Banking Data Analytics Course
Banking Data Analytics certification course
This specialized Banking Data Analytics certification course in Vadodara explores the vital role of data analytics within the banking and financial services sectors. It is designed to empower professionals with the skills to utilize data for enhanced decision-making, risk management, and improved customer experiences. The Banking Data Analytics training in Vadodara equips participants with a deep understanding of how analytics can transform banking operations and drive strategic growth.
Through a structured blend of theory and practice, students learn key analytical techniques such as customer segmentation, churn analysis, and sentiment analysis—tools critical to building stronger customer relationships. The program also dives into fraud detection using transactional data and anomaly detection algorithms, helping banks improve security and regulatory compliance.
Participants will develop predictive models to assess credit risk, optimize loan portfolios, and enhance decision-making in lending. The course also focuses on improving operational efficiency by identifying and resolving process inefficiencies through data-driven methods. Practical projects and real-world case studies give learners hands-on experience in tackling real banking challenges, making this one of the best Banking Data Analytics training programs in Vadodara.
Key Benefits of the Course
Graduates of this course gain the ability to transform vast, complex financial data into actionable business insights. These insights are crucial for improving customer satisfaction, streamlining operations, and enhancing financial decision-making.
Some of the standout benefits include:
- In-depth understanding of how analytics supports key banking functions
- Hands-on training with real-world data and tools
- Proficiency in risk modeling and fraud detection
- Career readiness for roles like data analyst, financial analyst, or risk manager
Whether you’re just starting out or seeking to upskill, enrolling in these Banking Data Analytics coaching classes in Vadodara can significantly boost your career trajectory. The course also emphasizes data ethics and privacy, ensuring participants understand responsible data usage in a regulated industry.
With a focus on practical application, strategic thinking, and technical expertise, this Banking Data Analytics certification course in Vadodara prepares professionals to lead data-driven initiatives and contribute meaningfully to their financial institutions.
What will I learn?
- How to collect, clean, and preprocess banking data for analysis.
- Gain proficiency in descriptive, diagnostic, predictive, and prescriptive analytics techniques specific to banking.
- Develop skills in customer segmentation, churn prediction, and cross-selling analytics.
- Apply banking data analytics techniques to real-world banking scenarios and case studies.
Requirements
- Basic understanding of banking concepts and terminology.
- No prior experience with programming or statistical software is necessary.
Banking Data Analytics Course Content
- SQL Fundamentals
- Various types of databases
- Introduction to Structured Query Language
- Distinction between client server and file server databases
- Understanding SQL Server Management Studio
- SQL Table basics
- Data types and functions
- Transaction-SQL
- Authentication for Windows
- Data control language
- The identification of the keywords in T-SQL, such as Drop Table
- Database Normalization
- Entity Relationship Model
- SQL Operators
- Working with SQL
- Join
- Tables
- Variables
- Advanced concepts of SQL tables
- SQL functions
- Operators & queries
- Table creation
- Data retrieval from tables
- Combining rows from tables using inner, outer, cross, and self joins
- Deploying operators such as ‘intersect,’ ‘except,’ ‘union,’
- Temporary table creation
- Set operator rules
- Table variables•
- Deep Dive into SQL Functions
- Working with Subqueries
- SQL Views, Functions, and Stored Procedures
- Deep Dive into User-defined Functions
- SQL Optimization and Performance
- SQL Server Management Studio
- Using pivot in MS Excel and MS SQL Server
- Differentiating between Char, Varchar, and NVarchar
- XL path, indexes and their creation
- Records grouping, advantages, searching, sorting, modifying data
- Clustered indexes creation
- Use of indexes to cover queries
- Common table expressions
- Index guidelines
- Managing Data with Transact-SQL
- Querying Data with Advanced Transact-SQL Components
- Programming Databases Using Transact-SQL
- Creating database programmability objects by using T-SQL
- Implementing error handling and transactions
- Implementing transaction control in conjunction with error handling in stored procedures
- Implementing data types and NULL
- Designing and Implementing Database Objects
- Implementing Programmability Objects
- Managing Database Concurrency
- Optimizing Database Objects
- Advanced SQL
- Correlated Subquery, Grouping Sets, Rollup, Cube
- Implementing Correlated Subqueries
- Using EXISTS with a Correlated subquery
- Using Union Query
- Using Grouping Set Query
- Using Rollup
- Using CUBE to generate four grouping sets
- Perform a partial CUBE
Basic Math
- Linear Algebra
- Probability
- Calculus
- Develop a comprehensive understanding of coordinate geometry and linear algebra.
- Build a strong foundation in calculus, including limits, derivatives, and integrals.
- What is Keras?
- How to Install Keras?
- Why to Use Keras?
- Different Models of Keras
- Preprocessing Methods
- What are the Layers in Keras?
- Descriptive Statistics
- Sampling Techniques
- Measure of Central Tendency
- Measure of Dispersion
- Skewness and Kurtosis
- Random Variables
- Bassells Correction Method
- Percentiles and Quartiles
- Five Number Summary
- Gaussian Distribution
- Lognormal Distribution
- Binomial Distribution
- Bernoulli Distribution
- Inferential Statistics
- Standard Normal Distribution
- ZTest
- TTest
- ChiSquare Test
- ANOVA / FTest
- Introduction to Hypothesis Testing
- Null Hypothesis
- Alternet Hypothesis
- Probability Theory
- What is Probability?
- Events and Types of Events
- Sets in Probability
- Probability Basics using Python
- Conditional Probability
- Expectation and Variance
- Python Programming
- Python for Analytics
- R Programming
- R for Anlalytics
- Introduction
- Roles
- Snowflake Pricing
- Resource Monitor – Track Compute Consumption
- Micro-Partitioning in Snowflake
- Clustering in Snowflake
- Query History & Caching
- Load Data from AWS – CSV / JASON / PARQUET & Stages
- Snow pipe – Continuous Data Ingestion Service
- Different Type of Tables
- Time Travel – Work with History of Objects & Fail Safe
- Task in Snowflake – Scheduling Service
- Snowflake Stream – Change Data Capture (CDC)
- Zero-Copy Cloning
- Snowflake SQL – DDL
- Snowflake SQL – DML & DQL
- Snowflake SQL – Sub Queries & Case Statement
- Snowflake SQL – SET Operators
- Snowflake SQL – Working with ROW NUMBER
- Snowflake SQL – Functions & Transactions
- Procedures
- User defined function
- Types of Views
- Intro to Qlik View
- Installation of Qlik view
- Data Modelling in Qlik View
- Circular reference
- Link Tables to your model
- Joins in Qlik view
- ETL in Qlik View
- Handling Null Values
- Visualizations in Qlik View
- Pivot Table in Qlik View
- KPI Development in Qlik View
- Introduction to Alteryx
- Download and Install Alteryx
- User Interface of Alteryx
- Get Data from Excel
- Get Data from CSV
- Append All CSV files
- Browse Tool
- Output Tool – Update Existing Data
- Directory Tool
- Directory Tool – Specific Files
- Text Input Tool
- Date and Time Tool
- Auto Field Tool
- Data Cleansing Tool
- Filter Tool (Text Example)
- Filter Tool (Number Example)
- Filter Tool ( Date Example)
- Formula Tool ( Basic Example )
- Formula Tool – (Multiple Examples)
- Generate Rows Tool
- Imputation Tool
- Multi-Field Binning Tool
- Multi-Field Formula
- Multi Row Formula
- Random % Sample Tool
- Sample Tool
- Record Id Tool
- Select Tool
- Sort
- Create Sample Tool
- Tile Tool
- Unique Tool
- Append Fields Tool
- Find And Replace Tool
- Fuzzy Match Tool
- Join Tool
- Join Multiple Tool
- Union Tool
- Regex Tool
- Text To Columns
- Cross Tab Tool
- Transpose Tool
- Running Total Tool
- Summarize Tool
- Table Tool
- Interactive Chart Tool
- Join Table And Chart
- Add Annotation
- Report Text Tool
- Report Header Tool
- Report Footer Tool
- Report Layout Tool
- Comment Tool
- Explorer Tool
- Container Tool
- Cloud AWS
- Cloud Azure
- Introduction to GIT
- Version Control System
- Introduction and Installation of Git
- History of Git
- Git Features
- Introduction to GitHub
- Git Repository
- Git Features
- Bare Repositories in Git
- Git Ignore
- Readme.md File
- GitHub Readme File
- GitHub Labels
- Difference between CVS and GitHub
- Git – SubGit
- Git Environment Setup
- Using Git on CLI
- How to Setup a Repository
- Working with Git Repositories
- Using GitHub with SSH
- Working on Git with GUI
- Difference Between Git and GitHub
- Working on Git Bash
- States of a File in Git Working Directory
- Use of Submodules in GitHub
- How to Write Good Commit Messages on GitHub?
- Deleting a Local GitHub Repository
- Git Workflow Etiquettes
- Git Packfiles
- Git Garbage Collection
- Git Flow vs GitHub Flow
- Git – Difference Between HEAD, Working Tree and Index
- Git Ignore
- Introduction of Scum and Agile
- How to differentiate between Waterfall and Agile
- Agile Framework
- Agile Manifesto
- Agile Principles
- Top Agile Methodologies
- Scrum terminology and roles
- Managing tasks and events within a Sprint
- Scrum Framework
- Introduction to Scrum Framework
- Three pillars of Scrum Framework
- Values of Scrum
- When to use Scrum
- Cross-Functional, Self-Organizing Teams
- Scrum Team philosophy
- Developers
- Product Owner
- Scrum Master
- Scrum Events and Planning
- Scrum Events
- Understanding Sprint
- Sprint Planning
- Daily Scrum Meeting
- Sprint Review Meeting
- Sprint Retrospective
- Scrum Planning with backlog
- Product Backlog
- Refining Backlog
- Backlog items Estimation
- Planning Poker
- T-Shirt Sizing
- Defining Product Goals
- User Stories and INVEST
- Sprint Backlog
- Definition of Done
- Product Increment
- Definition of Done
- Objective and Scope
- Objective: To improve the effectiveness of marketing campaigns by analyzing customer behavior data.
- Scope: Focus on a retail company’s email marketing campaigns over the past year.
- Background Information
- Business Context: A retail company wants to increase sales through targeted email marketing campaigns.
- Data Sources: Customer transaction data, email campaign engagement metrics (open rates, click-through rates), demographic information.
- Data Collection and Preparation
- Data Collection: Gather transactional data from the company’s CRM system and email campaign data from marketing tools.
- Data Cleaning: Handle missing values, remove duplicates, and ensure data consistency.
- Data Transformation: Merge datasets, perform segmentation based on customer demographics and purchase history.
- Data Analysis and Exploration
- Descriptive Analytics: Analyze email open rates, click-through rates, and conversion rates over time.
- Exploratory Data Analysis (EDA): Identify trends in customer behavior and preferences.
- Customer Segmentation: Use clustering algorithms to group customers based on their purchasing patterns and demographics.
- Modeling and Analysis
- Predictive Analytics: Build a predictive model to forecast customer response to different types of email campaigns.
- Campaign Optimization: Use A/B testing to compare the effectiveness of different campaign strategies.
- ROI Calculation: Estimate the return on investment (ROI) for each campaign based on sales uplift.
- Interpretation of Results
- Business Insights: Discover that customers in certain demographics respond better to personalized product recommendations.
- Impact Analysis: Show that targeted campaigns led to a 15% increase in sales compared to generic promotions.
- Case Study Structure
- Introduction: Overview of the retail company’s marketing challenges and objectives.
- Methodology: Detailed explanation of data collection, analysis techniques, and modeling approach.
- Results: Presentation of findings including visualizations (charts, graphs) and statistical analysis.
- Discussion: Interpretation of results, implications for marketing strategy, and actionable recommendations.
- Conclusion: Summary of key findings and the impact of data-driven insights on business outcomes.
- Review and Validation
- Peer Review: Obtain feedback from marketing experts and data analysts within the company.
- Validation: Validate findings by comparing with historical performance and conducting sensitivity analyses.
- Presentation and Documentation
- Presentation: Prepare a compelling slide deck with key findings, visualizations, and actionable insights.
- Documentation: Document methodologies, assumptions, and data sources for transparency and reproducibility.
- Dissemination
- Publishing: Share the case study internally with stakeholders and externally through industry conferences or publications.
- Feedback: Gather feedback from stakeholders to refine strategies and improve future campaigns.
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Why Choose Banking Data Analytics Certification Course from Bright Computer Education?
Banking Data Analytics courses are designed to offer a specialized, hands-on, and data-driven learning experience for individuals looking to make impactful decisions in the banking and financial services sector. Whether you’re aiming to Learn Banking Data Analytics in Vadodara, just starting out with Banking Data Analytics for beginners in Vadodara, or ready to sharpen your skills through Advanced Banking Data Analytics training in Vadodara, these programs are structured to support learners at every level. The curriculum focuses on fraud detection, credit risk analysis, customer segmentation, and regulatory reporting using tools like SQL, Python, Excel, and BI platforms. With real-world banking case studies, interactive sessions, and expert mentorship, students gain the analytical skills and industry knowledge required to succeed in today’s data-driven banking environment.
Designed Curriculum
Our curriculum covers everything from basic to advanced topics. Topics include variables, data types, control structures, functions, OOP, STL, and more.
Hands-on Learning
Dive into practical exercises and coding projects that reinforce learning and help you build real-world applications.
Experienced Instructors
Learn from industry experts with years of experience in C programming and software development.
Flexible Learning
Choose from flexible scheduling options, including self-paced learning or live virtual classes to fit your busy lifestyle.
Career Development
Gain valuable skills sought after by employers in various industries, from software development to embedded systems and beyond.
Interactive Learning
Engage with fellow learners and instructors through live Q&A sessions, discussion forums, and collaborative coding exercises.
Diverse Career Opportunities in Banking Data Analytics: Exploring Paths in India's Technology Sector
Banking Data Analytics focuses on using data to improve decision-making in areas such as credit risk, fraud detection, customer segmentation, loan analysis, and regulatory compliance. A course in this field equips learners with key tools like Excel, SQL, Python, R, and visualization platforms such as Power BI or Tableau, tailored for the banking and financial services sector.
In India, entry-level professionals in banking analytics can earn between ₹5–9 lakhs per annum, with top private banks, fintech firms, and NBFCs offering higher packages for skilled analysts. Internationally, in markets like the U.S., UK, Canada, and Singapore, banking data analysts earn between $85,000 to $120,000 annually.
After gaining 2–4 years of experience, professionals can grow into roles such as Risk Analyst, Credit Analyst, Fraud Analyst, or Data Scientist in banking. The growing digitization of financial services has made data analytics a critical function in modern banking.
In summary, a Banking Data Analytics course offers strong and stable career opportunities in both India and abroad—perfect for those aiming to build a career at the intersection of finance, data, and technology.
Frequently Asked Questions
Typically, the Banking Data Analytics course can be completed in around 2 to 4 months. The exact duration depends on how much time you dedicate each week to learning the concepts, completing assignments, and working on practical Banking-related projects
You don’t need a deep technical background to get started. Having basic knowledge of Banking processes and a general comfort with working on spreadsheets or simple analytics tools will be beneficial. The course is structured to guide you from the basics to more advanced topics at a steady pace.
Yes. Upon successfully finishing the coursework and assessments, you will earn a certification in Banking Data Analytics. This certificate validates your ability to apply data-driven strategies in human resource management, enhancing your professional profile.
You will have access to recorded lectures, interactive assignments, real-world finance projects, and case studies. Many programs also offer live mentorship, one-on-one doubt-clearing sessions, and online discussion groups to help you stay on track and deepen your understanding.
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