With over four years of diverse teaching experience across various platforms, including and private lectures, my teaching philosophy centers on creating an engaging, interactive, and student-focused learning environment. I strive to foster a deep understanding of complex subjects such as Statistics, Finance, Python, R, SAS, Machine Learning, Mathematics, and Investment, by employing a variety of innovative teaching methods tailored to meet the unique needs of each student.

- Interactive Online Teaching Methods: In my online classes, I utilize a range of interactive tools and techniques to enhance learning and maintain student engagement and I often use the flipped classroom model, where students are provided with pre-recorded lectures, reading materials, and problem sets before the live class.

- Discussion Forums and Q&A: Platforms like Moodle and Edmodo are employed for continuous interaction. Students can post their queries, share insights, and collaborate on projects outside of live class hours, fostering a community of learning and tools such as Kahoot, Mentimeter, and Quizlet are integrated into lessons to make learning more engaging.

- Project-Based Learning: Especially in subjects like Python, R, Machine Learning, and Investment, I emphasize project-based learning. Students work on real-world projects, which helps them apply theoretical knowledge to practical scenarios.

I have an expertise in below subjects and topics:

- Statistics: Covers descriptive statistics, probability, inferential statistics, hypothesis testing, regression analysis, and statistical software applications.

- Finance: Encompasses financial statements analysis, corporate finance, investment strategies, risk management, and portfolio optimization.

- Programming (Python, R, SAS): Ranges from basic syntax and data structures to advanced topics like data manipulation, visualization, machine learning algorithms, and software development.
Machine Learning: Includes supervised and unsupervised learning, neural networks, natural language processing, model evaluation, and practical implementation using Python and R.

- Mathematics: Focuses on algebra, calculus, linear algebra, and statistics essential for higher studies in engineering, economics, and data science.

- Investment: Covers financial markets, instruments, investment analysis, portfolio management, and behavioral finance.