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Full Stack Data Engineering Bootcamp For Banking & Finance

Become a fully fledged data engineer with our bootcamp that takes you from beginner to job-ready in just 4 months.
Design Thinking
Domain Mastery
Real world case studies
upcoming cohorts
June 20 - Oct 20, 2024
upcoming cohorts
July 20 - Nov 20,2024
Tech you will Learn
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Program Overview
Make A Life-Changing Career Choice
BIGGEST GROWING INDUSTRY
$349.6 Billion
Amount industry is set to grow by 2030
IN-DEMAND CAREER
45%
Growth in demand for Data Analysts in the next 5 years
MASSIVE JOB OPENINGS
1M+
Job openings and counting for Data Analysts worldwide.
HIGH ENTRY-LEVEL SALARY
₹8-14 LPA+
Current average CTC for entry-level Data Analysts in India.
HIGHLY PAID JOBS
$74K
Current average salary for data analysts worldwide according to Glassdoor. Huge year-on-year growth.
Don't Just Learn. Specialize.
India’s only course with industry specialization in banking and fianance.
50+
Banking case studies
10+
Porblem solving frameworks
Experience 360° deep specialized learning
50+
Assignments
10+
Industry Projects
100+
Hours of Leanring
Learn with Ai
Our program incorporates modern Gen AI based workflows for data science so that you are equipped with the tools of the future.
Made for working professionals
Enjoy flexible learning options. Go at your own pace or learn through live classes with industry experts.
Placement support from dedicated counselors
Mock interviews with senior industry leaders
Craft the perfect resume
Access our network of partner companies
India’s only course with industry specialization in banking and fianance.
50+
Banking case studies
10+
Porblem solving frameworks
Experience 360° deep specialized learning
50+
Assignments
10+
Industry Projects
100+
Hours of Leanring
Learn with Ai
Our program incorporates modern Gen AI based workflows for data science so that you are equipped with the tools of the future.
Made for working professionals
Enjoy flexible learning options. Go at your own pace or learn through live classes with industry experts.
Placement support from dedicated counselors
Mock interviews with senior industry leaders
Craft the perfect resume
Access our network of partner companies
What This Program Has To Offer
Key Features
Get industry specific training
Easy EMI option
7 day, no questions asked money back policy
30 day Pause
Dedicated student support
1:1 mentorship by industry experts
Get practical training by working on actual banking use cases
Learn through industry-approved learning frameworks
Deep Understanding of Analytics
Learn Data Manipulation & cleaning using Excel, SQL and Python
Statistical analysis
Data Visualization using Tableau, Power BI, Matplotlib and Seaborn
Learn Python and R for data analysis & automation
Database Management 
Machine Learning
Specialization in Banking & Finance
Learn core banking & finance concepts 
Master the different Fintech business models & their primary drivers
Understand how to apply data analytics to diverse Fintech products
Industry-tested Problem Solving Frameworks
Learn 15+ problem solving frameworks for finance
Be confident to tackle any problem at your job
Program Eligibility 
No prior coding experience required
Students with any graduate degree welcome
Basic computer literacy
Fundamental math skills
Curiosity and eagerness to learn
What You'll Learn
Core Data Engineering Algorithms and tools
  1. Proficiency in constructing robust ETL pipelines.
  2. Cloud deployment strategies and architectural principles.
  3. Manage vast datasets using cutting-edge technologies like PySpark and NoSQL databases
  4. Expertise in distributed data processing with PySpark and Kafka.
  5. Master the intricacies of data retrieval, integration, and management, streamlining data workflow.
Communication, Presentation & Business Skills
  1. Clarity in presenting findings, insights, and recommendations through reports or presentations.
  2. Ability to convey complex technical concepts to non-technical stakeholders.
  3. Proficiency in data visualization techniques to communicate information effectively.
  4. Precision in documenting processes, methodologies, and findings.
  5. Vigilance in spotting errors or discrepancies within datasets.

Problem Solving and Design Thinking
  1. Ability to break down complex problems into manageable components.
  2. Ability to approach problems objectively and evaluate evidence logically.
  3. Capacity to assess data quality, identify biases, and challenge assumptions.
  4. Skill in formulating hypotheses and designing experiments to test them.

Core Data Engineer Skills
  1. Develop a comprehensive understanding of data security and governance.
  2. Acquire proficiency in constructing ETL pipelines with PySpark and NoSQL databases
  3. Design and deploy highly scalable and fault-tolerant data infrastructure solutions
  4. Master advanced querying techniques and workflow automation, enhancing organizational efficiency and productivity in data retrieval, integration, and management.
  5. Ability to navigate diverse data types, structures, and database systems effectively.

Core Data Engineer Skills
  1. Develop a comprehensive understanding of data security and governance.
  2. Acquire proficiency in constructing ETL pipelines with PySpark and NoSQL databases
  3. Design and deploy highly scalable and fault-tolerant data infrastructure solutions
  4. Master advanced querying techniques and workflow automation, enhancing organizational efficiency and productivity in data retrieval, integration, and management.
  5. Ability to navigate diverse data types, structures, and database systems effectively.

Curriculum Designed For
Banking & Finance
Module 1: Data Engineering Foundations
4 Lectures

Dive into the fundamentals of data engineering, exploring data types, data structures, and their practical applications. Learn the principles of working with databases, including relational (RDBMS) and NoSQL, and master the art of querying data using SQL.

  • 1.1 Introduction to Data Types and Data Structures:
    • Understanding data types and structures is essential for efficient data storage, retrieval, and manipulation in various applications.
  • 1.2 Introduction to Data and its Application:
    • Data drives decision-making in industries like retail, healthcare, and finance, influencing strategies for marketing, operations, and research.
  • 1.3 Working with Databases (RDBMS, NoSQL), Data Models, and Schema:
    • Utilized in e-commerce platforms for storing customer data and transaction records, facilitating personalized recommendations and sales analysis.
  • 1.4 Querying Data with SQL:
    • SQL is used in financial institutions for analyzing transaction data and generating reports on account balances, fraud detection, and regulatory compliance.
Module 2: Data Warehousing
5 Lectures

Delve into the intricacies of building ETL pipelines using PySpark, enabling you to ingest, process, and transform large-scale datasets efficiently. Explore data ingestion techniques with Sqoop and build streaming data pipelines using PySpark and NoSQL databases.

  • 1.1 Building ETL Pipeline with PySpark:
    • PySpark ETL pipelines are employed in e-commerce companies for processing and analyzing large volumes of sales data to optimize inventory management and supply chain operations
  • 1.2 Data Ingestion (Sqoop):
    • Sqoop is utilized in healthcare systems for transferring patient data from on-premise databases to cloud-based platforms for analysis and research.
  • 1.3 Building Streaming Data Pipeline (PySpark and NoSQL):
    • PySpark streaming pipelines are deployed in social media platforms for real-time analysis of user interactions, enabling targeted advertising and content recommendation.
  • 1.4 Processing Large Data Sets:
    • Used in transportation networks for analyzing traffic patterns and optimizing route planning for delivery vehicles.
  • 1.5 Data Visualization:
    • Employed in marketing agencies for creating interactive dashboards to visualize campaign performance metrics and customer engagement data.

Module 3: Data Engineering Deployment
4 Lectures

Unlock the power of deploying big data solutions on cloud platforms, understanding cloud application architecture and scalability considerations. Learn how to deploy web services within and outside a cloud architecture, ensuring seamless integration and performance optimization.

  • 1.1 Deploying Big Data Solutions on Cloud:
    • Cloud-based big data solutions are deployed in manufacturing industries for monitoring and optimizing production processes, reducing downtime, and improving efficiency.
  • 1.2 Cloud Application Architecture:
    • Implemented in financial institutions for developing secure and scalable banking applications, facilitating online transactions and customer account management.
  • 1.3 Deploying a Web Service from Inside and Outside a Cloud Architecture:
    • Utilized in e-learning platforms for deploying web services to deliver educational content and track student progress.
  • 1.4 Data Scalability & Cloud Services:
    • Scalable cloud services are utilized in telecommunications companies for processing and analyzing vast amounts of network data to optimize network performance and customer experience.

Module 4: Data Retrieval and Integration
3 Lectures

Gain insights into retrieving, integrating, and processing data on cloud platforms, exploring fundamental concepts of data management and mining. Dive deep into Apache Hive for querying and processing large datasets, and automate data processing workflows with Oozie and Zookeeper.

  • 1.1 Fundamentals of Data on Cloud:
    • Cloud-based data services are employed in retail chains for centralized inventory management and sales analytics across multiple store locations.
  • 1.2 Retrieval and Integration:
    • Used in logistics companies for integrating shipment tracking data from multiple carriers and warehouses to provide real-time visibility to customers.
  • 1.3 Mining and Processing Data:
    • Data mining techniques are applied in healthcare organizations for analyzing patient records to identify disease patterns and improve diagnosis and treatment protocols.
Module 5: Data Management
1 Lectures

Master the fundamentals of data warehousing operations, including ETL operations, data storage, and querying with Hive. Explore advanced data transfer techniques using Sqoop and Flume, enabling efficient data movement across disparate systems.

Module 6: Security & Governance
3 Lectures

Understand the critical aspects of data security and governance, including enterprise security, infrastructure security, and compliance mechanisms. Learn how to design robust security architectures and implement vulnerability assessment and penetration testing (VA & PT) mechanisms

  • 1.1 Data Security and Privacy:
    • Data security measures are implemented in government agencies for protecting sensitive citizen information stored in databases and preventing unauthorized access.
  • 1.2 Enterprise Security:
    • Enterprise security practices are employed in banking institutions for securing customer financial data and preventing cyber-attacks and fraud.
  • 1.3 Infrastructure Security:
    • Infrastructure security protocols are implemented in telecommunications companies for securing network infrastructure and preventing data breaches and service disruptions.
  • 1.4 Network, OS, Database & Mobile Security:
    • Network, OS, database, and mobile security measures are implemented in technology companies for protecting corporate data and intellectual property from cyber threats and data leaks.
  • 1.5 Security Architecture and VA & PT Mechanism:
    • Security architecture and vulnerability assessment mechanisms are employed in defense organizations for securing military networks and systems from cyber threats and attacks.
Module 7: Data Processing with PySpark
7 Lectures

Dive into the world of distributed data processing with PySpark, exploring SparkContext, SparkSession, and DataFrame operations. Learn advanced techniques for optimizing performance, working with streaming data, and deploying scalable machine learning models.

  • 1.1 Spark Context and SparkSession:
    • Understand the foundational components of PySpark for distributed data processing and management.
  • 1.2 Data Loading and Storage:
    • Learn methods for loading data into PySpark and storing it in various formats for efficient processing.
  • 1.3 DataFrame Operations and Transformations:
    • Dive into DataFrame operations and transformations to manipulate and prepare data for analysis and modeling.
  • 1.4 Optimizing Performance:
    • Explore techniques for optimizing PySpark performance, including caching, partitioning, and leveraging cluster resources effectively.
  • 1.5 Working with Streaming Data:
    • Gain insights into processing real-time streaming data with PySpark, enabling timely analysis and decision-making.
  • 1.6 Machine Learning with PySpark:
    • Harness the power of PySpark for machine learning tasks, including model training, evaluation, and deployment.
  • 1.7 Deployment and Scalability:
    • Learn strategies for deploying PySpark applications in production environments and scaling them to handle large volumes of data efficiently.
Module 8: Data Processing with Kafka
6 Lectures

Explore Kafka fundamentals, including setup, configuration, and producing/consuming data streams. Discover stream processing with Kafka Streams, ensuring fault tolerance, scalability, and compliance with security standards.

  • 1.1 Setup and Configuration:
    • Establish and configure Kafka clusters to facilitate real-time data processing and messaging in enterprise environments.
  • 1.2 Producing and Consuming Data:
    •  Implement data producers and consumers to ingest and distribute streaming data across distributed Kafka topics for real-time analytics.
  • 1.3 Stream Processing with Kafka Streams:
    • Utilize Kafka Streams for real-time data processing, enabling applications such as fraud detection, monitoring, and anomaly detection.
  • 1.4 Fault Tolerance and Scalability:
    • Ensure fault tolerance and scalability in data processing pipelines by leveraging Kafka's distributed architecture and replication mechanisms.
  • 1.5 Monitoring and Operations:
    • Monitor Kafka clusters and data pipelines to ensure smooth operation, detect performance bottlenecks, and maintain high availability.
  • 1.6 Security and Compliance:
    • Implement security measures and compliance mechanisms to protect sensitive data and ensure regulatory compliance in Kafka deployments, particularly in industries such as finance and healthcare.
Industry Case Studies
You'll Work On
Advanced Fraud Detection Techniques in Banking
Learn the skills and knowledge to detect fraudulent activities in credit card transactions, ensuring financial security for banking institutions and customers.
Skills Learned:
Data Preprocessing
Feature Engineering
Model Evaluation
Anomaly Detection Techniques
Operational Efficiency Optimization in Banking
Develop the tools and techniques to forecast call volumes in banking call centers, improving resource allocation and enhancing customer service efficiency.
Skills Learned:
Time Series Forecasting
Model Evaluation
Operational Optimization Techniques
Customer Segmentation Strategies for Banking Marketing
Learn how to segment bank customers based on their behavior and demographics to optimize marketing campaigns for targeted customer engagement and retention.
Skills Learned:
Data Visualization
Clustering Algorithms
Customer Profiling techniques
Sales Prediction and Optimization in Banking
Learn how to predict sales performance and optimize sales strategies for banking products and services that drive revenue growth and customer acquisition.
Skills Learned:
Predictive Modeling
Sales forecasting
Model Interpretation Techniques
Real-time ATM Fraud Detection in Banking
Equip yourself with the skills to detect fraudulent activities in ATM transactions, safeguarding banking institutions and customers from financial losses.
Skills Learned:
Data Preprocessing
Model Evaluation
Anomaly Detection Techniques
Predictive Modeling for Loan Default Prediction in Banking
Predict loan default probabilities that enable banks to assess credit risk and make informed lending decisions.
Skills Learned:
Feature engineering
Risk Assessment
Model Evaluation Techniques
Predictive Analytics for Customer Lifetime Value Estimation
Learn how to estimate the lifetime value of bank customers, facilitating targeted marketing strategies and personalized customer experiences.
Skills Learned:
Customer Segmentation
Predictive modeling
Model Evaluation
Personalized Cross-Selling Recommendations in Banking
Gain the skills to build recommendation systems for cross-selling banking products and services based on customer behavior and preferences.
Skills Learned:
Collaborative Filtering
Recommendation Algorithms
Customer Profiling Techniques
Machine Learning for Mortgage Risk Assessment in Banking
Learn the tools to assess mortgage loan risks, enabling banks to make informed lending decisions and manage credit risk effectively.
Skills Learned:
Feature Selection
Risk Modeling
Credit Scoring Techniques
“The team was thrilled with the quality of instruction provided. We have requests from teams from other departments to undertake the training as well.”
Avinash Purohit
DGM, Canara Bank
Is This Bootcamp Right For You?
Are you looking for a career change?
Do you want to switch from your current job to a more lucrative career as a data analyst in finance ?
Do you want a promotion?
Are you a banking or finance professional looking to upgrade your career with the most sought after skill in today’s market - data analytics?
Are you a beginner to data analytics?
Are you a recent graduate looking for a comprehensive program to teach you everything you need to know to launch your career as a data analyst in the financial sector?

If you answered "Yes" to any of these questions, SkillCamper's Data Analyst Bootcamp is the perfect fit for you!

Try a free Masterclass
Alumni Success Stories

From career switchers to college grads, we have helped a diverse range of learners kickstart & progress rapidly in their data science careers. 

“I decided to shift my career to Data Science.”

After joining SkillCamper’s Data Science Bootcamp my confidence has increased because of the 1:1 attention I got from mentors that helped me cope with the new field. My doubts were resolved easily and the instructors are great!
Fatema Rampurawala
Graduate in BSC-IT

 “I have received a lot of help from the cohort.”

Everybody is collaborative and we help each other in getting clearer understanding when we are stuck. SkillCamper fosters a supportive environment where every question is valued, ensuring that no one feels left behind.
Pragati Jha
Public Relations Professional

“I feel 85-90% confident about sitting in an interview.”

SkillCamper’s soft skills training and placement support has been a huge help. The Data Science Bootcamp was a great learning experience and I’m eager to start applying these skills at my job. 
Supriya K
Final Year Student
SkillCamper Is Made For Working Professionals.

You don’t need to quit your job to learn in-demand skills and upgrade your career. Go at your own pace and get 1:1 support from mentors, program advisors and career experts all dedicated to helping you succeed.

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Curriculum & Course Materials

Live coding environment

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100+ hours of instruction

20+ assignments

10+ banking & finance case studies

Banking & finance domain focused curriculum

Capstone projects

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Cancel anytime in first 7 days, full refund

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15+ hours of sessions with industry veterans & experts

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Frequently Asked Questions
What technologies will I learn in the Data Science bootcamps?
A1: You will learn Python, R, SQL, Power BI, Tableau, Excel, Pandas, Numpy, Matplotlib, Seaborn, and PySpark.
Can I start with no prior experience in Data Science?
Yes, our programs are designed for both beginners and those looking to deepen their knowledge. We offer fundamental courses as well as advanced bootcamps.
What is the duration of your Data Science bootcamps?
Our bootcamps are designed to be completed in 4 months with a focus on real-world applications in the banking and finance sectors.
How is the curriculum for the Data Science bootcamp structured?
The curriculum includes hands-on projects, case studies focused on the banking and finance industry, and courses on core data analysis tools and techniques.
Will I have access to the course materials after the bootcamp is over?
Yes, you will have lifetime access to all course materials, which you can refer to at any time to refresh your knowledge or tackle new challenges.
 What type of job support do you provide after completing the course?
We offer comprehensive placement assistance, including resume building, mock interviews, and leveraging our network of industry partners.
How do you prepare students for the job market?
We equip students with industry-relevant skills, help craft winning resumes, and provide mock interview practice with experienced mentors.
Do you guarantee a job after completing the course?
While we do not guarantee a job, we provide extensive support to help you become highly competitive in the job market.
 Can you help me find a job in any specific industry?
Our job assistance is generalized; we prepare you for a variety of roles in the data science field rather than focusing on specific industries.
What is the cost of the Data Science bootcamp?
The cost varies depending on the program. Our full bootcamp is priced between ₹50,000 and ₹75,000, depending on whether you choose self-paced or live instruction.
 Are scholarships available for the courses?
Yes, we offer scholarships that can cover up to 70% of the tuition fees, making our courses more accessible to a wider range of students.
What is included in the course fee?
The fee includes access to all course materials, live coding sessions, AI-based learning platform, case studies, capstone projects, and mentorship from industry experts.
What payment options are available for the course fees?
We offer flexible payment options, including easy EMIs, and you can cancel anytime in the first 7 days for a full refund.
Is financial aid or other support available aside from scholarships?
While our primary financial support is through scholarships, our enrollment advisors can also assist you with payment plans and financing options to help manage the cost of your education.
SkillCamper Learner Support

Talk to program advisors.
We are here to help you anytime you need.