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DataPulse

The DataPulse Program is a comprehensive 20-month learning journey designed specifically for engineering graduates and tech enthusiasts still pursuing their studies. It aims to equip students with the essential skills and knowledge needed to excel in the world of data science, business analytics, and advanced data science, all while preparing them for real-world job opportunities before they even graduate.

Mode of Learning: Offline

Starting Date: Sep 03 , 2024

Duration: 20 Months

Language: English

Course Type: Paid

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What you'll learn

DataPulse Program – Empowering Future Data Leaders

Welcome to DataPulse, a premier 20-month program designed for engineering graduates and tech enthusiasts pursuing their studies. Our mission is to prepare you for a thriving career in data science, business analytics, and advanced data science, all while you're still in college.

Program Overview

DataPulse is a comprehensive learning journey that equips you with the skills and knowledge to thrive in the data-driven world. Whether you're passionate about solving real-world business problems, diving deep into data analytics, or preparing for a future in advanced data science, this program has it all.

Key Features

Comprehensive Curriculum: Master the entire spectrum of data science, from foundational business analytics to advanced data science techniques.
Hands-on Learning: Engage with real-world case studies, projects, and practical applications, ensuring you're ready for industry challenges.
Aptitude and Problem-Solving Training: Alongside technical skills, sharpen your analytical thinking and aptitude to become a versatile problem solver.
Job-Readiness Focus: Tailored to make you industry-ready by the time you graduate, providing a seamless transition from learning to employment.
Mentorship and Support: Gain insights from industry experts and mentors who guide you through projects and skill development.

Why DataPulse?

Future-Proof Your Career: Stand out in the competitive job market with advanced skills in data science and business analytics.
Learn While You Study: Designed to fit into your academic life, so you can master data science without compromising your college studies.
Holistic Development: From technical expertise to aptitude enhancement, we ensure you're not just a data scientist, but a well-rounded professional.

Join the DataPulse Program and become a leader in the data revolution, fully prepared for the opportunities of tomorrow.

Course Content

Overview: Semester 1: Foundations of Data Science Engineering is designed to provide a strong base in essential programming and database management skills, crucial for advanced data science learning. This semester focuses on Python for Data Structures and Algorithms (DSA) and SQL for Data Querying and Database Management, preparing you for more complex topics in subsequent semesters.

Key Learning Areas:

Python for Data Structures and Algorithms (DSA):
- Foundational Knowledge: Understand fundamental data structures and algorithms, their importance in computer science and data science, and basic problem-solving techniques using Python.
- Python Programming: Refresh your knowledge on Python basics, including data types, control flow, functions, and modules.
- Data Structures: Learn about lists, stacks, queues, and linked lists, including their operations and implementations. Explore trees, heaps, and hash tables, along with their applications and operations.
- Algorithms: Study sorting and searching algorithms, recursion, backtracking, and dynamic programming. Gain hands-on experience solving algorithmic problems and applying data structures in real-world scenarios.
- Capstone Project: Implement a comprehensive project incorporating multiple data structures and algorithms, culminating in a practical assessment through problem-solving exercises and project presentation.
SQL for Data Querying and Database Management:
- SQL Fundamentals: Learn SQL syntax for basic operations such as querying, filtering, and aggregating data. Understand data manipulation, constraints, and basic database design concepts.
- Advanced SQL Techniques: Explore subqueries, common table expressions, window functions, and advanced data modeling. Develop skills in query optimization and database management, including indexing, transactions, and concurrency control.
- Specialized Topics: Gain insights into data warehousing, business intelligence, and big data SQL extensions. Understand the integration of SQL with NoSQL databases and other big data tools.
- Capstone Project: Design and implement a database solution, including data warehousing and business intelligence components. Present your findings and insights from the project.

Objectives:

Develop proficiency in Python programming and data structures, laying a solid foundation for complex data science tasks.
Gain comprehensive skills in SQL for effective data querying and management.
Apply programming and database concepts to practical scenarios and real-world problems.
Prepare for more advanced topics in data science and engineering, setting the stage for specialization and complex data analysis.

Semester 1 is designed to ensure that by the end of this semester, you have a strong grasp of foundational programming and database skills, essential for tackling more advanced data science and engineering challenges in future semesters. This foundational expertise will prepare you for roles such as junior data analyst, database administrator, or software developer, and set you up for success in the subsequent semesters focused on more advanced data science techniques.

Semester 2:

Advanced Data Analytics and Business Intelligence focuses on elevating your data science skills by integrating advanced statistical analysis, data visualization, and business intelligence techniques. This semester is designed to build on the foundational knowledge you acquired in Semester 1, enhancing your ability to perform complex data analysis and make data-driven decisions in real-world business environments.

Key Learning Areas:

Statistics from Basic to Expert Level:
- Deepen your understanding of statistical concepts, from foundational principles to advanced techniques. You will explore probability distributions, hypothesis testing, regression analysis, and more, gaining the skills needed to conduct rigorous data analysis.
Excel for Data Analysis (Basic to Advanced):
- Master Excel as a powerful tool for data management and analysis. Learn advanced Excel functions, data manipulation techniques, and automation through macros and VBA to efficiently handle large datasets.
POWER BI for Data Visualization and Business Intelligence (Basic to Advanced):
- Develop expertise in Power BI to create dynamic, interactive dashboards and visualizations. Learn how to transform data into actionable insights, supporting strategic decision-making processes in business contexts.
Hands-on Business Analytics & Product Analytics:
- Apply your analytical skills to real-world business scenarios. Engage in case studies and projects that simulate industry challenges, enabling you to practice solving problems and making data-driven decisions that impact business outcomes.

Objectives:

Equip you with advanced analytical and technical skills necessary to thrive in data-driven roles.
Enhance your ability to visualize and communicate data insights through powerful tools like Power BI.
Provide practical, hands-on experience in applying data science concepts to solve real business problems.
Prepare you for a range of career opportunities in data analytics, business intelligence, and related fields.

Semester 2 is designed to ensure that by the end of the program, you are not only proficient in advanced data analysis techniques but also capable of using these skills to influence and drive business strategies, making you a valuable asset in any organization.

Industrial-Level Case Study: Business Analytics & Product Analytics

Scenario Overview
- Analyze and optimize product offerings and business strategies for a retail company.
Tasks:
- Data Exploration and Descriptive Analysis
  - Analyze historical sales data for trends and seasonality.
  - Use descriptive statistics and visualizations to understand product performance and customer behavior.
- Hypothesis Testing and A/B Testing
  - Formulate and validate hypotheses about customer preferences and product features.
- Predictive Modeling and Forecasting
  - Build regression models to predict sales and forecast demand and inventory.
- Dashboard Creation and Reporting
  - Develop interactive dashboards to monitor KPIs using Excel.
- Automation and Efficiency
  - Automate tasks using Excel macros and VBA scripts.

Introduction: Welcome to the capstone project of your internship. This two-month period is designed to apply your analytical skills to real-world business and product challenges. You will work on industry-specific projects that require a combination of statistical analysis, advanced data analysis using Excel, SQL for data querying and management, and POWER BI for business intelligence and data visualization.

Project Setup and Foundation:

Introduction and Project Planning:
- Kick-off Meeting: Define project goals, deliverables, and timelines.
- Project Scope: Select specific industry projects (Retail, Consumer, Banking, or Supply Chain).
- Data Collection: Gather and review relevant datasets.
Foundation in Statistics and Data Analysis:
- Statistical Analysis: Perform exploratory data analysis (EDA) to understand data characteristics and initial insights.
- Setting Up Workbooks: Create initial Excel workbooks for data processing.

Advanced Data Analysis with Excel:

Data Exploration and Visualization:
- Data Cleaning and Preparation: Use advanced Excel functions and tools to clean and prepare data.
- Building Dashboards: Develop interactive dashboards to visualize key metrics and trends.
Industry-Specific Analysis:
- Retail Example: Create a Sales Performance Dashboard and Store Profitability Analysis.
- Consumer Example: Predict Customer Lifetime Value and analyze churn.

SQL for Data Management and Querying:

Database Management and Data Integration:
- SQL Setup: Create and manage SQL databases and perform data integration tasks.
- Data Queries: Write complex SQL queries to extract, filter, and analyze data.
ETL Processes:
- Data Pipeline: Develop and implement ETL processes to streamline data integration and analysis.
Industry-Specific SQL Work:
- Retail Example: Analyze customer behavior through an e-commerce platform database.
- Banking Example: Develop a customer data warehouse for personalized banking services.

Business Intelligence with POWER BI:

Advanced Data Modeling and Visualization:
- Creating Reports: Use POWER BI to create dynamic reports and dashboards.
- Advanced Analytics: Implement advanced analytics features like forecasting and what-if analysis.
Industry-Specific BI Work:
- Retail Example: Develop a Dynamic Pricing Dashboard and analyze promotional effectiveness.
- Supply Chain Example: Create Supplier Performance Scorecards and enhance supply chain visibility.

Final Presentation and Wrap-Up:

Capstone Project Presentation:
- Compile Findings: Integrate insights and analysis from all phases.
- Present to Stakeholders: Prepare and deliver a presentation to senior management, showcasing insights, recommendations, and strategic decisions.
Feedback and Reflection:
- Review Session: Discuss feedback from mentors and stakeholders.
- Reflection: Reflect on the learning experience and key takeaways from the project.

Conclusion:

Congratulations on completing your two-month internship capstone project! You've demonstrated your ability to tackle complex business and product analytics challenges, applying your skills in statistics, Excel, SQL, and POWER BI. This experience prepares you for impactful roles in data-driven enterprises, equipped with hands-on expertise in business and product analytics.

Semester 3: Comprehensive Machine Learning, Deep Learning, and MLOps focuses on advancing your technical expertise in machine learning and deep learning while integrating the principles of MLOps. This semester builds on the knowledge and skills developed in Semester 2, equipping you with advanced techniques and practical experience necessary for tackling complex data science challenges and deploying scalable machine learning solutions.

Key Learning Areas:

Introduction to Machine Learning:
- Foundational Knowledge: Expand your understanding of machine learning fundamentals, including data preprocessing, exploratory data analysis (EDA), and feature engineering. Learn to build and evaluate machine learning models using a variety of algorithms.
- Supervised Learning Algorithms: Master techniques for regression and classification, including linear regression, logistic regression, support vector machines (SVM), and ensemble methods such as random forests and gradient boosting.
Unsupervised Learning Algorithms and Recommender Systems:
- Clustering and Dimensionality Reduction: Delve into unsupervised learning methods such as k-means, hierarchical clustering, and principal component analysis (PCA) to uncover hidden patterns in data.
- Recommender Systems: Learn to build recommendation engines using collaborative filtering and content-based filtering techniques, and analyze time series data for forecasting.
Optimization Algorithms:
- Gradient Descent Variants: Understand optimization techniques crucial for training machine learning models, including stochastic gradient descent (SGD) and advanced methods like Adam and RMSprop.
- Advanced Optimization Techniques: Explore additional methods such as evolutionary algorithms and simulated annealing to refine model performance.
Deep Learning Algorithms:
- Neural Networks and Architectures: Gain expertise in designing and training deep learning models, including feedforward neural networks (FNN), convolutional neural networks (CNN), and recurrent neural networks (RNN).
- Specialization: Choose between specialization in computer vision or natural language processing (NLP), learning advanced techniques and applications relevant to each field.
MLOps:
- Operationalizing ML Models: Learn to manage the machine learning lifecycle, including continuous integration and deployment (CI/CD), model versioning, and monitoring.
- Scalability and Security: Understand best practices for scaling machine learning infrastructure and ensuring security, as well as tools and techniques for automating and optimizing ML workflows.

Objectives:

Advanced Technical Proficiency: Develop advanced skills in machine learning and deep learning, with a focus on applying these techniques to complex data problems and real-world scenarios.
Specialization: Gain in-depth knowledge in either computer vision or NLP, tailoring your expertise to meet industry demands and career interests.
Operational Excellence: Learn the principles and practices of MLOps to manage, deploy, and maintain machine learning models effectively in production environments.
Practical Experience: Engage in hands-on projects and case studies to apply your skills to industry-specific challenges, preparing you for impactful roles in data science and machine learning.

Semester 3 aims to enhance your capabilities in advanced data science techniques and operational practices, ensuring you are well-prepared to tackle sophisticated problems, lead machine learning initiatives, and contribute to data-driven decision-making processes in various industries.

Month 1: Advanced Machine Learning Projects

Project Initialization and Advanced ML Techniques:
- Project Kick-off:
  - Define project goals and objectives with the intern.
  - Review existing datasets and identify requirements.
- Advanced ML Techniques:
  - Implement supervised and unsupervised learning algorithms on real-world datasets.
  - Apply advanced techniques such as ensemble methods (e.g., Random Forests, Gradient Boosting).
  - Focus on feature engineering, hyperparameter tuning, and model evaluation.
Specialized ML Techniques:
- Deep Learning Projects:
  - Build and train deep learning models using TensorFlow or PyTorch.
  - Work on specific tasks such as image classification, text classification, or object detection.
- ML Experimentation:
  - Use MLflow or other experiment tracking tools to manage experiments and results.
  - Compare model performance and iterate based on evaluation metrics.
- Deliverables:
  - Develop and submit a comprehensive report on the machine learning models implemented, including performance metrics and findings.
  - Present the results to the team, highlighting key insights and recommendations.

Month 2: MLOps Integration and Deployment

MLOps Fundamentals:
- Introduction to MLOps:
  - Understand the MLOps lifecycle and its importance.
  - Set up ML environments using Docker and virtual environments.
- CI/CD for ML:
  - Design and implement a CI/CD pipeline for ML workflows.
  - Integrate tools like Jenkins or GitHub Actions for automated testing and deployment.
  - Automate model versioning and experiment tracking.
Model Deployment and Monitoring:
- Model Deployment:
  - Deploy ML models using TensorFlow Serving, FastAPI, or similar tools.
  - Explore different deployment strategies (batch, real-time, on-device).
- Monitoring and Maintenance:
  - Set up monitoring tools to track model performance and detect drift.
  - Implement automated retraining pipelines based on performance metrics.
- Deliverables:
  - Document the MLOps setup, including environment configurations, CI/CD pipelines, and deployment strategies.
  - Provide a demonstration of the deployed models and monitoring setup to stakeholders.

Month 3: Capstone Project and Final Evaluation

Capstone Project Initiation:
- Project Scope:
  - Define the scope of the capstone project, integrating ML and MLOps.
  - Identify the problem statement, project goals, and deliverables.
- Data Collection and Preparation:
  - Collect and preprocess data for the capstone project.
  - Implement ML models and apply MLOps practices to manage the lifecycle.
Capstone Project Execution and Presentation:
- Project Execution:
  - Complete the implementation of ML models and MLOps workflows.
  - Conduct final testing and validation.
- Final Presentation:
  - Prepare a comprehensive report detailing the project’s methodology, results, and impact.
  - Present findings to the team, including insights, challenges, and future recommendations.
- Deliverables:
  - Submit a detailed report on the capstone project, including all aspects of ML and MLOps integration.
  - Deliver a final presentation showcasing the project’s results, including any learned lessons and potential improvements.

Key Points:

Weekly Check-ins: Schedule regular meetings to discuss progress, address challenges, and provide feedback.
Mentorship: Assign mentors to guide the intern through complex tasks and provide support.
Learning Resources: Provide access to learning resources and tools necessary for successful project completion.
Feedback Loop: Ensure continuous feedback is provided to help the intern grow and improve their skills.

Overview:

The final semester focuses on advanced topics in Generative AI and Prompt Engineering. This comprehensive module will deepen your knowledge and skills in creating and fine-tuning generative models and optimizing prompt strategies for natural language processing. You will apply these techniques to practical projects, preparing you for advanced roles in AI research and development.

Key Learning Areas:

Generative AI

Module 1: Introduction to Generative AI
- Overview of Generative AI:
  - History and evolution of generative models.
  - Types of generative models: GANs, VAEs, and Flow-based Models.
  - Applications across various domains.
- Foundations:
  - Generative Adversarial Networks (GANs): Basic principles, architecture, and training.
  - Variational Autoencoders (VAEs): Basics and variations.
  - Flow-based Models: Introduction to RealNVP and Glow.
Module 2: Advanced Generative Models
- Deep Dive into GANs:
  - Architecture: Generator, Discriminator, and loss functions.
  - GAN Variants: DCGANs, CycleGANs, and StyleGANs.
  - Training challenges and stabilization techniques.
- Variational Autoencoders (VAEs):
  - VAE Architecture: Encoder, Decoder, and Latent Space.
  - Variants: Conditional VAEs, Beta-VAEs, and their applications.
- Flow-based Models:
  - Principles behind normalizing flows.
  - RealNVP and Glow: Implementation and applications.
Module 3: Natural Language Generation
- Introduction to NLP Generative Models:
  - Overview of language models and sequence-to-sequence models.
  - Transformers: Architecture, attention mechanisms.
- Pre-trained Models:
  - BERT, GPT, T5: Overview, fine-tuning, and transfer learning.
Module 4: Computer Vision and Image Generation
- Image Generation with GANs:
  - Techniques and architectures for high-quality image generation.
  - Style transfer and super-resolution.
  - Advanced topics: Object Detection (YOLO, SSD) and Image Segmentation (U-Net, Mask R-CNN).
Module 5: Music and Audio Generation
- Generative Models for Audio:
  - Introduction and implementation of WaveNet and WaveRNN.
  - Music generation techniques using RNNs and Transformers.
  - Real-world applications in music and audio synthesis.

Deliverables:

Project Report: Summarize findings, methodologies, and results from each module.
Presentation: Prepare and deliver a presentation on a Generative AI project.

Prompt Engineering

Module 6: Introduction to Prompt Engineering
- Overview of Prompt Engineering:
  - Definition, importance, and types of prompts.
- Prompt Design:
  - Effective prompting techniques and optimization strategies.
Module 7: Fundamentals of NLP and Prompting
- Basics of Natural Language Processing (NLP):
  - Text preprocessing and representation techniques.
- Prompt Engineering with Pre-trained Models:
  - Crafting prompts for GPT and BERT.
  - Prompt tuning techniques.
Module 8: Advanced Prompt Engineering Techniques
- Complex Prompting Strategies:
  - Conditional and few-shot prompting.
  - Interactive prompting and iterative refinement.
- Use Cases and Applications:
  - Practical applications in Q&A, content generation, and more.
Module 9: Ethics and Considerations in Prompt Engineering
- Ethical Implications:
  - Addressing biases and ensuring responsible use.
- Best Practices:
  - Techniques for effective and ethical prompting.
Module 10: Capstone Project in Generative AI and Prompt Engineering
- Project Initiation:
  - Define project scope, objectives, and datasets.
  - Plan and outline methodologies.
- Project Execution:
  - Implement generative models and prompt engineering techniques.
  - Evaluate, refine, and optimize project outcomes.
- Final Presentation:
  - Develop a comprehensive report and deliver a formal presentation showcasing project findings and insights.

Objectives:

Equip students with advanced skills in Generative AI and Prompt Engineering.
Develop the ability to create, fine-tune, and deploy generative models and prompts.
Provide practical experience through real-world projects and case studies.
Prepare students for specialized roles in AI research, development, and application.

Program Key Benefits:

Advanced Expertise: Gain deep knowledge and hands-on experience with cutting-edge generative models and prompt engineering techniques.
Practical Skills: Apply theoretical knowledge to real-world projects, enhancing practical skills and problem-solving abilities.
Career Readiness: Prepare for advanced roles in AI and data science, with skills applicable to various industries and research areas.
Comprehensive Learning: Integrate concepts from multiple domains, including computer vision, natural language processing, and audio generation, for a holistic understanding of AI technologies.

Advanced Capstone Project Overview
- Integration of skills and knowledge acquired throughout the program.
- Addressing complex problems that require a combination of various data science and AI techniques.
Project Scope Definition
- Defining a comprehensive project scope based on student interests and industry relevance.
- Ensuring the project involves significant challenges and opportunities for showcasing skills.
Execution and Presentation
- Developing the project through iterative phases, including research, implementation, and testing.
- Presenting the final project to peers, mentors, and industry experts, demonstrating proficiency in data science and AI.

Statistics and Data Analysis
- Project Topic: Exploratory Data Analysis (EDA) and Statistical Modeling of a Real-world Dataset
- Deliverables:
  - Comprehensive report on data insights
  - Visualization dashboards illustrating key findings
Machine Learning and Deep Learning
- Project Topic: Building and Optimizing a Machine Learning Model for Predictive Analytics
- Deliverables:
  - Detailed model performance metrics
  - Complete code implementation with documentation
SQL and Database Management
- Project Topic: Designing and Implementing a Database System for a Real-world Scenario
- Deliverables:
  - Entity-Relationship (ER) diagram
  - SQL queries and database implementation
Business Intelligence and Data Visualization
- Project Topic: Creating Interactive Dashboards Using Power BI or Tableau
- Deliverables:
  - Interactive dashboard
  - Presentation of data insights and business recommendations
MLOps
- Project Topic: Developing a CI/CD Pipeline for Machine Learning Model Deployment
- Deliverables:
  - CI/CD pipeline configuration
  - Deployment documentation and best practices
General Artificial Intelligence (Gen AI)
- Project Topic: Designing an AI System with Cognitive Abilities (e.g., Reasoning, Natural Language Understanding)
- Deliverables:
  - System architecture design
  - Demonstration of AI capabilities and performance
Generative AI
- Project Topic: Creating an Application Using Generative Models (e.g., Image Generation, Text-to-Speech)
- Deliverables:
  - Working prototype of the application
  - User guide and technical documentation
Prompt Engineering
- Project Topic: Implementing a Prompt-based NLP System for a Specific Application (e.g., Question Answering, Text Generation)
- Deliverables:
  - System documentation and setup
  - Performance evaluation and testing results

Retail Analytics
- Project Topic: Customer Segmentation and Predictive Modeling for Sales Forecasting
- Industry Problem: Enhancing customer targeting and personalization to improve sales and marketing strategies.
Healthcare Analytics
- Project Topic: Predictive Modeling for Disease Diagnosis Using Patient Data
- Industry Problem: Improving healthcare decision-making and patient care through accurate predictive analytics.
Financial Analytics
- Project Topic: Fraud Detection System Using Machine Learning Algorithms
- Industry Problem: Reducing financial risks and identifying fraudulent activities in banking and finance sectors.
Supply Chain Analytics
- Project Topic: Optimization of Supply Chain Operations Using Predictive Analytics
- Industry Problem: Enhancing supply chain efficiency and reducing operational costs.
Marketing Analytics
- Project Topic: Customer Lifetime Value Prediction and Churn Analysis
- Industry Problem: Optimizing marketing strategies and budget allocation to maximize ROI and reduce churn.
Social Media Analytics
- Project Topic: Sentiment Analysis and Topic Modeling on Social Media Data
- Industry Problem: Understanding customer sentiment and brand perception to guide marketing and product development.
Manufacturing Analytics
- Project Topic: Predictive Maintenance Using IoT Sensor Data
- Industry Problem: Minimizing downtime and improving production processes through predictive maintenance.
Environmental Analytics
- Project Topic: Predictive Modeling for Climate Change Impact Assessment
- Industry Problem: Supporting environmental sustainability initiatives through accurate climate impact predictions.

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led by experts. It equips students with flexible and high-quality teaching to
achieve academic excellence.

Enrollment Information:

How to Enroll: Click our website's 'Enroll Now' button and follow the registration process.
Payment Options: Full payment or flexible installment plans are available.
Contact Us: For more information or assistance, please contact our support team at [email protected]

Career Opportunities

Upon completing the program, participants can pursue roles such as:

Data Scientist
Machine Learning Engineer
AI Specialist
MLOps Engineer
Business Intelligence Analyst
Market Research Analyst
Product Analyst
Statistical Analyst
Business Intelligence (BI) Analyst

Meet the Mentor

Course Fees

DataPulse

₹ 35,400.00 /~~50,000.00~~

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Faq's

The DataPulse Program is a 20-month comprehensive learning course designed for engineering graduates and tech enthusiasts still pursuing their studies. It focuses on building expertise in business analytics, data science, and advanced data science, along with aptitude training, to prepare students for job opportunities before they graduate.

The program is open to:

Engineering students currently enrolled in college.
Tech enthusiasts who are passionate about data science and want to build a career in this field. No prior experience in data science is required, but basic knowledge of programming or mathematics is helpful.

The program covers a wide range of topics including:

Business Analytics: Introduction to analytics, data-driven decision making, and business intelligence.
Data Science: Fundamentals of data science, statistical analysis, machine learning, and data visualization.
Advanced Data Science: Deep learning, natural language processing, and advanced machine learning algorithms.
Aptitude Training: Problem-solving, logical reasoning, and analytical thinking.

The program spans 20 months, with a well-balanced blend of:

Theory and practical applications
Hands-on projects and case studies
Industry-relevant assignments This ensures that students not only learn concepts but also gain practical experience working with real-world data.

The DataPulse Program is designed to make students job-ready by the time they graduate from college. The curriculum, practical training, and industry-focused projects give students the skills and experience needed to transition smoothly into data science roles.

Aptitude training focuses on developing critical skills like logical reasoning, problem-solving, and analytical thinking—all essential for succeeding in data science roles and excelling in job interviews. These skills make you a well-rounded professional, capable of handling complex data challenges.

Yes, the program is designed to fit alongside your regular academic studies. With flexible learning schedules, students can balance their college coursework while participating in the program. The emphasis is on gradual, consistent learning to ensure you don't feel overwhelmed.

Students will work on various industry-focused projects that cover:

Data analysis and business intelligence
Machine learning model development
Predictive analytics
Advanced applications like deep learning and natural language processing These projects will help you apply theoretical knowledge to real-world data challenges.

Yes, throughout the program, you'll have access to industry experts and mentors who will guide you through your projects, provide career advice, and help you deepen your understanding of data science concepts.

The DataPulse Program equips you with both technical skills and the aptitude needed to thrive in the data science industry. By the time you finish the program, you’ll have a portfolio of real-world projects, hands-on experience, and job-ready skills, making you a strong candidate for data science roles.

You can apply directly through our website by filling out the application form. Once your application is reviewed, eligible candidates will be notified and given instructions on the next steps.

The total cost of the DataPulse Program is ₹35,400 INR, including GST. We offer flexible payment plans and scholarship options to support students. For more details, please visit our Pricing section or contact our support team.

Yes, upon successful completion of the program, you will receive a certification that validates your expertise in data science, business analytics, and advanced data science, making you a strong candidate for data-driven roles in the industry.

Absolutely! We encourage collaboration and networking. You will have opportunities to interact with peers, work on group projects, and participate in community events that foster learning and development.

What students say abouts us

Bharthi K

It's my pleasure to study Data Science at Master's Campus, which provides exceptional resources, supportive faculty, and career-boosting projects and internships

Karthik GS

MastersCampus Academy provides an exceptional learning experience. The courses are well-structured, offering both theoretical knowledge and practical insights. The faculty members are highly experienced, approachable, and dedicated to ensuring that students fully understand the material. I appreciated the personalized attention, which made the learning process much smoother

Lekhana US

I started the Data Science course at Masterscampus in my 5th semester, and now, in my 7th semester, I can confidently say that it has been one of the best decisions for my academic and professional growth. I've learned essential skills in Excel, SQL, Power BI, and Python, all taught by working professionals who bring real-world experience into the classroom. This hands-on approach has made the concepts much easier to grasp and apply. MastersCampus has truly helped me build a strong foundation for my future career

Vikash GN

I had an excellent experience with MastersCampus Academy. The instructors are highly knowledgeable and dedicated, offering personalized attention to ensure each student excels. The curriculum is well-structured and up-to-date, covering all the essential topics comprehensively. The facilities are modern and well-maintained, creating a conducive learning environment. The administrative staff is friendly and supportive, making the entire process smooth and efficient. Overall, I highly recommend MastersCampus Academy for anyone looking to advance their skills and knowledge

Janardhan M

My experience with the Data Science course at Masterscampus has been fantastic. The course provided a great learning experience that significantly upgraded my skills in Data Science, thanks to the industry professionals who teach it. A key highlight was the support I received for my mini-project on demand forecasting for a large retail sale. The hands-on guidance made a big difference in my understanding and application of the concepts. I highly recommend MastersCampus for anyone looking to advance their career in Data Science

Koushik P

I recently joined MastersCampus to enhance my skills in data science, specifically in Excel, SQL, Power BI, Python, predictive modeling, and statistics. The experience has been exceptional. The instructors are not only highly qualified but also bring practical industry experience, which enriches the learning process. A key highlight was completing my CRE Insights Pro project from start to finish. The hands-on guidance I received allowed me to apply theoretical concepts in a real-world context, significantly boosting my confidence and proficiency. I highly recommend MastersCampus for anyone looking to deepen their expertise in data science.

Suhas CS

I recently completed the data science course in Mastercampus, and I am thoroughly impressed with the overall experience. Here are some key highlights: Content Quality: The curriculum is comprehensive and well-structured, covering essential topics like statistics, machine learning, and data visualization. Each module builds on the previous one, making it easy to follow along. Instructors: The instructors and especially Sunil and Pavan are very knowledgeable and engaging. They provide real-world examples that enhance understanding and encourage participation through interactive sessions. Hands-On Projects: The course includes practical projects that allow you to apply what you’ve learned. These projects are invaluable for building a strong portfolio and gaining practical experience. Support and Resources: The course offers excellent support, including access to additional resources, forums for discussion, and prompt feedback on assignments. Networking Opportunities: Connecting with peers and industry professionals through group activities and discussions has been a great advantage, providing insights and potential collaboration and also provide number of career opportunities in top MNC’s. Overall, I highly recommend this data science course to anyone looking to deepen their understanding and skills in the field. It’s a worthwhile investment in your career

Rachana UR

MasterCampus Academy offers an exceptional learning experience that truly stands out. The curriculum is rigorous and well-structured, ensuring students to gain the skills needed for their future careers. The faculty is knowledgeable and approachable. This program given me confidence and skills I needed in the data science field. Overall, I highly recommend Master Campus Academy for anyone seeking a quality education in a supportive environment

Nisarga NM

I recently completed a data science course at MastersCampus while studying Medical Engineering. Eager to transition into data science, I found this program to be incredibly beneficial. The course offered a comprehensive curriculum that equipped me with essential skills in data analysis, machine learning, and predictive modeling. The instructors are experienced professionals who provided valuable insights and real-world applications, making complex concepts much easier to grasp. Thanks to this program, I now have a strong foundation in data science, which has significantly boosted my confidence and career prospects. I highly recommend MastersCampus for anyone looking to make a successful transition into data science.

Veeresh GN

I recently completed a data science course at MastersCampus, building on my two years of prior experience. This course has been transformative for me, particularly the individual project on demand forecasting for a leading tire manufacturing company. The personalized approach allowed me to apply advanced concepts in a real-world scenario, significantly enhancing my skills in Excel, SQL, Power BI, Python, and predictive modeling. The knowledgeable instructors provided invaluable insights, making complex topics accessible. This experience has been instrumental in helping me successfully switch to a career in data science. I highly recommend MastersCampus for anyone looking to advance their expertise in this field

Lavanya S

Incredible platform to learn data analytics, the entire program is well structured and, the trainer is amazing. They are approachable and clear every internal doubts. Great teaching with real time examples

Aishwarya V

I started the Data Science course in MastersCampus Academy .The faculty is highly knowledgeable and dedicated, providing personalized attention to each student. The curriculum is well-structured, combining theoretical knowledge with practical applications, which truly prepares students for their future careers. Overall, These programs are given me the confidence and skills I needed in Data Science filed. Highly recommended

Dixith Gowda MS

I'm currently pursuing the data science course at MastersCampus, and it has been a really positive experience. I joined because of my strong interest in data science, and this institute has lived up to my expectations. "MastersCampus is truly a standout institute for anyone interested in data science. The course content is well-crafted, ensuring a comprehensive learning experience that covers all key areas, from foundational concepts to advanced topics. The faculty are not only experts in their fields but also genuinely invested in helping students succeed. The modern facilities and well-maintained environment make learning both effective and enjoyable. It's a great place for freshers aiming to kickstart their careers or professionals looking to upskill. The supportive team and practical learning approach make MastersCampus a top choice for data science education

Vyshnavi ML

I recently completed a data science course at MastersCampus while pursuing my MSc in Molecular Biology. My goal was to integrate data science into molecular biology, and this platform has been instrumental in achieving that. The course provided a solid foundation in data science concepts, specifically tailored for applications in molecular biology. The instructors were knowledgeable and offered valuable insights into how data science can be effectively integrated into the field. I now have a mature understanding of skills like Excel, SQL, Power BI, and predictive modeling, which are crucial for my career. This experience has significantly enhanced my employability in roles that require expertise in both molecular biology and data science. I highly recommend MastersCampus to anyone looking to bridge these two important fields.

Kusumitha KS

I recently had the opportunity to interact with MastersCampus, I joined for data Analytics , the company bringing us very neet quality of learning, the company shows a lot of potential for growth and innovation

Chandhu M

I was struggling in my sales job when I heard about the data science course at MastersCampus. With faith in their placement support, I decided to leave my sales position and enroll in the program. The course has been a game-changer for me. I have acquired a solid understanding of data science concepts and developed strong technical skills in areas like Python, SQL, and machine learning. The instructors provided excellent guidance, and I received substantial support throughout the learning process. What’s more, MastersCampus has delivered on their promise regarding placement assistance. I’m now on my way to a successful career in data science, and I highly recommend this program to anyone looking to make a similar transition

Ankitha HR

I found it very interesting in there academic program..And there teaching skills is so understandable..i am happy to be a part to this program

Prajwal S

My experience with the Data Science course at Masters campus has been fantastic. The course provided a great learning experience that significantly upgraded my skills in Data Science, thanks to the industry professionals who teach it. A key highlight was the support I received for my mini-project on demand forecasting for a large retail sale. The hands-on guidance made a big difference in my understanding and application of the concepts. I highly recommend Masters campus for anyone looking to advance their career in Data Science

Likhith Gowda H

I started the Data Science course at MastersCampus in my 5th semester, and now, in my 7th semester, I can confidently say it has been a pivotal decision for my growth. I've acquired essential skills in Excel, SQL, Power BI, and Python, all taught by industry professionals. What stands out most is the hands-on approach with frequent hackathons and assignments that reinforce learning and apply knowledge in real-world scenarios. These experiences have not only clarified complex concepts but also greatly boosted my confidence. MastersCampus has provided an invaluable foundation for my future career

Sudeep KS

I recently completed a course in Data Analysis and Artificial Intelligence at MastersCampus Academy, and I must say it was an exceptional experience. The curriculum is comprehensive, covering everything from the basics of data handling to advanced AI techniques. What sets this academy apart is the balance between theory and practical application. The instructors are experts in their fields, bringing real-world experience into the classroom, which made the learning process both engaging and relevant. Their teaching methods are clear, and they always ensured that complex concepts were broken down into understandable segments. The hands-on projects were particularly valuable, allowing me to apply what I learned in real-world scenarios, which has significantly boosted my confidence. Another highlight is the community and support system. The academy fosters a collaborative environment, encouraging students to work together and share knowledge. The feedback from instructors was always constructive, guiding me to improve continuously. In addition, the resources provided, including up-to-date course materials, case studies, and access to industry-standard tools, were incredibly useful. The academy also offers excellent career support, helping me with resume building, interview preparation, and job placements. Overall, exceeded my expectations, and I highly recommend it to anyone looking to advance their skills in data analysis and AI. It's an investment in your future that truly pays off.

Punith JB

I completed Data Science course at Master Campus , and it exceeded my expectations. The curriculum was well-designed, covering essential topics like machine learning, data analysis, and visualization, with a strong emphasis on practical applications. The instructors were experts in their field, making even the most complex concepts accessible and engaging. The course also provided ample opportunities for hands-on practice through real-world projects, which helped me build a strong portfolio. The support services offered by Master Campus were also exceptional. From career counseling to resume workshops and mock interviews, the institute went above and beyond to prepare us for the job market. Their networking events and industry connections were instrumental in helping me land my first data science role. Overall, the course was challenging but highly rewarding, and I would recommend it to anyone serious about pursuing a career in data science.

Data Science & AI Engineering