it is in chinese,
I wrote about the solutions to some problems I found from programming and data analytics. They may help you on your work. Thank you.
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)
1. how to normalize data
2. how to detect outlier, what is IQR?
3. how to reverse a list in python
4. how to insert a number in a list in python
5. how does spark's rdd work? how is it diffrent from pyspark's dataframe?
6. how to calculate cumulative sums in a table in sql
7. what is the difference between mapreduce and in-memory?
8. what is mapreduce?
9. what is lag?
10. proceeding and in sql?
11. how to count number of data points in a numpy array?
12. how to do hyperthesis test?
13. what is false positive rate? what is false negative rate?
14. how to delete duplicates in a dataframe in python?
15. what is false discovery rate? and bonferroni correction?
what is false discovery rate?
https://www.youtube.com/watch?v=3PVkfQRUGI4
an interesting video talking about it.
it is something we predefined in a hyperthesis testing. a type one erro for the multiple testing we tried to control.
https://www.youtube.com/watch?v=HLzS5wPqWR0
to understand bonferroni correction, first , we need to understand family-wise error rate,
a1=type one error
FWER=1-(1-a1)^m
m is the number of tests
bonferroni correction
corrected a1
=a1/k
k is the number of tests performed.
FWER=1-(1-a1/k)^k
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