R is not a simple programming language, and it does better on reading excel files than python

R is not a simple programming language, and it does better on reading excel files than python .

tried to read excel files to python and R.

in python, pandas can read in excel files

import pandas as pd

df=pd.read_excel("files")

And most of times, there are some errors. And the files could not be read in. 

But the same excel files, R works better on reading in the excel files.

library("readxl")

my_data<-read_excel("files")

Most of times, there is no error. And the files can be read in. 

R can do most of the programmings as any programming languages. 

make flowcharts with draw.io

 make flowcharts with draw.io

draw.io an app to create  flowcharts . 

https://app.diagrams.net/

chrome book, an interesting laptop

 chromebook 

an interesting laptop

it is from google

and it has an operating system specially designed

the operating system can be installed on any laptop

https://www.youtube.com/watch?v=4gZYV0RWJQ8

brain training apps

 brain training and testing apps

there is a brain training and testing machine 

lumosity 

https://www.youtube.com/watch?v=_MG6J2z2dVE

https://www.youtube.com/watch?v=v34gQO_akvw&t=2s

there are some brain training apps

impulse

elevate

mind games

an interesting tool to draw flowchart

 lucid chart is an interesting tool to draw flowchart

https://www.youtube.com/watch?v=St95m84Rs5g

https://www.lucidchart.com/pages/

top 3 linux systems

 top 3 linux systems 

1.  MX linux

2.  manjaro

3.  Linux mint

https://distrowatch.com/dwres.php?resource=popularity

different methods to build online forums

 different methods to build online forums

1. Nodebb

2. Vanilla

3. bbPress , it is from wordpress

4. phpBB 

5. simple Machines

6. Fluxbb

7. myBB

8. Flarum

9.Lemmy

10. forem  

Dev.to

11. TalkYard

12. discourse

turn damaged laptop to a nice desktop

 I saw some posts on youtube about turning damaged laptop to a nice desktop. It is an interesting way to re-use your damaged , or unused laptops.

1. this one is a cheap way

https://www.youtube.com/watch?v=WJ-i0bgRWwA

2. this one is an interesting way

https://www.youtube.com/watch?v=AOh4h0u3msU

3.  and this one

https://www.youtube.com/watch?v=lXcfKTNObOo

4. this one

https://www.youtube.com/watch?v=8jeLCQ62vFk

pixel 3 vs iphone

 pixel 3 vs iphone 

1. some apps available on pixel , but not on ios

2. pixel 3 has better connection on wifi than iphone. 

If you frequently use your usb on your computer, your computer will more easily freeze up

 If you frequently use your usb on your computer, your computer will more easily freeze up. 

logistic regression, a guide

 

https://medium.com/analytics-vidhya/a-comprehensive-guide-to-logistic-regression-e0cf04fe738c

decision boundary=cut off =threshold 

time series analysis, lags and autocorrelations

 

https://www.business-science.io/timeseries-analysis/2017/08/30/tidy-timeseries-analysis-pt-4.html

guide to time series analysis

 

https://towardsdatascience.com/the-complete-guide-to-time-series-analysis-and-forecasting-70d476bfe775

bidding on click through, cost per impressions, conversion

 facebook , bidding on click through, cost per impressions, conversion? 

how to choose from the prices? bidding 

choose the cut off for binary classification

 

http://ethen8181.github.io/machine-learning/unbalanced/unbalanced.html#choosing-the-suitable-cutoff-value

why most of the time , the cut off or threshold is 0.5?

https://www.graphpad.com/guides/prism/latest/curve-fitting/reg_logistic_roc_curves.htm

in the built-in function, the cut off is 0.5

when reporting the confusion matrix, can change the cut off to an arbituray number, and the confusion matrix will be changed accordingly, when changing the cut off, the false positive rate and false negative rate will change accordingly. 

dat <- iris dat$positive <- as.factor(ifelse(dat$Species == "setosa", "s", "ns")) library(caret) mod <- train(positive~Sepal.Length, data=dat, method="glm")

confusionMatrix(table(predict(mod, type="prob")[,"s"] >= 0.25,
                      dat$positive == "s"))
# Confusion Matrix and Statistics
# 
#        
#         FALSE TRUE
#   FALSE    88    3
#   TRUE     12   47
#                                           
#                Accuracy : 0.9             
#                  95% CI : (0.8404, 0.9429)
#     No Information Rate : 0.6667          
#     P-Value [Acc > NIR] : 2.439e-11       
#                                           
#                   Kappa : 0.7847          
#  Mcnemar's Test P-Value : 0.03887         
#                                           
#             Sensitivity : 0.8800          
#             Specificity : 0.9400          
#          Pos Pred Value : 0.9670          
#          Neg Pred Value : 0.7966          
#              Prevalence : 0.6667          
#          Detection Rate : 0.5867          
#    Detection Prevalence : 0.6067          
#       Balanced Accuracy : 0.9100        

an interesting website for data analysis

 

https://www.digitaling.com/

it is in chinese, 

internet industry technical terms

advertising related  

1. ad network

2. ad exchange

3. RBT real time bidding

4. DSP demand side platform

5. DMP data-management platform

6. programmatic buying

7. Private market place

8. Programmatic Direct buy

9. Premium Inventory

10. Remnant Inventory

11. CPM cost per mille

12. CPC cost per click, Cost Per Thousand;Cost Per Impressions

13. CPC (Cost Per Click;Cost Per Thousand Click-Through)

14. CPA(Cost-per-Action)

15. CPS(Cost-Per-Sale)

16. CPT cost per time

17. CPV cost per visit

18. CPI cost per visit

19. CPD cost per download

20. banner

21 Interstitial

22. Native Advertising (Native Ads)

Operation related

23. AARRR ：Acquisition、Activation、Retention、Revenue、Refer

24. DNU(Daily New Users)

25. CAC（Customer Acquisition Cost）

26. CPC (Cost Per Customer )

27. CR (Conversions Rates)

28. DAU(Daily Active Users)

29. WAU(Weekly Active Users)

30. MAU(Monthly Active Users)

31. DEC(Daily Engagement Count)

32. DAOT/AT(Daily Avg.Online Time)

33. DAU (Daily Active User)

34. MAU (Monthly active users)

35.Users Retention

36. Day 1/3/7/30 Retention Ratio

37. Users Churn

38. Day 1 Churn Ratio

39. Day 7 Churn Ratio

40. Day 30 Churn Ratio

41. MPR(Monthly Payment Ratio)

42.  MAU, APA

43. APA(Active Payment Account)

44. ARPU(Average Revenue per Uers)

45. ARPU

46. monthly ARPU= /MAU

47.ARPPU(Average Revenue per Paying User)

48. ARPPU

monthly ARPPU=

49. life time

50 life time value

51 PCU(Peak Concurrent Users)

52. ACU(Average Concurrent Users)

53. New Users Converstion Rate

54. SEO(Seach Engine Optimization)

55. SEM (Search Engine Marketing)

56. ASO (App Store Optimization)

57. KPI(Key performance indicators)

58. GMV(Gross Merchandise Voltume )

59. SKU (Stock Keeping Unit)

60. ‎Long Tail Keyword

61. MVP(Minimum Viable Product )

62. SP (Service Provider)

63. CP（Content Provider

64. BD (Business Development)

65. SDK (Software Development Kit)

66. UE/UE(User Experience)

67. EDM (Email Direct Marketing)

68. SNS (Social Networking Services)

69. UGC (User Generated Content)

70. PGC(Professional Generated Content)

71. OGC(Occupationally-generated Content)

72. KOL(Key Opinion Leader)

CS50 a fabulous class

 

https://www.youtube.com/c/cs50/videos

good for beginners

time series, how to choose lag

statistics knowledge websites

 

https://stattrek.com/

https://www.khanacademy.org/math/statistics-probability

https://brilliant.org/

https://www.datacamp.com/community/tutorials