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Full Stack Data Science Bootcamp For Banking & Finance

Become a fully fledged data science 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

Dive into the world of data science and embark on a journey to master essential concepts and techniques. Whether you're a beginner or seeking to enhance your skills, this comprehensive boot camp provides you with the knowledge and tools needed to excel in the field of data science in banking and finance. Learn at your own pace and unlock exciting opportunities in today's data-driven world.

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 Science Algorithms and tools
  1. Proficiency in Data Manipulation and Exploratory Data Analysis (EDA) techniques.
  2. Machine Learning algorithms including Supervised & Unsupervised learning techniques.
  3. Advanced Machine Learning skills including Ensemble learning, neural networks and Natural Language Processing (NLP).
  4. Understand model deployment strategies, big data technologies, and data engineering principles.
  5. Data Visualization and Storytelling

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 Scientist Skills
  1. Solid understanding of Data Science fundamentals, programming fundamentals & statistical concepts
  2. Advanced machine learning techniques and big data technologies for handling large-scale data.
  3. Ability to develop and deploy machine learning models into production environments.
  4. Competency in data engineering principles, data pipeline development, and ETL processes for building robust data infrastructure.
  5. Strong understanding of fundamental statistical concepts and machine learning algorithms for predictive modeling.
Core Data Scientist Skills
  1. Solid understanding of Data Science fundamentals, programming fundamentals & statistical concepts
  2. Advanced machine learning techniques and big data technologies for handling large-scale data.
  3. Ability to develop and deploy machine learning models into production environments.
  4. Competency in data engineering principles, data pipeline development, and ETL processes for building robust data infrastructure.
  5. Strong understanding of fundamental statistical concepts and machine learning algorithms for predictive modeling.
Curriculum Designed For
Banking & Finance
Module 1: Introduction to Data Science
3 Lectures

Introduction to Data Science provides a foundational understanding of the principles and techniques essential for extracting insights from data. Mastering the fundamentals of data science, individuals gain the ability to address complex challenges, identify opportunities, and drive innovation in the real world.

  • 1.1 What is Data Science?:
    • Explore the interdisciplinary field of data science and its role in extracting insights from data.
  • 1.2 Applications of Data Science:
    • Discover real-world applications across various industries, from healthcare to finance, where data science drives innovation and decision-making.
  • 1.3 Data Science Lifecycle:
    • Understand the systematic process of collecting, preparing, analysing, and interpreting data to extract valuable insights.
Module 2: Programming Fundamentals
4 Lectures

Programming Fundamentals offers a comprehensive introduction to the core concepts and techniques of programming essential for data science and analysis. It covers fundamental programming constructs such as variables, data types, control structures, functions, and modules, providing a solid foundation for data manipulation and analysis. Mastery of programming fundamentals enables individuals to write efficient and scalable code, automate repetitive tasks, and develop robust data analysis solutions, making it a crucial skill set for aspiring data scientists and analysts.

  • 1.1 Introduction to Python:
    • Master the fundamentals of Python programming language, a versatile tool for data manipulation and analysis.
  • 1.2 Data Types and Variables:
    • Learn about different data types and how variables are used to store and manipulate data.
  • 1.3 Control Structures (if, else, loops):
    • Understand control structures for directing program flow and making decisions based on conditions.
  • 1.4 Functions and Modules:
    • Explore the concept of functions and modules for organising code into reusable components.
Module 3: Data Manipulation and Analysis
4 Lectures

Data Manipulation and Analysis is a critical aspect of data science, focusing on transforming raw data into meaningful insights. This module equips participants with the skills to clean, preprocess, and manipulate data using tools like Pandas in Python. By mastering data manipulation techniques, individuals can effectively handle complex datasets, extract relevant information, and prepare data for further analysis, enabling them to derive actionable insights and make informed decisions in various domains such as finance, healthcare, marketing, and more.

  • 1.1 Working with DataFrames (Pandas):
  • Dive into Pandas, a powerful library for data manipulation and analysis, and learn to work with tabular data effectively.
  • 1.2 Data Cleaning and Preprocessing:
  • Explore techniques for cleaning and preprocessing raw data to ensure its quality and reliability.
  • 1.3 Data Aggregation and Grouping: Learn how to aggregate and group data to derive meaningful insights and summaries
  • 1.4 Data Visualization: Discover the importance of data visualisation and learn to create visualisations using Matplotlib and Seaborn.
Module 4: Exploratory Data Analysis (EDA) and Statistics
5 Lectures

Exploratory Data Analysis (EDA) and Statistics play a pivotal role in the data science workflow, providing crucial insights into the underlying patterns, relationships, and distributions within datasets. This module delves into EDA techniques such as data visualisation, summary statistics, and hypothesis testing, enabling participants to gain a deep understanding of their data and uncover valuable insights. By mastering EDA and statistics, individuals can effectively identify trends, outliers, and correlations, facilitating informed decision-making and driving impactful solutions across diverse domains including finance, healthcare, retail, and beyond.

  • 1.1 Introduction to Data Visualization Libraries:
    • Explore Matplotlib and Seaborn libraries for creating various types of plots such as line plots, scatter plots, histograms, etc.
  • 1.2 Plotting Techniques:
    • Learn advanced plotting techniques and how to customise plots to convey insights effectively
  • 1.3 Exploratory Data Analysis (EDA):
    • Dive into EDA techniques to uncover patterns, anomalies, and relationships in data.
  • 1.4 Descriptive Statistics:
    • Understand descriptive statistics to summarise and describe the main features of a dataset.
  • 1.5 Statistical Concepts:
    • Explore statistical concepts such as probability theory, statistical distributions, and hypothesis testing.
Module 5: Machine Learning Basics
5 Lectures

Machine Learning Basics is a fundamental module that introduces participants to the core concepts, algorithms, and applications of machine learning. Through this module, learners gain a solid understanding of supervised and unsupervised learning, model evaluation metrics, and common machine learning algorithms such as linear regression, logistic regression, decision trees, and support vector machines (SVM). Proficiency in machine learning basics equips individuals with the essential skills to build predictive models, classify data, and uncover patterns from datasets, thereby enabling data-driven decision-making and problem-solving in various domains including finance, healthcare, marketing, and more.

  • 1.1 Introduction to Machine Learning:
    • Understand the fundamentals of machine learning and its applications in predictive modelling and pattern recognition.
  • 1.2 Supervised vs. Unsupervised Learning:
    • Learn the difference between supervised and unsupervised learning techniques and their use cases.
  • 1.3 Model Evaluation Metrics:
    • Explore metrics for evaluating the performance of machine learning models.
  • 1.4 Supervised Learning Algorithms:
    • Dive into popular supervised learning algorithms such as Linear Regression, Logistic Regression, Decision Trees, Random Forests, and Support Vector Machines (SVM).
  • 1.5 Unsupervised Learning Algorithms:
    • Explore unsupervised learning algorithms like K-Means Clustering and Hierarchical Clustering for grouping similar data points and discovering hidden patterns.
Module 6: Advanced Topics
4 Lectures

Advanced Topics delves into cutting-edge concepts and techniques in data science, exploring specialized areas beyond the basics. Participants are exposed to advanced machine learning algorithms like ensemble learning, neural networks, and deep learning, which enable them to tackle complex problems and achieve higher levels of model performance. Additionally, this module covers topics such as natural language processing (NLP), recommendation systems, anomaly detection, and reinforcement learning, providing learners with the expertise to address sophisticated challenges in areas like text analysis, personalized recommendations, anomaly detection in financial transactions, and autonomous decision-making systems. Mastery of advanced topics empowers data scientists to push the boundaries of innovation and make significant contributions to industries ranging from e-commerce to healthcare and beyond.

  • 1.1 Advanced Machine Learning:
    • Delve into advanced machine learning techniques such as ensemble learning, neural networks, and deep learning for solving complex problems.
  • 1.2 Natural Language Processing (NLP):
    • Explore techniques for processing and analyzing text data, including sentiment analysis, text classification, and language translation.
  • 1.3 Recommendation Systems:
    • Learn how recommendation systems use machine learning to provide personalised recommendations to users.
  • 1.4 Model Deployment and Production:
    • Understand the process of deploying machine learning models into production environments and strategies for model deployment.
Module 7: Big Data and Distributed Computing
3 Lectures

Big Data and Distributed Computing introduces participants to the fundamental concepts and technologies essential for handling large-scale datasets and performing complex computations across distributed systems. In this module, learners gain an understanding of big data technologies like Hadoop and Spark, which enable the storage, processing, and analysis of massive volumes of data across clusters of computers. They also explore distributed data processing techniques, scalable machine learning algorithms, and data engineering principles, equipping them with the skills to design and implement robust data infrastructure solutions capable of handling the volume, velocity, and variety of big data. Proficiency in big data and distributed computing empowers data professionals to extract actionable insights from massive datasets efficiently, enabling organisations to make data-driven decisions and drive innovation at scale.

  • 1.1 Introduction to Big Data Technologies:
    • Explore big data technologies such as Hadoop and Spark for processing and analysing large volumes of data
  • 1.2 Distributed Data Processing:
    • Learn how distributed computing frameworks enable parallel processing of big data across multiple nodes.
  • 1.3 Scalable Machine Learning Algorithms:
    • Explore scalable machine learning algorithms designed to handle large datasets efficiently.
Module 8: Data Engineering and Business Skills
5 Lectures

Data Engineering equips participants with the knowledge and skills required to design, develop, and maintain robust data infrastructure and pipelines, ensuring efficient data processing and management. Learners delve into data pipeline development, ETL processes, data warehousing concepts, and advanced data engineering techniques, enabling them to build scalable and reliable data systems that support various data-driven applications and analytics. Additionally, the Business Skills component focuses on enhancing participants' communication, collaboration, and problem-solving abilities, preparing them to effectively communicate data insights, drive informed decision-making, and deliver measurable value to stakeholders across the organization. Proficiency in data engineering and business skills enables professionals to bridge the gap between technical analysis and business objectives, driving organizational success through data-driven strategies and initiatives.

  • 1.1 Data Engineering:
    • Gain an understanding of data engineering principles, data pipeline development, and ETL processes for building robust data infrastructure.
  • 1.2 Business and Communication Skills:
    • Enhance your business acumen and communication skills to effectively communicate data insights and drive decision-making processes.
  • 1.3 Effective Communication of Data Insight:
    • Learn to effectively communicate insights derived from data to stakeholders using storytelling techniques.
  • 1.4Business Acumen and Decision-Making:
    • Understand the role of data science in driving business decisions and developing data-driven strategies for organizational success.
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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₹ 50,000

Curriculum & Course Materials

Live coding environment

AI-based learning platform

100+ hours of instruction

20+ assignments

10+ banking & finance case studies

Banking & finance domain focused curriculum

Capstone projects

Live Classes

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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.