Full Stack Data Science Career Path

Become a fully fledged data scientist with our Data Science Course For Banking & Finance that takes you from beginner to job-ready in just 4 months through industry-oriented learning.
20 Videos
No Coding Experience Required
45 Assignments
Self Paced

About This Course

Dive into the powerful world of data manipulation with Python libraries for data science like NumPy and Pandas. Explore how to efficiently work with multidimensional arrays, perform data wrangling operations, and analyze large datasets. These skills are crucial for anyone aspiring to become a data scientist, data analyst, or machine learning engineer.
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.
Curriculum Designed For Career Success
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.
Case Studies

Industry Case Studies You Will 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
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Make A Life-Changing Career Choice

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.
BIGGEST GROWING INDUSTRY
$349.6 Billion
Amount industry is set to grow by 2030
HIGH ENTRY- LEVEL SALARY
₹8-14 LPA+
Current average CTC for entry-level Data Analysts in India.
Don't Just Learn. Specialize.
India's only course with industry specialization in the domain of your choice.
50+
Industry case studies
10+
Problem solving frameworks
Experience 360° deep specialized learning
50+
Assignments
10+
Industry Projects
100+
Hours of Learning
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

Land Your Dream Job With
Full Placement Support

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Craft a Winning Resume

Get expert help building a resume that showcases your data skills.
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Nail Your Interview

Practise mock interviews with our experienced mentors to ace the recruitment process. 
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Company Screening & Selection

Benefit from our extensive industry network and connections to unlock exciting career opportunities.
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The team was thrilled with the quality of instruction provided. We have requests from teams from other departments to undertake the training as well. 
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Avinash Purohit
DGM, Canara Bank
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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!

What makes us different

Youtube Tutorials& Courses
Live classes
No learner support
No access to any mentor
No live classes
No accountability
No time commitment
SkillCamper
16 weeks course
1:1 Mentorship
Access to industry experts
Live classes with experts
Dedicated academic counselors to ensure you complete course requirements
15-20 hours of time commitment per week - designed for working professionals
Other Bootcamps &Degree Programs
20-60 weeks course
1:1 support may or may not be available
Access to industry may or may not be available
Live classes only
Accountability through assignments and grading
Full-time commitment - not made for working professionals
Online CertificationProgram
3-4 weeks course
No learner support
No access to industry experts
No live classes
Limited accountability
8-10 hours per week of time commitment- suited to working professionals

“The mentors at SkillCamper teach very well, making all the concepts easy to understand. ”

Their teaching style is clear and effective, and I am grateful for their guidance. I hope they continue this excellent approach in the future.
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Ravi Prakash
Automation Test Analyst

“SkillCamper's Data Analytics Bootcamp is fantastic. ”

I had tried learning some data analytics tools through free platforms, but it wasn't enough to get a good opportunity. SkillCamper goes beyond just teaching tools; they focus on domain expertise, which is essential today. The course material is very practical, and I feel like I'm gaining valuable skills. Highly recommended!
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Sanjay Shah
Graduate in BSC-IT

“I'm doing the Data Analytics Bootcamp at SkillCamper, and it's great. ”

Even though I don't have a tech background, the mentors explain things in a simple way that I can understand. The projects and the friendly community make learning fun and helpful. I highly recommend SkillCamper for anyone new to data analytics!
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Ashish Lodhe
Graduate in BSC-IT

“The course is going well and the mentors are very supportive. ”

As a student from a non-tech background, I find their teaching style easy to follow. They explain everything in simple terms and help with any questions. I highly recommend SkillCamper for anyone starting from scratch! 
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Suman M-
Graduate in BSC-IT
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Testimonials

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. 
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Live Interaction

Self paced

Fee Structure

₹ 75,000

₹ 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

Flexible study options

Cancel anytime in first 7 days, full refund

Mentors

15+ hours of sessions with industry veterans & experts

Personalized mentorship by course instructors

Unlimited 1:1 doubt solving sessions

Career Support

Personalized placement assistance

1:1 mock interviews with industry experts

Soft-skills training module

Essential digital tools for digital workplace module

Interview preparation module

Masterclass on resume building & LinkedIn

Access to curated companies & jobs

Live Interaction

Self paced

Fee Structure

$599

$299

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

Flexible study options

Cancel anytime in first 7 days, full refund

Mentors

15+ hours of sessions with industry veterans & experts

Personalized mentorship by course instructors

Unlimited 1:1 doubt solving sessions

Career Support

Personalized placement assistance

1:1 mock interviews with industry experts

Soft-skills training module

Essential digital tools for digital workplace module

Interview preparation module

Masterclass on resume building & LinkedIn

Access to curated companies & jobs

Frequently Asked Questions

What skills will I learn in the Full Stack Data Science Bootcamp?
You’ll gain hands-on experience in data science tools like Python, SQL, and Tableau, along with key libraries such as Pandas, NumPy, and scikit-learn. You’ll also learn about machine learning models, data visualisation, and industry-specific applications such as fraud detection and credit risk analysis.
Do I need any prior experience in data science to join this course?
No, this course is designed for beginners. You don’t need prior experience in data science or programming. We start with the basics and progress to more advanced topics, making it suitable for anyone looking to start a career in data science, especially within banking and finance.
How long does the course take, and can I study at my own pace?
The bootcamp runs for 4 months. You can either attend live instructor-led classes or follow the course in a self-paced format. This flexibility allows you to manage your time and schedule around work or other commitments.
What will I be able to do after completing the bootcamp?
By the end of this course, you’ll be able to build and deploy data-driven solutions for financial services. You’ll understand how to apply data science techniques to real-world problems, such as predictive modelling, customer segmentation, and credit risk assessment, all with a strong focus on banking and finance.
Will I work on real-world projects?
Yes, you will complete industry-focused projects that reflect real challenges faced in the banking sector. Projects like fraud detection, loan default prediction, and customer segmentation will help you build a portfolio that showcases your skills to potential employers.
How does this course help with job placement?
We provide comprehensive career support, including 1:1 mentorship, mock interviews, resume reviews, and guidance on creating a strong portfolio. Our dedicated team will help you prepare for job interviews, and you’ll gain access to our network of partner companies in the banking and finance sector.
What payment options are available? Is there a refund policy?
We offer flexible payment plans, including EMI options. If you’re not satisfied, we have a 7-day, no-questions-asked refund policy, allowing you to try the course risk-free.
Will I have access to the course materials after completion?
Yes, you will have lifetime access to all course materials, including video lessons, assignments, and projects, so you can continue learning at your own pace and revisit any content as needed.
How does this bootcamp help me start a career in data science?
This bootcamp is designed to give you the technical skills and practical experience needed to start a career in data science. With real-world projects, mentorship, and career support, you’ll be well-prepared to land a role as a data scientist in the banking and finance sector.
What are the requirements for taking this course?
While beginners are welcome, familiarity with basic statistics and programming is helpful to grasp the data science concepts covered.
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.

Ready to become a Data Scientist that industry loves to hire? Apply Now. 

Explore Courses
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What technologies will I learn in the Data Science bootcamps?
You will learn Python, R, SQL, Power BI, Tableau, Excel, Pandas, Numpy, Matplotlib, Seaborn, and PySpark.