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Data Science Course Big Data Projects Placement

Batch Name

PCDS

Course Start

1-March-2026

Course duration

180 Hours

Eligibility

12th

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Data Science Course Big Data Projects Placement

Data Science Course – Big Data, Live Projects & Career Boost


Introduction

The world is rapidly transforming through the power of data, and this data science course is designed to prepare you for a thriving career in this high-demand field. Whether you're learning online from home or attending in-person classes, this course helps you understand what is data science, how it impacts industries, and how you can use it to drive decisions, solve problems, and innovate using real-world data.

Through practical exposure and hands-on experience, you'll explore everything from statistics for data science and data visualization to data mining, machine learning, and big data analytics. You'll also become familiar with tools and platforms used by modern data professionals, making this one of the best data science courses available today.

Module-1

Course Papers

  • Foundations of Data Science
Course Syllabus

  • What is Data Science
  • The Evolution of Data Science
  • Importance of Data Science
  • Applications of Data Science
  • Key Skills Required for Data Scientists
  • Future Trends in Data Science

  • Data Collection
  • Data Cleaning and Preparation
  • Exploratory Data Analysis (EDA)
  • Model Building
  • Model Evaluation
  • Deployment and Monitoring

  • Data Types
  • Data Structures
  • Choosing the Right Data Structure

  • Automated Data Collection
  • Primary Data Collection
  • Secondary Data Collection
  • Real-Time Data Collection
  • Ethical Considerations in Data Collection

  • Importance of Data Cleaning
  • Common Data Cleaning Techniques
  • Tools for Data Cleaning
  • Challenges in Data Cleaning
  • Best Practices for Data Cleaning

  • Importance of EDA
  • Steps in Exploratory Data Analysis

  • Importance of Feature Engineering
  • Types of Features

  • Importance of Model Selection
  • Types of Machine Learning Models

  • Importance of Model Deployment
  • Deployment Strategies
  • Model Monitoring and Maintenance
Module-2

Course Papers

  • Data Science and Industry Applications
Course Syllabus

  • Ensemble Learning
  • Deep Learning
  • Transfer Learning
  • Reinforcement Learning
  • AutoML (Automated Machine Learning)

  • Importance of Data Ethics
  • Privacy Concerns in Machine Learning
  • Bias and Fairness in Machine Learning
  • Regulatory Frameworks for Ethical AI
  • Best Practices for Ethical Machine Learning
  • Real-World Examples of Ethical and Unethical AI

  • Importance of Data Visualization
  • Principles of Effective Data Visualization
  • Types of Data Visualizations
  • Tools for Data Visualization
  • Common Mistakes in Data Visualization
  • Practical Example: Creating a Sales Dashboard

  • Characteristics of Big Data
  • Types of Big Data
  • Big Data Tools and Technologies
  • Challenges in Managing Big Data
  • Applications of Big Data

  • Introduction to Machine Learning
  • How Machine Learning Differs from Traditional Programming
  • Key Machine Learning Techniques
  • Steps in Building a Machine Learning Model
  • Applications of Machine Learning
  • Challenges in Machine Learning

  • Importance of Data Visualization
  • Types of Data Visualizations
  • Tools for Data Visualization
  • Designing Effective Visualizations
  • Real-World Applications of Data Visualization
  • Challenges in Data Visualization

  • Understanding Data Ethics
  • Privacy Concerns in Data Science
  • Ethical Challenges in Data Science
  • Regulations and Frameworks for Data Privacy
  • Strategies for Ethical Data Practices
  • Balancing Ethics and Innovation

  • What is Data-Driven Decision Making?
  • The Process of Data-Driven Decision Making
  • Tools for Data-Driven Decision Making
  • Real-World Applications
  • Benefits and Challenges

  • What is Data Governance?
  • Importance of Data Governance
  • Data Security in Data Science
  • Regulatory Frameworks and Compliance
  • Challenges in Data Governance and Security
  • Best Practices for Governance and Security

  • What is Data Ethics?
  • Importance of Ethics in Data Science
  • Challenges in Ethical Data Use
  • What is Responsible AI?
  • Best Practices for Implementing Responsible AI

  • Overview of a Complete Data Science Project Workflow
  • Real-World Project: House Price Prediction
  • Project Ideas for Data Science Practice
  • Best Practices for Data Science Projects
  • Building a Data Science Portfolio
  • Final Thoughts on the Future of Data Science
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Why Choose This Course?

This data science course with certification stands out because it offers more than just theory. You’ll gain real-world insights into how companies use data science to stay competitive. You’ll also build complete Data Science Projects using the latest Data Science Modules, leveraging tools such as the python compiler, SQL, and data visualization tools like Tableau and Power BI.

The course content reflects current industry demands and equips you with skills in Data Science with Python, working knowledge of SQL query interview questions, and the ability to navigate big data tools with ease. With a balanced mix of foundational learning and advanced topics like Deep Learning, this is a data science course with placement assistance designed to prepare you for job roles in both tech and non-tech sectors.

What Will You Learn?

  • The complete data science syllabus, from basics to advanced
  • How to use a python compiler for building models
  • The difference between AI and Data Science
  • How Python is used in Data Science across industries
  • Various types of machine learning and how to choose the right one
  • Building and deploying ML models using ethical, explainable practices
  • Practical data visualization techniques for storytelling and insights
  • Managing large datasets using big data tools and technologies
  • How to analyze data through SQL and prepare for SQL interview questions
  • The role of data governance, privacy, and responsible AI in data-centric systems
  • Real-world data science and machine learning applications
  • How to create your portfolio through hands-on Data Science Projects


Opportunities After This Course

  • Data Scientist
  • Data Analyst
  • Machine Learning Engineer
  • Business Intelligence Analyst
  • Data Engineer
  • Big Data Analyst
  • Research Scientist in AI
  • Product Analyst
  • Consultant for analysis in big data environments


Whether you aim to join startups or large enterprises, this course empowers you with the skills that recruiters are looking for. You’ll also be confident answering technical questions, including common SQL interview questions and machine learning vs deep learning in data science.

Who Can Enroll?

  • Fresh graduates and final-year students
  • Working professionals looking to switch to Data Science
  • Python or SQL developers aiming to enter analytics roles
  • Entrepreneurs who want to use data for better business decisions
  • Anyone curious about how to start learning data science or wondering what does a data scientist do


No prior data science experience is needed, though basic knowledge of Python or programming is helpful.

Enroll Now

Whether you're aiming to master data science course online or attend live classroom sessions, this course offers unmatched flexibility and value. You’ll gain a recognized data science certification, access real-world projects, and receive support for placements and interview preparation.

Enroll now in one of the best data science courses available – and start building your future with data!