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[] Udemy - Practical statistics for data and business analysis

  • 收录时间:2019-06-27 07:28:54
  • 文件大小:2GB
  • 下载次数:133
  • 最近下载:2021-01-16 21:57:05
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文件列表

  1. 9. Data types/7. Quizzes and examples about data types.mp4 179MB
  2. 7. Comparison between inferential ans descriptive statistics/2. Simplified viewpoint about descriptive and inferential statistics.mp4 89MB
  3. 7. Comparison between inferential ans descriptive statistics/5. Population and sample in inferential statistics.mp4 61MB
  4. 5. What is after data analysis/4. What is after data analysis .mp4 58MB
  5. 3. Startup point of programming in data analysis/4. Where is my start up point to learn programming .mp4 57MB
  6. 3. Startup point of programming in data analysis/7. Review about our questions related to programming.vtt 49MB
  7. 3. Startup point of programming in data analysis/7. Review about our questions related to programming.mp4 49MB
  8. 7. Comparison between inferential ans descriptive statistics/3. Data before and after descriptive statistics.mp4 49MB
  9. 10. Center of numerical data/9. Examples of mode.mp4 45MB
  10. 10. Center of numerical data/7. Examples of mean.mp4 42MB
  11. 1. Getting Started/2. Introduction and overview about data analysis.mp4 41MB
  12. 9. Data types/6. Difference between numerical and categorical data.mp4 40MB
  13. 8. FAQ about descriptive statistics/2. What will we learn in descriptive statistics .mp4 38MB
  14. 4. Example about programming and big data/1. Important introduction about SQL example.mp4 38MB
  15. 9. Data types/5. Data types ( continuous vs discrete ).mp4 34MB
  16. 10. Center of numerical data/8. Examples of median.mp4 33MB
  17. 4. Example about programming and big data/9. Variety in big data.mp4 30MB
  18. 5. What is after data analysis/3. Difference between data analytics and data science.mp4 30MB
  19. 1. Getting Started/7. Collection about important questions related to data science.mp4 30MB
  20. 4. Example about programming and big data/4. Python with sample activity.mp4 29MB
  21. 8. FAQ about descriptive statistics/4. Waitress should be friendly or friendlier .mp4 28MB
  22. 4. Example about programming and big data/10. What is velocity in big data .mp4 28MB
  23. 5. What is after data analysis/5. What is professional people in data analysis care .mp4 26MB
  24. 4. Example about programming and big data/7. Professional answer about what is big data .mp4 26MB
  25. 9. Data types/8. The summary about data types.mp4 25MB
  26. 9. Data types/4. Categorical data types.mp4 25MB
  27. 5. What is after data analysis/2. Introduction with important questions .mp4 25MB
  28. 7. Comparison between inferential ans descriptive statistics/6. Simplified viewpoint about inferential statistics data.mp4 25MB
  29. 2. Careers and robot jobs/2. Important questions about Robot jobs and my career.mp4 24MB
  30. 2. Careers and robot jobs/5. Robot jobs will create new jobs for you because it is a friend.mp4 23MB
  31. 6. introduction before descriptive statistics/2. Our strategy to learn practical statistics.mp4 22MB
  32. 4. Example about programming and big data/11. Big data is something made overloads.mp4 22MB
  33. 2. Careers and robot jobs/4. Example about robot jobs.mp4 21MB
  34. 3. Startup point of programming in data analysis/2. Collection of important questions related to programming.mp4 20MB
  35. 10. Center of numerical data/3. Characteristics of numerical data.mp4 20MB
  36. 2. Careers and robot jobs/3. High demand for hiring data analysis engineer.mp4 20MB
  37. 7. Comparison between inferential ans descriptive statistics/4. Conclusions between inferential and descriptive statistics.mp4 19MB
  38. 10. Center of numerical data/2. introduction about data center.mp4 19MB
  39. 9. Data types/3. the benefit of data types.mp4 19MB
  40. 4. Example about programming and big data/2. Run your first SQL command without any previous experience.mp4 18MB
  41. 1. Getting Started/9. People are panic from robot jobs.mp4 18MB
  42. 5. What is after data analysis/6. Your data is your treasure.mp4 17MB
  43. 10. Center of numerical data/5. Example about characteristics of categorical data.mp4 16MB
  44. 10. Center of numerical data/10. I'm confused between mean , median and mode.mp4 16MB
  45. 8. FAQ about descriptive statistics/3. Statistics between Lie and trustworthy.mp4 14MB
  46. 1. Getting Started/5. Machine learning should be after practical statistics.mp4 14MB
  47. 9. Data types/2. Introduction about data types.mp4 13MB
  48. 6. introduction before descriptive statistics/3. Four main things in practical statistics.mp4 12MB
  49. 4. Example about programming and big data/8. What is OVERLOADS in a big data .mp4 12MB
  50. 1. Getting Started/6. General overview about what will you learn in data science courses.mp4 12MB
  51. 1. Getting Started/8. Be patient for interview questions.mp4 12MB
  52. 1. Getting Started/4. What is the main concept of data analysis .mp4 12MB
  53. 10. Center of numerical data/4. Categorical data characteristics considered to be limited.mp4 12MB
  54. 1. Getting Started/3. Is programming for data science easy or hard .mp4 11MB
  55. 3. Startup point of programming in data analysis/3. Should i learn programming like professional .mp4 10MB
  56. 10. Center of numerical data/6. What are measures of center .mp4 10MB
  57. 2. Careers and robot jobs/6. It is not easy to hire data science engineer.mp4 9MB
  58. 4. Example about programming and big data/6. What is big data .mp4 9MB
  59. 3. Startup point of programming in data analysis/5. R language not for software developers.mp4 8MB
  60. 4. Example about programming and big data/5. Review about Python and SQL in data analysis.mp4 8MB
  61. 2. Careers and robot jobs/7. Why do you think that learning programming is Barrier in data analysis .mp4 8MB
  62. 3. Startup point of programming in data analysis/6. Programming in data analysis uses simple and easy language.mp4 7MB
  63. 4. Example about programming and big data/3. Note the difference between SQL and English language.mp4 6MB
  64. 3. Startup point of programming in data analysis/8. What is our example in programming .mp4 5MB
  65. 9. Data types/7. Quizzes and examples about data types.vtt 10KB
  66. 3. Startup point of programming in data analysis/4. Where is my start up point to learn programming .vtt 7KB
  67. 7. Comparison between inferential ans descriptive statistics/2. Simplified viewpoint about descriptive and inferential statistics.vtt 6KB
  68. 4. Example about programming and big data/4. Python with sample activity.vtt 5KB
  69. 4. Example about programming and big data/9. Variety in big data.vtt 4KB
  70. 4. Example about programming and big data/10. What is velocity in big data .vtt 3KB
  71. 5. What is after data analysis/4. What is after data analysis .vtt 3KB
  72. 7. Comparison between inferential ans descriptive statistics/5. Population and sample in inferential statistics.vtt 3KB
  73. 7. Comparison between inferential ans descriptive statistics/3. Data before and after descriptive statistics.vtt 3KB
  74. 4. Example about programming and big data/1. Important introduction about SQL example.vtt 3KB
  75. 10. Center of numerical data/9. Examples of mode.vtt 3KB
  76. 9. Data types/5. Data types ( continuous vs discrete ).vtt 3KB
  77. 10. Center of numerical data/8. Examples of median.vtt 3KB
  78. 5. What is after data analysis/3. Difference between data analytics and data science.vtt 3KB
  79. 9. Data types/6. Difference between numerical and categorical data.vtt 3KB
  80. 1. Getting Started/7. Collection about important questions related to data science.vtt 3KB
  81. 8. FAQ about descriptive statistics/2. What will we learn in descriptive statistics .vtt 2KB
  82. 10. Center of numerical data/1. Slides and material used in this content.html 2KB
  83. 3. Startup point of programming in data analysis/1. Slides and material used in this content.html 2KB
  84. 6. introduction before descriptive statistics/2. Our strategy to learn practical statistics.vtt 2KB
  85. 10. Center of numerical data/7. Examples of mean.vtt 2KB
  86. 10. Center of numerical data/3. Characteristics of numerical data.vtt 2KB
  87. 1. Getting Started/2. Introduction and overview about data analysis.vtt 2KB
  88. 4. Example about programming and big data/2. Run your first SQL command without any previous experience.vtt 2KB
  89. 9. Data types/4. Categorical data types.vtt 2KB
  90. 9. Data types/1. Slides and material used in this content.html 2KB
  91. 8. FAQ about descriptive statistics/4. Waitress should be friendly or friendlier .vtt 2KB
  92. 2. Careers and robot jobs/3. High demand for hiring data analysis engineer.vtt 2KB
  93. 7. Comparison between inferential ans descriptive statistics/6. Simplified viewpoint about inferential statistics data.vtt 2KB
  94. 5. What is after data analysis/5. What is professional people in data analysis care .vtt 2KB
  95. 4. Example about programming and big data/7. Professional answer about what is big data .vtt 2KB
  96. 3. Startup point of programming in data analysis/5. R language not for software developers.vtt 2KB
  97. 4. Example about programming and big data/11. Big data is something made overloads.vtt 2KB
  98. 2. Careers and robot jobs/5. Robot jobs will create new jobs for you because it is a friend.vtt 2KB
  99. 2. Careers and robot jobs/4. Example about robot jobs.vtt 2KB
  100. 9. Data types/8. The summary about data types.vtt 2KB
  101. 4. Example about programming and big data/6. What is big data .vtt 1KB
  102. 7. Comparison between inferential ans descriptive statistics/4. Conclusions between inferential and descriptive statistics.vtt 1KB
  103. 9. Data types/3. the benefit of data types.vtt 1KB
  104. 2. Careers and robot jobs/6. It is not easy to hire data science engineer.vtt 1KB
  105. 1. Getting Started/9. People are panic from robot jobs.vtt 1KB
  106. 2. Careers and robot jobs/1. Slides and material used in this content.html 1KB
  107. 1. Getting Started/5. Machine learning should be after practical statistics.vtt 1KB
  108. 5. What is after data analysis/6. Your data is your treasure.vtt 1KB
  109. 5. What is after data analysis/2. Introduction with important questions .vtt 1KB
  110. 1. Getting Started/8. Be patient for interview questions.vtt 1KB
  111. 2. Careers and robot jobs/2. Important questions about Robot jobs and my career.vtt 1KB
  112. 10. Center of numerical data/5. Example about characteristics of categorical data.vtt 1KB
  113. 6. introduction before descriptive statistics/3. Four main things in practical statistics.vtt 1KB
  114. 1. Getting Started/1. Slides and material used in this content.html 1KB
  115. 3. Startup point of programming in data analysis/3. Should i learn programming like professional .vtt 1KB
  116. 4. Example about programming and big data/3. Note the difference between SQL and English language.vtt 1KB
  117. 3. Startup point of programming in data analysis/2. Collection of important questions related to programming.vtt 1KB
  118. 8. FAQ about descriptive statistics/3. Statistics between Lie and trustworthy.vtt 1KB
  119. 1. Getting Started/4. What is the main concept of data analysis .vtt 1012B
  120. 1. Getting Started/3. Is programming for data science easy or hard .vtt 965B
  121. 10. Center of numerical data/4. Categorical data characteristics considered to be limited.vtt 935B
  122. 10. Center of numerical data/2. introduction about data center.vtt 933B
  123. 5. What is after data analysis/1. Slides and material used in this content.html 929B
  124. 4. Example about programming and big data/8. What is OVERLOADS in a big data .vtt 926B
  125. 1. Getting Started/6. General overview about what will you learn in data science courses.vtt 891B
  126. 8. FAQ about descriptive statistics/1. Slides and material used in this content.html 884B
  127. 7. Comparison between inferential ans descriptive statistics/1. Slides and material used in this content.html 873B
  128. 4. Example about programming and big data/5. Review about Python and SQL in data analysis.vtt 845B
  129. 10. Center of numerical data/6. What are measures of center .vtt 842B
  130. 2. Careers and robot jobs/7. Why do you think that learning programming is Barrier in data analysis .vtt 734B
  131. 6. introduction before descriptive statistics/1. Slides and material used in this content.html 708B
  132. 3. Startup point of programming in data analysis/6. Programming in data analysis uses simple and easy language.vtt 684B
  133. 9. Data types/2. Introduction about data types.vtt 665B
  134. 3. Startup point of programming in data analysis/8. What is our example in programming .vtt 510B
  135. [DesireCourse.Net].url 51B
  136. [CourseClub.Me].url 48B