Data Analyst Vs Business Analyst Vs Data Engineer Vs BI Specialist

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  Data Science - Complete Track • Step 1 - Identify the business problem/value addition/question – ( Data Insights ) this has to be the starting point. • Step 2 - Data availability ( Data Governance ) - Have the structure of your data set defined – The real challenge starts here-                o Do we have the data?                o Do we have access to the required data? • Step 3 -Getting Data ( Data Mining / Data Pipeline ) – How to collect the data from different sources in the system • Step 4 - Data preparation (ETL / ELT) – Once you have data there will be lot of cleaning and preparation required, reduce/increase/combine/split the predictors, determine and eliminate outliers, populate missing values convert few categorical variables into numerical etc. • Step 5 –Exploratory Data Analysis (EDA) – In this step we do descriptive and diagnostic analysis of the existing data. We build multiple graphs that give us direction towards the next steps of predictive analytics. We migh
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BANKING BASICS

 BANKING BASICS FOR DATA ANALYTICS


TYPE OF BANKS:


Retail Banks

Retail banks offer their services to the general public and usually have branch offices as well as main offices for the convenience of their customers.


They provide a range of services such as checking and savings accounts, loan and mortgage services, financing for automobiles, and short-term loans such as overdraft protection. Many also offer credit cards.


They also offer access to investments.

The larger retail banks also cater to high-net-worth individuals with specialty services such as 

  • Private banking  [ high-net-worth individual (HNWI) ]
  • Wealth management services.


Example : Citibank.


Commercial or Corporate Banks

Commercial or corporate banks tailor their services to business clients, from small business owners to large, corporate entities. Along with day-to-day business banking, these banks also offer credit services, cash management, commercial real estate services, employer services, and trade finance,


JPMorgan Chase and Bank of America are examples of commercial banks, though both have large retail banking divisions as well.


Investment Banks

Investment banks focus on providing corporate clients with complex services and financial transactions such as underwriting and assisting with merger and acquisition (M&A) activity. They are primarily financial intermediaries in these transactions.


Their clients include large corporations, other financial institutions, pension funds, governments, and hedge funds.


Morgan Stanley and Goldman Sachs are among the biggest U.S. investment banks.



As corporate and commercial banks serve increasingly complex needs, 

new      data - and  - analytics-powered tools

can help the front line understand their customers and improve sales.

Banks are in varying stages of their implementations of Advanced Analytics (AA)
Glossary of Banking Terms and Phrases

How are banks using data analytics?

Data analytics in the finance and banking sector is mainly used in

Demand, Supply, and Risk Management.

Such as:

  • Fraud detection
  • Risk modeling for investment banks
  • Credit risk analysis
  • Operational & liquidity risk
  • Sales performance analysis
  • Branch and online channel sales analysis
  • AI-driven chatbots & Virtual Assistants
  • Personalized marketing
  • Lifetime value prediction
  • Recommendation engines












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