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Banking & Financial Services Analytics
This learning track introduces the concepts, techniques, and applications of Data Treatment, Data Visualizations, Feature Engineering, Descriptive and Predictive Analysis, Supervised and Unsupervised Machine Learning, ML Model Development, Hyperparameter tuning, Evaluation and Selection, Ensemble Learning, Explainable AI methods in the Banking and Finance industry.
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30 Courses
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18 Projects & Case Studies
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Difficulty: Intermediate
Foundational knowledge or experience in statistics or analytics is recommended
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Duration: Approximately 3 months
Suggested learning pace is 5hr/week
Course Overview
- Learn various Data Management and Data Transformation techniques, Data Visualization, and Univariate & Multivariate analysis to gain insights on financial actions of customers, their credit card usage, loan repayment, stock price analysis, & more.
- Understanding of the concepts and various techniques of Supervised Machine Learning, Unsupervised Machine Learning and Ensemble Learning to fulfil various objectives like prediction of loan default, credit risk analysis, potential term deposit customers, customer attrition etc.
- Develop a thorough understanding of Text analytics, and its application in sentiment analysis of credit card user’s reviews of financial operation, customer support etc.
- Learn how to apply Explainable AI methods like LIME and SHAP to deeply understand the model predictions and perform efficient model selection for further use in real-world problems in the banking and finance industry.
What’s included
Lifetime Access
Access this case study for life once completed
Flexible Scheduling
Start learning online immediately, at your own pace
Desktop Only
We recommend completing this learning track on a desktop
Skills You Will Learn
- Fundamentals of Data Analytics
- Fundamentals of Data Preprocessing
- Data Mining Concepts and Techniques
- Basic Data Visualization Methods- I
- Advanced Feature Engineering techniques
- Machine Learning – Linear Regression
- Model Evaluation Techniques – Regression Models
- Model Selection Techniques
- Machine Learning – Logistic Regression
- Model Evaluation Techniques – Classification Models
- Basics of Hyperparameter Tuning – Linear & Logistic Regression
- Getting Started with Naive Bayes Classifier
- Support Vector Machines in ML
- Hyperparameter Tuning in SVM
- Understanding Decision Trees
- Hyperparameter Tuning in Tree-Based Models
- Bagging & Random Forest in Machine learning
- Introduction to Gradient Boosting Classification
- Introduction to Extreme Gradient Boosting Classifier
- Introduction to AdaBoost Classifier
- Concepts and Application of Objective and Subjective Segmentation
- Understanding Principal Component Analysis (PCA)Fundamentals of Time Series Analysis
- Introduction to Natural Language Processing (NLP)Mining Text Data Cleansing, Treatment, Structural Representation & Visualization
- Text Analytics – Classification and Clustering
- Sentiment Analysis – Using Unstructured Text Data
- Introduction to Explainable AI (XAI) using LIME
- Introduction to Explainable AI (XAI) using SHAP
- Introduction to Explainable AI (XAI) for Text using LIME & SHAP
- EMI Tenure Affinity Testing – A use case for A/B Testing
- Banking Customer profiling using Statistical Analysis
- Historical Campaign Performance Dashboard
- Application Of Descriptive Analytics in Banking – A Credit Card Use Case
- Analyze Credit Default Data – Application of Descriptive Analysis Techniques
- Detecting Credit Card Fraud – Feature Engineering Techniques
- Credit Risk Modelling – Probability of Default: Data Treatment & Feature Selection
- Loan Amount Prediction for different Applicants using Regression Techniques
- Build a Regression Tree for Predicting Spend on Credit Card
- Predict Credit Card Customer Attrition – Application of Logistic Regression
- Identify Customers with Higher Likelihood of Credit Card Attrition – Application of Decision Tree
- Predict Customer Attrition Using Naïve Bayes Classification
- Predicting Acquisition of Start Ups
- Predicting Term Deposit Subscription by Banking Customers using Advanced Classification Techniques
- Customer Segmentation Based on the Transaction History using Advance Clustering Techniques
- Stock Price Analysis for Banking Institutions using Time Series Techniques
- Application of Various Text Clustering Techniques on Customer Feedback Data
- Credit Risk Modelling – Probability of Default: Model Comparison & XAI
How it Works
Learn new skills that will boost your career by enrolling in courses across data analytics, data science, ML and AI. These courses will utilize readings, videos, quizzes, data cases, and even coding exercises to teach you skills and concepts in a way that will solidify your new knowledge for hands-on application.
With our hands-on projects, you will take your newly learned skills along with our 750+ low-code/no-code functions and embedded coding console to complete milestone-based projects. Once completed, you will have effectively applied new skills and concepts to real-world data cases that can be translated directly into your career.
Complete assessments and track your progress in real-time to benchmark your proficiency in relation to key functional areas. As you progress through your courses, our patented platform will utilize ML and AI to record and analyze your inputs and output to provide active feedback and recommendations that will help you learn more effectively than the standard Letter Grade system used today.
Learner Outcomes
Complete courses to earn shareable certificates and badges. These awarded items will look great in your portfolio as you showcase your skills and project experience to employers and colleagues.
- Application of Statistical tests to profile customers, Data visualization to delve deeper on campaign performance, Credit card usage by customers and various attributes of Credit card defaulters.
- Application of Supervised Learning concepts are to solve problems pertaining to term deposit prediction, loan amount prediction, credit default probability and credit card spending.
- Application of Unsupervised Learning techniques to group customers to drive targeted campaigns and segment customers based on their transactional history.
- Application of Text Analytics & XAI to solve problems related to Customer feedback and Credit Risk Modeling
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WHAT THE COMMUNITY SAYS ABOUT US“Great way to learn data analytics and data science”
WHAT THE COMMUNITY SAYS ABOUT US“The courses were excellent and covered topics that I didn’t expect”
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Our courses range from Beginner to Advanced. We have courses and learning tracks that are available for beginners, we do however recommend having a basic level of analytics before starting any beginner course.
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Rolai’s patented process provides a personalized learning process for each user. Rolai goes deeper than simply learning concepts and testing your skills. At Rolai, learners can apply their skills to actual industry use cases and projects.
