Data Analytics for Commercial and Retail Banking
Master the art of Data Analytics in the dynamic field of Commercial and Retail Banking. Join our immersive course designed to equip you with cutting-edge analytical skills. Transform raw data into strategic insights that drive financial success.
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What you'll learn
- Understand the impact of Data Analytics on customer behavior prediction and cost efficiency in banking.
- Perform Customer Segmentation with advanced clustering and classification techniques.
- Acquire expertise in Data Visualization to create compelling dashboards and storyboards with Tableau.
- Analyze real-world case studies to understand the application of Data Analytics in banking scenarios.
- Learn about Data Quality management and database selection (SQL vs. NoSQL) for banking applications.
- Navigate through setting up a comprehensive Data Architecture including ingestion tools and data lakes.
- Develop skills in Predictive Analytics with practical exercises in Python & R for decision-making.
- Gain proficiency in Exploratory Data Analysis using statistical distributions and fitting linear models.
Why should you attend?
Data Analytics is transforming the commercial and retail banking landscape by enabling institutions to predict customer behavior, improve cost efficiency, and foster innovation. This course delves into the utilization of analytics within the banking sector, exploring foundational concepts such as data processing and quality, SQL vs. NoSQL databases, and the nuances of exploratory data analysis including statistical distributions and linear modeling. Learners will gain hands-on experience with predictive analytics using Python & R, understanding techniques like decision trees, collaborative filtering, and neural networks for churn modeling. The intricacies of customer segmentation using advanced clustering and classification methods are also covered. Moreover, participants will learn to effectively present their findings through data visualization with tools like Tableau and will be guided through setting up robust data architectures incorporating modern technologies like Nifi and Hadoop. Practical case studies provide real-world insights into how data analytics drives business decisions in banking.
Course Syllabus
Day 1 - Banking Analytics Fundamentals
Short Break
15 minsShort Break
15 minsRecap and Q&A
15 minsLunch
1 hourShort Break
15 minsShort Break
15 minsShort Break
15 minsRecap and Q&A
15 minsEnd of Day 1
Day 2 - Advanced Analytics Implementation
Short Break
15 minsShort Break
15 minsRecap and Q&A
15 minsLunch
1 hourShort Break
15 minsShort Break
15 minsShort Break
15 minsRecap and Q&A
15 minsEnd of Day 2
FAQs
Why should you attend?
Data Analytics is transforming the commercial and retail banking landscape by enabling institutions to predict customer behavior, improve cost efficiency, and foster innovation. This course delves into the utilization of analytics within the banking sector, exploring foundational concepts such as data processing and quality, SQL vs. NoSQL databases, and the nuances of exploratory data analysis including statistical distributions and linear modeling. Learners will gain hands-on experience with predictive analytics using Python & R, understanding techniques like decision trees, collaborative filtering, and neural networks for churn modeling. The intricacies of customer segmentation using advanced clustering and classification methods are also covered. Moreover, participants will learn to effectively present their findings through data visualization with tools like Tableau and will be guided through setting up robust data architectures incorporating modern technologies like Nifi and Hadoop. Practical case studies provide real-world insights into how data analytics drives business decisions in banking.
What you'll learn
- Understand the impact of Data Analytics on customer behavior prediction and cost efficiency in banking.
- Perform Customer Segmentation with advanced clustering and classification techniques.
- Acquire expertise in Data Visualization to create compelling dashboards and storyboards with Tableau.
- Analyze real-world case studies to understand the application of Data Analytics in banking scenarios.
- Learn about Data Quality management and database selection (SQL vs. NoSQL) for banking applications.
- Navigate through setting up a comprehensive Data Architecture including ingestion tools and data lakes.
- Develop skills in Predictive Analytics with practical exercises in Python & R for decision-making.
- Gain proficiency in Exploratory Data Analysis using statistical distributions and fitting linear models.
Course Syllabus
Day 1 - Banking Analytics Fundamentals
Short Break
15 minsShort Break
15 minsRecap and Q&A
15 minsLunch
1 hourShort Break
15 minsShort Break
15 minsShort Break
15 minsRecap and Q&A
15 minsEnd of Day 1
Day 2 - Advanced Analytics Implementation
Short Break
15 minsShort Break
15 minsRecap and Q&A
15 minsLunch
1 hourShort Break
15 minsShort Break
15 minsShort Break
15 minsRecap and Q&A
15 minsEnd of Day 2
Public Pricing
Corporate Pricing
Pax:
Training Provider Pricing
Pax:
Features
Subsidies
FAQs
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