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دسته بندی دوره ها

Payment Risk and Payment Fraud: Data Science and Analytics

سرفصل های دوره

we will learn modeling and coding (SQL/Python) knowledge for data science and data analytics in payment risk


1. Introduction
  • 1. Introduction
  • 2. Course Outline

  • 2. Payment
  • 1.1 Payment Overview v5.pptx
  • 1. Payment Overview
  • 2.1 Payment -- Card V5.pptx
  • 2. Card Transactions
  • 3.1 Payment -- ACH v3.pptx
  • 3. ACH Transactions
  • 4.1 Payment -- Chargeback and Refund v2 (1).pptx
  • 4. Chargeback and Refund

  • 3. Payment Risk
  • 1.1 Payment Risk v3.pptx
  • 1. Payment Risk Overview
  • 2.1 Consumer Risk - ATO v3.pptx
  • 2. ATO Introduction
  • 3.1 How to identify ATO v3.pptx
  • 3. How to identify ATO
  • 4.1 Consumer Risk - SF&NSF v3.pptx
  • 4. Stolen Financial and NSF overview
  • 5.1 How to identify SF and NSF v3.pptx
  • 5. How to identify SF and NSF
  • 6.1 Family Fraud 101.pptx
  • 6. Family Fraud Overview
  • 7.1 How to identify Family Fraud.pptx
  • 7. How to identify Family Fraud
  • 8.1 Merchant Risk Overview v3.pptx
  • 8. Merchant Risk Introduction
  • 9.1 How to identify merchant risk v3.pptx
  • 9. How to identify merchant risk

  • 4. Statistic and ML
  • 1.1 Stats Outline.pptx
  • 1. Stats outline
  • 2.1 Hypothesis.pptx
  • 2. Hypothesis
  • 3.1 Sampling.pptx
  • 3. Sampling
  • 4.1 How to calculate sample size.pptx
  • 4. Sample Size Calculation
  • 5. Confusion Metrix
  • 6.1 ML 101.pptx
  • 6. ML Basic
  • 7.1 Linear Regression 101.pptx
  • 7. Linear Regression 101
  • 8.1 Linear Regression 102.pptx
  • 8. Linear Regression 102
  • 9.1 Linear Regression 103.pptx
  • 9. Linear Regression 103
  • 10.1 Linear Regression 104.pptx
  • 10.2 Linear Regression.xlsx
  • 10. Linear Regression 104
  • 11.1 Logistic Regression 101.pptx
  • 11. Logistic Regression 101
  • 12.1 Logistic Regression 102.pptx
  • 12. Logistic Regression 102
  • 13.1 Decision Tree 101.pptx
  • 13. Decision Tree 101
  • 14.1 Decision Tree 102.pptx
  • 14. Decision Tree 102
  • 15.1 Random Forest 101.pptx
  • 15. Random Forest 101
  • 16.1 Random Forest 102.pptx
  • 16. Random Forest 102
  • 17.1 Gradient Boosting 101.pptx
  • 17. GBDT 101
  • 18.1 Gradient boosting 102.pptx
  • 18. GBDT 102
  • 19.1 xgboost 101.pptx
  • 19. Xgboost 101
  • 20.1 Xgboost 102.pptx
  • 20. Xgboost 102
  • 21.1 Model Testing and Validation.pptx
  • 21. Model testing and evaluation

  • 5. SQL
  • 1. How to run SQL in this class
  • 2.1 Section 3 SQL Example.xlsx
  • 2. where our sql examples are and how to play with them
  • 3.1 SQL - Select.pptx
  • 3. Select
  • 4.1 SQL - Select distinct.pptx
  • 4. Select distinct
  • 5.1 SQL - Where Clause.pptx
  • 5. where clause
  • 6.1 SQL - Group By.pptx
  • 6. group by
  • 7.1 SQL - Aggregate Functions.pptx
  • 7. aggregate function
  • 8.1 SQL - Max Min Function.pptx
  • 8. Maxmin function
  • 9.1 SQL - Having Clause.pptx
  • 9. Having Clause
  • 10.1 SQL - Join Clause.pptx
  • 10. Join
  • 11.1 SQL - In Operator.pptx
  • 11. In operator
  • 12.1 SQL - Not Equal Operator.pptx
  • 12. Not equal operator
  • 13.1 SQL - Date Function.pptx
  • 13. date function
  • 14.1 SQL - case statement.pptx
  • 14. case when statement
  • 15.1 SQL - Cast.pptx
  • 15. cast function
  • 16.1 SQL - Limit and Offset Clause.pptx
  • 16. Limit and offset function
  • 17.1 SQL - Window Functions.pptx
  • 17. window function
  • 18.1 SQL - Subqueries.pptx
  • 18. Subquery
  • 19.1 SQL - Complex Join.pptx
  • 19. Complex Join
  • 20.1 SQL - Join and aggregate functions.pptx
  • 20. Join and aggregate functions
  • 21.1 SQL - combine having and where.pptx
  • 21. combine having and where
  • 22.1 SQL - Duplicates.pptx
  • 22. Duplicates
  • 23.1 SQL - nth.pptx
  • 23. Nth number
  • 24.1 SQL - Previous Date Record.pptx
  • 24. Previous Daterecord
  • 25.1 SQL - Query Efficiency.pptx
  • 25. Query Efficiency

  • 6. Python
  • 1.1 Python input, output and import.pptx
  • 1.2 Python.xlsx
  • 1. Python input and output
  • 2.1 Python Statement, Indentation and Comments.pptx
  • 2. Python Statement, Indentation and Comments
  • 3.1 Python Data Type.pptx
  • 3. Python Data type
  • 4.1 Python Function.pptx
  • 4. Python functions
  • 5.1 Python Operators.pptx
  • 5. Python operator
  • 6.1 Python if..else.pptx
  • 6. Python if else
  • 7.1 Python for loop.pptx
  • 7. Python for loop
  • 8.1 Python while loop.pptx
  • 8. Python while loop
  • 9.1 Python List 101.pptx
  • 9. Python List 101
  • 10.1 Python List 201.pptx
  • 10. Python List 102
  • 11.1 Python tuple 101.pptx
  • 11. Python Tuple 101
  • 12.1 Python Tuple 201.pptx
  • 12. Python Tuple 102
  • 13.1 Python Set 101.pptx
  • 13. Python Set 101
  • 14.1 Python Set 201.pptx
  • 14. Python Set 102
  • 15.1 Python dictionary 101.pptx
  • 15. Python Dictionary 101
  • 16.1 Python Dictionary 201.pptx
  • 16. Python Dictionary 102
  • 17.1 Python numpy 101.pptx
  • 17. Python numpy 101
  • 18.1 Python numpy 201.pptx
  • 18. Python numpy 102
  • 19.1 Python numpy 301.pptx
  • 19. Python numpy 103
  • 20.1 Python numpy 401.pptx
  • 20. Python numpy 104
  • 21.1 Python Pandas 101.pptx
  • 21. Python Pandas 101
  • 22.1 Python numpy 201.pptx
  • 22. Python Pandas 102
  • 23.1 Python Pandas 301.pptx
  • 23. Python Pandas 103
  • 24.1 Python Pandas 401.pptx
  • 24. Python Pandas 104
  • 25.1 Python Pandas 501.pptx
  • 25. Python Pandas 105
  • 26.1 Python Matplotlib 101.pptx
  • 26. Python matplotlib 101
  • 27.1 Python Matplotlib 201.pptx
  • 27. Python matplotlib 102
  • 28.1 Python scikit-learn 101.pptx
  • 28. Python scikit-learn 101

  • 7. Case study
  • 1.1 First Case Overview.pptx
  • 1.2 Nashville housing house details.xlsx
  • 1.3 Nashville housing sale.xlsx
  • 1.4 Nashville housing seller info.xlsx
  • 1. First Case Study Nashville Housing Overview
  • 2.1 Nashville housing thinking process.pptx
  • 2. Thinking process
  • 3.1 Nashville housing overall trend.pptx
  • 3. Nashville Housing Overall Trend
  • 4.1 Nashville Housing Analysis.xlsx
  • 4.2 Nashville housing details.pptx
  • 4. Nashville Housing analysis
  • 5.1 Nashville housing summary.pptx
  • 5. Nashville Housing Summary
  • 6.1 Second Case Overview.pptx
  • 6.2 Section 6 Subscription Details Python - customer info.csv
  • 6. Second Case Study Subscription business model analysis
  • 7.1 Second Case Overall Business Performance.pptx
  • 7.2 subscription business analysis revenue.xlsx
  • 7. Overall business performance
  • 8.1 Second Case Dataframe Analysis.pptx
  • 8.2 Section 6 Subscription Details Python.xlsx
  • 8.3 subscription data analysis.zip
  • 8. Load Subscription business data into dataframe
  • 9.1 Second Case Decision Tree.pptx
  • 9.2 subscription data decision tree model.zip
  • 9. Build a decision tree model in Python to improve business performance

  • 8. Congratulations
  • 1.1 payment risk ds congratulations.pptx
  • 1. Congratulations!
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    مدت زمان: 524 دقیقه
    تاریخ انتشار: 9 آبان 1402
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