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.
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
Shareable Certificate
Earn a sharable certificate upon completion
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
Data Pre-processing
Data Transformation
Data Visualization
Feature Engineering & Reduction
Descriptive Analysis
Model Building
Model Evaluation
Model Selection
Predictive Analytics
Explainable AI
LIME
SHAP
Natural Language Processing
Syllabus
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
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
WHAT THE COMMUNITY SAYS ABOUT US
“Rolai is the best program available for someone looking to enhance their skills”
Connor McEachron
Planning & Analytics @ Brooks Brothers
WHAT THE COMMUNITY SAYS ABOUT US
“Great way to learn data analytics and data science”
Balaji Reddy
Manager – Applications Development
WHAT THE COMMUNITY SAYS ABOUT US
“The courses were excellent and covered topics that I didn’t expect”
Aadarsha G
Student At Ohio Wesleyan University
All the Most Frequently Asked Questions
What People Are Asking About Data Education
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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.
Course Catalog 
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Difficulty: Intermediate
Duration: Approximately 3 months
