Basic to advance Python for data analysis

Key Offerings:

1. Comprehensive Curriculum:

   • Beginner to Advanced Levels: Classes are designed to accommodate all skill levels, from complete beginners to experienced programmers looking to enhance their skills.
   • Core Python Concepts: Learn the fundamentals including data types, control structures, functions, and modules.
   • Advanced Topics: Dive into object-oriented programming, exception handling, file operations, and libraries like NumPy, pandas, and more.
   • Real-World Applications: Hands-on experience with data analysis, automation, and more.

2. Project-Based Learning:

   • Practical Projects: Work on real-world projects that reinforce learning and build a portfolio.
   • Capstone Projects: Apply all learned skills in a comprehensive project that showcases your abilities.

3. Interactive Learning:

   • Live Coding Sessions: Participate in interactive coding exercises during classes.
   • Coding Labs: Access to coding labs for practicing and solving problems in real-time.

Mentorship:

1. Experienced Instructors:

   • Industry Professionals: Learn from instructors who are experienced professionals in the field.
   • One-on-One Sessions: Receive personalized guidance and feedback through one-on-one mentoring sessions.

2. Career Guidance:

   • Resume Building: Get help with crafting a standout resume that highlights your Python skills and projects.
   • Interview Preparation: Prepare for technical interviews with mock interviews and practice questions.

Tests and Assignments:

1. Regular Assessments:

   • Quizzes and Tests: Periodic quizzes and tests to evaluate understanding and retention of material.
   • Assignments: Practical assignments that challenge you to apply concepts and solve problems.

2. Graded Projects:

   • Project Evaluation: Receive detailed feedback on projects to understand strengths and areas for improvement.
   • Peer Reviews: Participate in peer review sessions to learn from others and receive diverse feedback.

Different Learning Paths:

1. Data Science Path:

   • Focus Areas: Learn data manipulation, statistical analysis, and data visualization.
   • Tools: Gain expertise in libraries such as pandas, NumPy, Matplotlib, and Scikit-learn.

2. Automation and Scripting Path:

   • Focus Areas: Develop scripts for automating tasks and processes.
   • Tools: Learn about libraries for web scraping and automation, and working with APIs.

3. Machine Learning Path:

   • Focus Areas: Explore machine learning algorithms, model building, and evaluation.
   • Tools: Work with TensorFlow, Keras, and Scikit-learn.