This Data Science Course is designed for students, engineering learners, and aspiring data professionals who want to gain hands-on expertise in data analysis, visualization, and machine learning. Covering Core Python, Machine Learning, and basic Tableau, the course provides a comprehensive foundation to work with real-world data and make informed decisions.

The course is suitable for beginners as well as students looking to strengthen their programming and analytical skills. It bridges the gap between theory and practical application, ensuring learners can confidently work with datasets, extract insights, and implement machine learning models.

What Students Will Learn

By the end of this course, students will be able to:

• Write efficient Python code for data manipulation and analysis using NumPy, Pandas, and Matplotlib

• Understand fundamental data science concepts and workflows

• Perform data visualization using Tableau and Python libraries

• Apply supervised machine learning techniques for predictive analytics

• Explore datasets, clean and preprocess data, and generate meaningful insights

• Build simple machine learning models for regression and classification

• Understand evaluation metrics and model performance

• Develop problem-solving skills for real-world data scenarios

• Gain hands-on experience through practical exercises and mini-projects

This approach ensures that students not only learn theory but also develop practical skills highly valued in the data industry.

Teaching Method

The course is offered in online and offline modes, providing flexibility for learners.

Key teaching features include:

• Live interactive sessions with coding demonstrations

• Step-by-step guidance on Python programming and machine learning

• Hands-on practice with real datasets

• Regular assignments and exercises to reinforce learning

• Doubt-solving sessions in every class

• Practical exposure to Tableau for basic data visualization

• Flexible pace suitable for beginners and intermediate learners

Students will engage in projects and exercises that integrate Python programming, machine learning, and data visualization to strengthen their data science skillset.

Why This Tutor

Nandhini A follows a structured, learner-focused approach, ensuring students grasp both theoretical concepts and practical applications. Her teaching emphasizes clarity, project-based learning, and real-world problem-solving, allowing students to confidently apply their skills in academic or professional settings.

Location Context

Classes are available online for students from any location and offline, enabling hands-on, in-person learning for local participants. This hybrid approach ensures accessibility and flexibility for diverse learning needs.

Benefits / Outcomes

Upon completing this course, students will be able to:

• Analyze and visualize datasets using Python and Tableau

• Implement machine learning models for predictive analytics

• Understand data science workflows and industry best practices

• Apply Python programming skills in practical data projects

• Build a foundation for advanced data science, AI, or analytics courses

• Develop a portfolio of mini-projects demonstrating applied skills

This course prepares students for careers in data science, analytics, and machine learning, while also strengthening problem-solving and coding abilities.