Ankur Vishwakarma

Class Location

Online class via Zoom

Student's Home

Tutor's Home

Years of Experience in Full Stack Data Science

3

Teaching Experience in detail in Full Stack Data Science

I have hands-on teaching experience in **Full Stack Data Science**, where I focus on building both strong theoretical foundations and real-world practical skills required in the industry. My teaching approach covers the complete data science lifecycle: **1. Programming & Data Handling** I teach Python from basics to advanced, including libraries such as NumPy, Pandas, and Matplotlib. Students learn how to clean, manipulate, and analyze structured and unstructured data efficiently. **2. Statistics & Mathematics for Data Science** I simplify core concepts like probability, hypothesis testing, linear algebra, and descriptive statistics, ensuring students understand *why* models work, not just how to use them. **3. Machine Learning & Model Building** I train students in supervised and unsupervised learning techniques using scikit-learn. This includes regression, classification, clustering, and model evaluation with real datasets. **4. Deep Learning (Intro to Advanced)** I introduce neural networks and deep learning concepts using TensorFlow/Keras, focusing on practical implementation rather than just theory. **5. Data Visualization & Storytelling** Students learn to communicate insights using tools like Matplotlib, Seaborn, and dashboards. I emphasize business storytelling and decision-making. **6. SQL & Database Management** I teach SQL for data extraction, joins, aggregations, and real-world querying skills essential for analysts and data scientists. **7. Full Stack Integration** What makes my teaching “full stack” is the integration of: * Backend: Model building & APIs (Flask/FastAPI) * Frontend basics: Deploying simple web apps * Deployment: Hosting ML models using platforms like Heroku/Render **8. Real-world Projects** I guide students through end-to-end projects such as: * Predictive modeling systems * Recommendation engines * Data dashboards * ML-powered web applications **9. Industry Readiness** I also mentor students on: * Resume building for data roles * GitHub portfolio creation * Interview preparation (technical + case-based) **Teaching Style** My sessions are highly practical, interactive, and beginner-friendly. I adapt based on the student’s background and ensure concepts are clear through examples, assignments, and mini-projects. Overall, my goal is to help learners transition from **basic understanding to job-ready Full Stack Data Science professionals**.