Anhad Bhatia

Greetings! I'm Anhad, a seasoned Python tutor specializing in data analytics and scientific computing. With certified expertise in Python and Data Analytics, I've spent the past four years shaping minds and careers in scientific computing and data science. I'm proud to have served as an assistant faculty at distinguished institutions like IIT Delhi and the University of Delhi. Additionally, I've contributed to research projects at the University of Sheffield, bringing a global perspective to my teaching.

Professional Experience:

My educational journey includes impactful collaborations on research projects at the University of Sheffield, where I've applied Python in cutting-edge studies. Beyond that, I've had the privilege of instructing at prestigious institutions across India, such as IIT Delhi and the University of Delhi. My teaching philosophy combines theoretical knowledge with practical applications, ensuring my students not only understand Python but also appreciate its diverse applications.

Teaching Expertise:

Whether you're starting with Python basics or delving into the intricacies of machine learning, my lessons are crafted to suit diverse learning needs. My approach emphasizes hands-on projects and real-world applications to solidify your understanding of Python. From fundamental concepts to advanced machine learning techniques, I'm here to guide you every step of the way.

Lesson Plan:

1. Introduction to Python (Weeks 1-2):
- Overview of Python and applications.
- Setting up Python environment (IDEs, Jupyter Notebooks).
- Basic syntax and data types.
- Control flow and loops.

2. Data Handling with Python (Weeks 3-4):
- Introduction to NumPy for numerical computing.
- Data manipulation with Pandas.
- Data visualization using Matplotlib and Seaborn.

3. Introduction to Scientific Computing (Weeks 5-6):
- Scientific computing libraries: SciPy.
- Solving mathematical problems using Python.

4. Intermediate Python (Weeks 7-8):
- Advanced data structures (dictionaries, sets).
- File handling and input/output operations.

5. Introduction to Machine Learning (Weeks 9-10):
- Overview of machine learning concepts.
- Scikit-learn library for machine learning in Python.

6. Hands-On Projects and Research Integration (Weeks 11-12):
- Applying Python skills to real-world projects.
- Assistance and support in projects.
- Creating your own web applications
- Deployment of the same

These lessons are designed in such a way, that learning experience becomes engaging and develops logic to solve problems. Let's dive in and crack it!