Скачать или смотреть Fundamentals of SQL for Banking Analysts: Essential Skills for Financial Data Analysis

Learn how to use SQL-like queries in Python for real-world banking analytics. This tutorial guides you through building and analyzing synthetic banking data sets, including transactions, customers, and accounts. You will practice filtering, joining, grouping, and aggregating data to answer key business questions and generate actionable insights.

Follow step-by-step examples that mirror common SQL operations using pandas, from basic selections to advanced joins and cumulative calculations. By the end, you will be able to manipulate banking data efficiently, avoid common errors, and export your results for business reporting or compliance review.

00:00 Introduction to SQL for banking analysts
00:23 Setting up Python for data analysis
01:09 Understanding banking data sets
01:39 Creating a synthetic transactions table
02:50 Exploring transaction data structure
03:12 Building the customers table
03:44 Reviewing customer data
04:20 Creating and linking accounts
04:49 Reviewing account data
05:21 SQL-like queries in pandas
05:44 Filtering debit transactions
06:33 Finding top transactions by amount
07:25 Counting transactions by channel
08:15 Monthly debit and credit comparison
08:52 Joining transactions with customer data
09:55 Comparing transaction size by segment
10:54 Counting unique customers per channel
11:44 Ranking account types by credited volume
12:42 Calculating running account balances
13:47 Error handling in queries
14:38 Avoiding merge mistakes
15:11 Inspecting and summarizing data
16:10 Saving results for reporting
17:08 Solving a high-value customer problem
18:14 Key takeaways and next steps

#SQL #Python #BankingAnalysis