays Using Numpy Data Science Library/5. Array Operations, Array Methods and Functions .mp467.58MB
  25. 13. Visualizing Data with Matplotlib for Data Science/1. Introduction .mp436.11MB
  26. 13. Visualizing Data with Matplotlib for Data Science/2. What is Matplotlib .mp4161.46MB
  27. 13. Visualizing Data with Matplotlib for Data Science/3. Fields in the dataset from Kaggle .mp4155.4MB
  28. 13. Visualizing Data with Matplotlib for Data Science/4. Customizing Plots .mp479.96MB
  29. 14. Visualize Data with Seaborn for Data Science/1. Introduction -3.mp469.41MB
  30. 14. Visualize Data with Seaborn for Data Science/2. Matplotlib vs Seaborn .mp4149.37MB
  31. 14. Visualize Data with Seaborn for Data Science/3. Plotting with Seaborn .mp492.89MB
  32. 14. Visualize Data with Seaborn for Data Science/4. Customizing Plots .mp482.13MB
  33. 14. Visualize Data with Seaborn for Data Science/5. Real-world Notebook .mp422.03MB
  34. 15. Explore Web Scraping Fundamentals for Data Science/1. Introduction .mp426.88MB
  35. 15. Explore Web Scraping Fundamentals for Data Science/2. How the Internet Works .mp439.23MB
  36. 15. Explore Web Scraping Fundamentals for Data Science/3. Visual Studio Code .mp496.97MB
  37. 15. Explore Web Scraping Fundamentals for Data Science/4. HTML .mp445.74MB
  38. 15. Explore Web Scraping Fundamentals for Data Science/5. CSS .mp453.26MB
  39. 15. Explore Web Scraping Fundamentals for Data Science/6. Web Scraping with BeautifulSoup .mp4148.94MB
  40. 16. Collect Web Data with Python and BeautifulSoup/1. Introduction .mp455.1MB
  41. 16. Collect Web Data with Python and BeautifulSoup/2. What is BeautifulSoup .mp434.03MB
  42. 16. Collect Web Data with Python and BeautifulSoup/3. The find() Method Part 1 .mp491.83MB
  43. 16. Collect Web Data with Python and BeautifulSoup/4. The find() Method Part 2 .mp4129.45MB
  44. 16. Collect Web Data with Python and BeautifulSoup/5. The find_all() Method Part 1 .mp4135.68MB
  45. 16. Collect Web Data with Python and BeautifulSoup/6. The find_all() Method Part 2 .mp482.26MB
  46. 17. Use GitHub Repositories for Data Science/1. Introduction -2.mp438.37MB
  47. 17. Use GitHub Repositories for Data Science/2. What is Git .mp461.15MB
  48. 17. Use GitHub Repositories for Data Science/3. What is GitHub .mp462.01MB
  49. 17. Use GitHub Repositories for Data Science/4. Create an Online Repo and Push Your Code to GitHub .mp484.79MB
  50. 17. Use GitHub Repositories for Data Science/5. Hosting Datasets for use in Jupyter Notebook .mp494.05MB
  51. 17. Use GitHub Repositories for Data Science/6. Challenge .mp428.92MB
  52. 18. Analyze Core Data Structures for Data Science/1. Introduction .mp4133MB
  53. 18. Analyze Core Data Structures for Data Science/2. What are Data Structures .mp485.17MB
  54. 18. Analyze Core Data Structures for Data Science/3. Python Basic Data Structure Limitations .mp4124.69MB
  55. 18. Analyze Core Data Structures for Data Science/4. Data Structures Deep Dive .mp4141.53MB
  56. 18. Analyze Core Data Structures for Data Science/5. Social Network Analysis Use Case .mp4118.33MB
  57. 19. Evaluate Complexity and Memory for Data Science/1. Introduction - Programming for Data Science CBT Nuggets-3.mp4144MB
  58. 19. Evaluate Complexity and Memory for Data Science/2. Complexity Analysis and Memory .mp493.48MB
  59. 19. Evaluate Complexity and Memory for Data Science/3. Algorithm Comparison .mp4121.9MB
  60. 19. Evaluate Complexity and Memory for Data Science/4. Pandas Data Types .mp4210.61MB
  61. 2. Access the Command Line for Data Science/1. Introduction .mp4170.83MB
  62. 2. Access the Command Line for Data Science/2. What is a command-line, terminal, and Shell .mp4137.41MB
  63. 2. Access the Command Line for Data Science/3. macOS Terminal, Git for Windows, and Linux Emulators .mp480.92MB
  64. 2. Access the Command Line for Data Science/4. Basic Linux Commands .mp4105.55MB
  65. 2. Access the Command Line for Data Science/5. Create Projects and Workflows .mp482.95MB
  66. 20. Apply Big O Notation Concepts for Data Science/1. Introduction .mp4131.78MB
  67. 20. Apply Big O Notation Concepts for Data Science/2. Big O Notation .mp457.39MB
  68. 20. Apply Big O Notation Concepts for Data Science/3. Big O Notation and Time Complexity Visualization .mp457.09MB
  69. 20. Apply Big O Notation Concepts for Data Science/4. Quadratic time .mp438.24MB
  70. 20. Apply Big O Notation Concepts for Data Science/5. Factorial time .mp4132.61MB
  71. 20. Apply Big O Notation Concepts for Data Science/6. Coffee Shop Complexity .mp4109.62MB
  72. 21. Explore R Fundamentals for Data Science/1. Introduction -3.mp4193.71MB
  73. 21. Explore R Fundamentals for Data Science/2. What is R and Why Should I Learn it in 2023 .mp4167.8MB
  74. 21. Explore R Fundamentals for Data Science/3. Getting Started with R and Google Colab .mp4176.75MB
  75. 21. Explore R Fundamentals for Data Science/4. R Data Types .mp4101MB
  76. 22. Implement and Compare R Data Structures/1. Introduction .mp4117.68MB
  77. 22. Implement and Compare R Data Structures/2. R and Python Data Structures Part 1 Vectors .mp457.24MB
  78. 22. Implement and Compare R Data Structures/3. R and Python Data Structures Part 2 Arrays and Lists .mp440.55MB
  79. 22. Implement and Compare R Data Structures/4. R and Python Data Structures Part 3 Data Frames .mp430.79MB
  80. 22. Implement and Compare R Data Structures/5. Operations and Calculations .mp459.29MB
  81. 22. Implement and Compare R Data Structures/6. Matrix Calculations .mp476.17MB
  82. 22. Implement and Compare R Data Structures/7. Data Exploration .mp4133.38MB
  83. 23. Perform EDA with R and Python for Data Science/1. Introduction .mp419.12MB
  84. 23. Perform EDA with R and Python for Data Science/2. Load and Prepare the Dataset (EDA light) .mp4104.48MB
  85. 23. Perform EDA with R and Python for Data Science/3. Perform Exploratory Data Analysis (EDA) Part II .mp4116.08MB
  86. 23. Perform EDA with R and Python for Data Science/4. Perform Exploratory Data Analysis (EDA) Part I .mp476.31MB
  87. 23. Perform EDA with R and Python for Data Science/5. Challenge .mp474.7MB
  88. 24. Explore AI Language Models and OpenAI's ChatGPT/1. Introduction.mp471.61MB
  89. 24. Explore AI Language Models and OpenAI's ChatGPT/2. What is AI.mp4131.06MB
  90. 24. Explore AI Language Models and OpenAI's ChatGPT/3. OpenAI GPT-3 Language Models.mp467.36MB
  91. 24. Explore AI Language Models and OpenAI's ChatGPT/4. What is ChatGPT and How Does it Work Under the Hood.mp435.38MB
  92. 24. Explore AI Language Models and OpenAI's ChatGPT/5. Prompts and Completions.mp4185.37MB
  93. 25. Query OpenAI's Language Model API with Google's Colab/1. Introduction.mp482.88MB
  94. 25. Query OpenAI's Language Model API with Google's Colab/2. Bare Bones Completion.mp4102.56MB
  95. 25. Query OpenAI's Language Model API with Google's Colab/3. API Authentication.mp445.25MB
  96. 25. Query OpenAI's Language Model API with Google's Colab/4. Creating a Completion.mp4114.59MB
  97. 25. Query OpenAI's Language Model API with Google's Colab/5. Time Complexity.mp471.82MB
  98. 25. Query OpenAI's Language Model API with Google's Colab/6. Bonus Use Case White Paper Summarization.mp478.8MB
  99. 26. Create an AI Powered Web App with OpenAI, Streamlit/1. Introduction .mp498.27MB
  100. 26. Create an AI Powered Web App with OpenAI, Streamlit/2. What is Streamlit .mp481.15MB
  101. 26. Create an AI Powered Web App with OpenAI, Streamlit/3. What is Streamlit Community Cloud .mp432.25MB
  102. 26. Create an AI Powered Web App with OpenAI, Streamlit/4. Designing an AI Web App .mp478.5MB
  103. 26. Create an AI Powered Web App with OpenAI, Streamlit/5. HungryBear Non-production Code .mp4110.57MB
  104. 26. Create an AI Powered Web App with OpenAI, Streamlit/6. HungryBear Production Code Part 1 .mp464.04MB
  105. 26. Create an AI Powered Web App with OpenAI, Streamlit/7. HungryBear Production Code Part 2 .mp4133.31MB
  106. 3. Set Up a Data Science Development Environment/1. Introduction .mp437.3MB
  107. 3. Set Up a Data Science Development Environment/2. Install Anaconda macOS .mp464.05MB
  108. 3. Set Up a Data Science Development Environment/3. Install Anaconda Windows .mp429.03MB
  109. 3. Set Up a Data Science Development Environment/4. Virtual Environments with Conda .mp450.55MB
  110. 3. Set Up a Data Science Development Environment/5. Install Jupyter Notebook .mp464.27MB
  111. 3. Set Up a Data Science Development Environment/6. Starting a Jupyter Notebook and Session .mp466.41MB
  112. 3. Set Up a Data Science Development Environment/7. Closing a Jupyter Notebook Session .mp413.58MB
  113. 3. Set Up a Data Science Development Environment/8. Explore Visual Code for Data Science .mp445.88MB
  114. 4. Explore Python Data Types for Data Science/1. Introduction -3.mp424.85MB
  115. 4. Explore Python Data Types for Data Science/2. Primitive & Non-Primitive Data Types, Part 1 Conda Environment and GitHub .mp434.96MB
  116. 4. Explore Python Data Types for Data Science/3. Primitive & Non-Primitive Data Types, Part 2 Data Types in Jupyter Notebook .mp460.92MB
  117. 4. Explore Python Data Types for Data Science/4. Numbers Integers and Floats .mp443.71MB
  118. 4. Explore Python Data Types for Data Science/5. Text Strings and Bools .mp436.7MB
  119. 4. Explore Python Data Types for Data Science/6. Collections Lists .mp443.31MB
  120. 4. Explore Python Data Types for Data Science/7. Collections Dictionaries .mp4132.09MB
  121. 4. Explore Python Data Types for Data Science/8. Collections Tuples, and Sets .mp461.57MB
  122. 5. Explore Strings and Sequences for Data Science/1. Introduction .mp444.6MB
  123. 5. Explore Strings and Sequences for Data Science/2. Working with Variables .mp442.73MB
  124. 5. Explore Strings and Sequences for Data Science/3. Leaving Comments .mp431.62MB
  125. 5. Explore Strings and Sequences for Data Science/4. Working with Strings .mp496.02MB
  126. 5. Explore Strings and Sequences for Data Science/5. String Formatting .mp433.67MB
  127. 5. Explore Strings and Sequences for Data Science/6. Indexing .mp452.69MB
  128. 5. Explore Strings and Sequences for Data Science/7. Slicing .mp458.96MB
  129. 6. Explore Math Operators and LaTex for Data Science/1. Introduction .mp432.71MB
  130. 6. Explore Math Operators and LaTex for Data Science/2. Python and Math .mp481.54MB
  131. 6. Explore Math Operators and LaTex for Data Science/3. Math Operators .mp469.11MB
  132. 6. Explore Math Operators and LaTex for Data Science/4. Boolean Values .mp430.75MB
  133. 6. Explore Math Operators and LaTex for Data Science/5. Built-in Python Functions .mp477.63MB
  134. 6. Explore Math Operators and LaTex for Data Science/6. Scientific Notation .mp446.05MB
  135. 6. Explore Math Operators and LaTex for Data Science/7. LaTex for Equations and Formulas .mp459.28MB
  136. 7. Write Reusable Python Functions for Data Science/1. Introduction .mp423.98MB
  137. 7. Write Reusable Python Functions for Data Science/2. Comparison and Logical Operators .mp488.8MB
  138. 7. Write Reusable Python Functions for Data Science/3. Writing Functions .mp474.39MB
  139. 7. Write Reusable Python Functions for Data Science/4. If statements and Functions .mp480.12MB
  140. 7. Write Reusable Python Functions for Data Science/5. Understanding Functions .mp471.3MB
  141. 7. Write Reusable Python Functions for Data Science/6. Pseudocode .mp466.19MB
  142. 7. Write Reusable Python Functions for Data Science/7. Asking for Input .mp467.15MB
  143. 8. Write Loops to Automate Tasks for Data Science/1. Introduction - Loops to Automate Tasks .mp421.11MB
  144. 8. Write Loops to Automate Tasks for Data Science/2. Functions Review .mp481.88MB
  145. 8. Write Loops to Automate Tasks for Data Science/3. if Statements Part 1 .mp4123.5MB
  146. 8. Write Loops to Automate Tasks for Data Science/4. if Statements Part 2 .mp488.66MB
  147. 8. Write Loops to Automate Tasks for Data Science/5. for Loops .mp458.45MB
  148. 8. Write Loops to Automate Tasks for Data Science/6. while Loops .mp456.19MB
  149. 8. Write Loops to Automate Tasks for Data Science/7. Challenge .mp4112.7MB
  150. 9. Use Python Built-In Methods for Data Science/1. Introduction Python Built-in Methods .mp446.95MB
  151. 9. Use Python Built-In Methods for Data Science/2. List Review .mp4107.09MB
  152. 9. Use Python Built-In Methods for Data Science/3. List Methods .mp471.9MB
  153. 9. Use Python Built-In Methods for Data Science/4. Dictionary Review .mp453.54MB
  154. 9. Use Python Built-In Methods for Data Science/5. Dictionary Methods .mp453.52MB
  155. 9. Use Python Built-In Methods for Data Science/6. Numpy and Pandas .mp495.42MB
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