Data Science in finance | IABAC

Data science in finance involves using statistical models, machine learning, and data analysis to enhance decision-making. Applications include risk assessment, fraud detection, algorithmic trading, portfolio optimization, and financial forecasting, helping financial institutions make informed, data-driven decisions.

1. Data Scientist
in Finance
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2. TABLE OF CONTENTS
Introduction to Data Science in Finance
Key Applications of Data Science in Finance
Essential Skills for Data Scientists in Finance
Tools & Techniques in Financial Data Science
Real-World Applications in Finance
Big Data’s Role in Finance
Career Paths in Financial Data Science
How to Get Started
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3. INTRODUCTION
TO DATA
SCIENCE IN
FINANCE
Data science in finance uses data to solve
problems and help make better decisions.
It’s important for banks, investment firms,
and insurance companies, which handle a
lot of data every day. By applying data
science, these companies can make
smarter choices, manage risks, find good
investments, prevent fraud, and offer
personalized services, leading to more
efficient business operations.
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4. Risk Management: Predict loan defaults
Investment Insights: Spot stock market
opportunities
Fraud Detection: Prevent financial
fraud
KEY APPLICATIONS OF DATA
SCIENCE IN FINANCE
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5. Financial Data Understanding: Stock prices, trends,
transactions
Programming: Python, R, SQL
Finance Knowledge: Interest rates, risk, returns
ESSENTIAL SKILLS FOR DATA
SCIENTISTS IN FINANCE
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6. TOOLS & TECHNIQUES IN FINANCIAL
DATA SCIENCE
Financial data science relies on
tools like Python, R, and SQL for
data processing, with Excel
used for simpler tasks and
machine learning for complex
analysis. Visualization tools like
Tableau and Matplotlib help
present insights clearly.
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7. Risk Management: Assess default risks
Algorithmic Trading: Automate stock
trading
Fraud Detection: Identify unusual patterns
Personalized Services: Robo-advisors for
investments
Real-World Applications in Finance
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8. BIG DATA’S ROLE IN FINANCE
Data Sources: Transaction records, stock
exchanges, social media
Data Cleaning: Organizing & preparing for
analysis
Challenges: Managing large datasets,
ensuring privacy
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9. CAREER PATHS IN
FINANCIAL DATA SCIENCE
Risk Analyst: Analyze & mitigate financial
risks
Quantitative Analyst (Quant): Build models
for investment decisions
Financial Data Engineer: Manage & process
large datasets
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10. HOW TO GET STARTED
Learn Programming: Start with Python, R
Understand Finance Basics: Risk, returns,
market behavior
Practice: Use real-world datasets
Build a Portfolio: Showcase skills on GitHub
Certifications: Consider IABAC or equivalent
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11. THANK YOU
Visit www.iabac.org