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J P Nagar, Bangalore, India - 560078.
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English Proficient
University of Essex 2024
Master of Science (M.Sc.)
J P Nagar, Bangalore, India - 560078
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Class Location
Online class via Zoom
Student's Home
Tutor's Home
BTech Computer Science subjects
Machine Learning, Programming in C#, Artificial Intelligence, Data Structures and Algorithms, Object Oriented Programming & Systems, Software Engineering and Architecture, Software Project Management, Information Retrieval
BTech Branch
BTech Computer Science Engineering
Type of class
Crash Course, Regular Classes
Class strength catered to
Group Classes, One on one/ Private Tutions
Taught in School or College
No
Class Location
Online class via Zoom
Student's Home
Tutor's Home
Years of Experience in Data Science Classes
1
Data science techniques
Python, Artificial Intelligence, Machine learning
Teaching Experience in detail in Data Science Classes
I have experience teaching and mentoring Data Science at both postgraduate and undergraduate levels, with a strong focus on building foundational understanding and practical application. My teaching approach emphasizes making complex concepts accessible, especially for learners transitioning from non-technical backgrounds. I have delivered structured sessions covering core Data Science topics including data types, data preprocessing, exploratory data analysis (EDA), data visualization, and introductory machine learning. I guide students through the complete data science workflow—from problem definition and data collection to cleaning, analysis, modeling, and interpretation of results. I provide hands-on training in Python using libraries such as NumPy, Pandas, Matplotlib, and Scikit-Learn, ensuring students gain practical coding experience alongside theoretical knowledge. I have also supported learners through lab sessions, coursework, and case studies, helping them debug code, understand model performance metrics, and improve analytical thinking. My mentoring style focuses on clarity, patience, and structured explanation of statistical concepts, including regression, classification, clustering, and model evaluation. Additionally, I incorporate real-world examples from healthcare, finance, and public datasets to help students understand industry applications. I encourage version control practices using GitHub and promote reproducible, well-documented work. My goal is to build both technical confidence and strong problem-solving skills in Data Science.
Upcoming Live Classes
1. Which BTech branches do you tutor for?
BTech Computer Science Engineering
2. Do you have any prior teaching experience?
No
3. Which classes do you teach?
I teach BTech Tuition and Data Science Classes.
4. Do you provide a demo class?
Yes, I provide a free demo class.
5. How many years of experience do you have?
I have been teaching for less than a year.
Class Location
Online class via Zoom
Student's Home
Tutor's Home
BTech Computer Science subjects
Machine Learning, Programming in C#, Artificial Intelligence, Data Structures and Algorithms, Object Oriented Programming & Systems, Software Engineering and Architecture, Software Project Management, Information Retrieval
BTech Branch
BTech Computer Science Engineering
Type of class
Crash Course, Regular Classes
Class strength catered to
Group Classes, One on one/ Private Tutions
Taught in School or College
No
Class Location
Online class via Zoom
Student's Home
Tutor's Home
Years of Experience in Data Science Classes
1
Data science techniques
Python, Artificial Intelligence, Machine learning
Teaching Experience in detail in Data Science Classes
I have experience teaching and mentoring Data Science at both postgraduate and undergraduate levels, with a strong focus on building foundational understanding and practical application. My teaching approach emphasizes making complex concepts accessible, especially for learners transitioning from non-technical backgrounds. I have delivered structured sessions covering core Data Science topics including data types, data preprocessing, exploratory data analysis (EDA), data visualization, and introductory machine learning. I guide students through the complete data science workflow—from problem definition and data collection to cleaning, analysis, modeling, and interpretation of results. I provide hands-on training in Python using libraries such as NumPy, Pandas, Matplotlib, and Scikit-Learn, ensuring students gain practical coding experience alongside theoretical knowledge. I have also supported learners through lab sessions, coursework, and case studies, helping them debug code, understand model performance metrics, and improve analytical thinking. My mentoring style focuses on clarity, patience, and structured explanation of statistical concepts, including regression, classification, clustering, and model evaluation. Additionally, I incorporate real-world examples from healthcare, finance, and public datasets to help students understand industry applications. I encourage version control practices using GitHub and promote reproducible, well-documented work. My goal is to build both technical confidence and strong problem-solving skills in Data Science.
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