DesireCourse Net Udemy - Practical statistics for data and business analysis

1. 1. Getting Started/2. Introduction and overview about data analysis.mp441.36MB
2. 1. Getting Started/3. Is programming for data science easy or hard .mp410.83MB
3. 1. Getting Started/4. What is the main concept of data analysis .mp411.67MB
4. 1. Getting Started/5. Machine learning should be after practical statistics.mp413.83MB
5. 1. Getting Started/6. General overview about what will you learn in data science courses.mp412.31MB
6. 1. Getting Started/7. Collection about important questions related to data science.mp429.66MB
7. 1. Getting Started/8. Be patient for interview questions.mp411.96MB
8. 1. Getting Started/9. People are panic from robot jobs.mp417.55MB
9. 10. Center of numerical data/10. I'm confused between mean , median and mode.mp415.8MB
10. 10. Center of numerical data/2. introduction about data center.mp419.44MB
11. 10. Center of numerical data/3. Characteristics of numerical data.mp420.05MB
12. 10. Center of numerical data/4. Categorical data characteristics considered to be limited.mp411.6MB
13. 10. Center of numerical data/5. Example about characteristics of categorical data.mp416MB
14. 10. Center of numerical data/6. What are measures of center .mp410.21MB
15. 10. Center of numerical data/7. Examples of mean.mp441.69MB
16. 10. Center of numerical data/8. Examples of median.mp432.65MB
17. 10. Center of numerical data/9. Examples of mode.mp444.73MB
18. 2. Careers and robot jobs/2. Important questions about Robot jobs and my career.mp424.48MB
19. 2. Careers and robot jobs/3. High demand for hiring data analysis engineer.mp419.6MB
20. 2. Careers and robot jobs/4. Example about robot jobs.mp421.27MB
21. 2. Careers and robot jobs/5. Robot jobs will create new jobs for you because it is a friend.mp422.8MB
22. 2. Careers and robot jobs/6. It is not easy to hire data science engineer.mp49.04MB
23. 2. Careers and robot jobs/7. Why do you think that learning programming is Barrier in data analysis .mp47.84MB
24. 3. Startup point of programming in data analysis/2. Collection of important questions related to programming.mp420.33MB
25. 3. Startup point of programming in data analysis/3. Should i learn programming like professional .mp410.45MB
26. 3. Startup point of programming in data analysis/4. Where is my start up point to learn programming .mp456.62MB
27. 3. Startup point of programming in data analysis/5. R language not for software developers.mp48.34MB
28. 3. Startup point of programming in data analysis/6. Programming in data analysis uses simple and easy language.mp47.5MB
29. 3. Startup point of programming in data analysis/7. Review about our questions related to programming.mp449.45MB
30. 3. Startup point of programming in data analysis/8. What is our example in programming .mp44.95MB
31. 4. Example about programming and big data/1. Important introduction about SQL example.mp437.55MB
32. 4. Example about programming and big data/10. What is velocity in big data .mp427.62MB
33. 4. Example about programming and big data/11. Big data is something made overloads.mp421.69MB
34. 4. Example about programming and big data/2. Run your first SQL command without any previous experience.mp418.13MB
35. 4. Example about programming and big data/3. Note the difference between SQL and English language.mp46.35MB
36. 4. Example about programming and big data/4. Python with sample activity.mp428.72MB
37. 4. Example about programming and big data/5. Review about Python and SQL in data analysis.mp47.86MB
38. 4. Example about programming and big data/6. What is big data .mp49.03MB
39. 4. Example about programming and big data/7. Professional answer about what is big data .mp425.67MB
40. 4. Example about programming and big data/8. What is OVERLOADS in a big data .mp412.33MB
41. 4. Example about programming and big data/9. Variety in big data.mp430.35MB
42. 5. What is after data analysis/2. Introduction with important questions .mp424.63MB
43. 5. What is after data analysis/3. Difference between data analytics and data science.mp429.88MB
44. 5. What is after data analysis/4. What is after data analysis .mp457.86MB
45. 5. What is after data analysis/5. What is professional people in data analysis care .mp426.25MB
46. 5. What is after data analysis/6. Your data is your treasure.mp416.87MB
47. 6. introduction before descriptive statistics/2. Our strategy to learn practical statistics.mp421.86MB
48. 6. introduction before descriptive statistics/3. Four main things in practical statistics.mp412.48MB
49. 7. Comparison between inferential ans descriptive statistics/2. Simplified viewpoint about descriptive and inferential statistics.mp488.57MB
50. 7. Comparison between inferential ans descriptive statistics/3. Data before and after descriptive statistics.mp448.84MB
51. 7. Comparison between inferential ans descriptive statistics/4. Conclusions between inferential and descriptive statistics.mp419.47MB
52. 7. Comparison between inferential ans descriptive statistics/5. Population and sample in inferential statistics.mp460.95MB
53. 7. Comparison between inferential ans descriptive statistics/6. Simplified viewpoint about inferential statistics data.mp424.59MB
54. 8. FAQ about descriptive statistics/2. What will we learn in descriptive statistics .mp437.78MB
55. 8. FAQ about descriptive statistics/3. Statistics between Lie and trustworthy.mp413.93MB
56. 8. FAQ about descriptive statistics/4. Waitress should be friendly or friendlier .mp427.72MB
57. 9. Data types/2. Introduction about data types.mp412.89MB
58. 9. Data types/3. the benefit of data types.mp418.81MB
59. 9. Data types/4. Categorical data types.mp425MB
60. 9. Data types/5. Data types ( continuous vs discrete ).mp434.02MB
61. 9. Data types/6. Difference between numerical and categorical data.mp439.72MB
62. 9. Data types/7. Quizzes and examples about data types.mp4179.38MB
63. 9. Data types/8. The summary about data types.mp425.39MB