A.Pallipatti, Pappireddipatti, India - 636905.
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Tamil Mother Tongue (Native)
English Mother Tongue (Native)
gobi arts and science 2012
Bachelor of Science (B.Sc.)
A.Pallipatti, Pappireddipatti, India - 636905
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Class Location
Online Classes (Video Call via UrbanPro LIVE)
Student's Home
Tutor's Home
Years of Experience in Salesforce Administrator Training
6
Class Location
Online Classes (Video Call via UrbanPro LIVE)
Student's Home
Tutor's Home
Years of Experience in Salesforce Developer Training
5
Teaching Experience in detail in Salesforce Developer Training
Salesforce developer training at deloite, cognizant
Class Location
Online Classes (Video Call via UrbanPro LIVE)
Student's Home
Tutor's Home
Years of Experience in Python Training classes
5
Course Duration provided
1-3 months
Seeker background catered to
Individual
Certification provided
No
Python applications taught
Text Processing with Python, Scipy Stack with Python , Machine Learning with Python, PySpark, Web Scraping with Python , Networking with Python , Regular Expressions with Python , GUI (Graphical User Interfaces) with Python , Data Analysis with Python , Data Extraction with Python , Web Development with Python , Game Development with Python, Help in assignment, Data Science with Python, Testing with Python, Data Visualization with Python, Automation with Python
Class Location
Online Classes (Video Call via UrbanPro LIVE)
Student's Home
Tutor's Home
Years of Experience in Data Science Classes
10
Data science techniques
Python, Machine learning, Artificial Intelligence
Teaching Experience in detail in Data Science Classes
1. Course Design & Curriculum Development Structured learning paths from beginner to advanced levels in Data Science using Python. Created modular lesson plans covering: Python for Data Science (NumPy, Pandas, Matplotlib, Seaborn) Statistics & Probability Exploratory Data Analysis (EDA) Machine Learning (Supervised & Unsupervised) Model Evaluation & Tuning (cross-validation, confusion matrix, ROC-AUC) Capstone Projects and Kaggle Competitions 2. Tools & Technologies Taught Languages: Python, basic R Libraries: NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, XGBoost ML Frameworks: TensorFlow (basics), PyTorch (intro) Data Handling: SQL, Excel, CSV, APIs, web scraping Cloud & Deployment: Intro to Google Colab, GitHub, Streamlit for model deployment 3. Audience & Formats Taught live online classes, YouTube content, and 1-on-1 mentoring Audience ranged from: College students (STEM background) Working professionals transitioning to Data Science Self-learners preparing for interviews and projects 4. Projects Supervised Guided students to complete mini-projects and real-world datasets: Titanic survival prediction House price prediction (Kaggle) Customer segmentation (clustering) Sentiment analysis of tweets (NLP) Diabetes prediction (classification) 5. Assessment & Evaluation Designed quizzes, coding exercises, and project evaluations Conducted mock interviews and resume reviews Provided personalized feedback based on model performance and code quality 6. Outcomes Helped several learners: Get started in Kaggle Land internships and jobs as Junior Data Analysts Build GitHub portfolios Many transitioned from non-coding backgrounds to functional data science skills
1. Which classes do you teach?
I teach Data Science, Python Training, Salesforce Administrator and Salesforce Developer Classes.
2. Do you provide a demo class?
Yes, I provide a free demo class.
3. How many years of experience do you have?
I have been teaching for 6 years.
Class Location
Online Classes (Video Call via UrbanPro LIVE)
Student's Home
Tutor's Home
Years of Experience in Salesforce Administrator Training
6
Class Location
Online Classes (Video Call via UrbanPro LIVE)
Student's Home
Tutor's Home
Years of Experience in Salesforce Developer Training
5
Teaching Experience in detail in Salesforce Developer Training
Salesforce developer training at deloite, cognizant
Class Location
Online Classes (Video Call via UrbanPro LIVE)
Student's Home
Tutor's Home
Years of Experience in Python Training classes
5
Course Duration provided
1-3 months
Seeker background catered to
Individual
Certification provided
No
Python applications taught
Text Processing with Python, Scipy Stack with Python , Machine Learning with Python, PySpark, Web Scraping with Python , Networking with Python , Regular Expressions with Python , GUI (Graphical User Interfaces) with Python , Data Analysis with Python , Data Extraction with Python , Web Development with Python , Game Development with Python, Help in assignment, Data Science with Python, Testing with Python, Data Visualization with Python, Automation with Python
Class Location
Online Classes (Video Call via UrbanPro LIVE)
Student's Home
Tutor's Home
Years of Experience in Data Science Classes
10
Data science techniques
Python, Machine learning, Artificial Intelligence
Teaching Experience in detail in Data Science Classes
1. Course Design & Curriculum Development Structured learning paths from beginner to advanced levels in Data Science using Python. Created modular lesson plans covering: Python for Data Science (NumPy, Pandas, Matplotlib, Seaborn) Statistics & Probability Exploratory Data Analysis (EDA) Machine Learning (Supervised & Unsupervised) Model Evaluation & Tuning (cross-validation, confusion matrix, ROC-AUC) Capstone Projects and Kaggle Competitions 2. Tools & Technologies Taught Languages: Python, basic R Libraries: NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, XGBoost ML Frameworks: TensorFlow (basics), PyTorch (intro) Data Handling: SQL, Excel, CSV, APIs, web scraping Cloud & Deployment: Intro to Google Colab, GitHub, Streamlit for model deployment 3. Audience & Formats Taught live online classes, YouTube content, and 1-on-1 mentoring Audience ranged from: College students (STEM background) Working professionals transitioning to Data Science Self-learners preparing for interviews and projects 4. Projects Supervised Guided students to complete mini-projects and real-world datasets: Titanic survival prediction House price prediction (Kaggle) Customer segmentation (clustering) Sentiment analysis of tweets (NLP) Diabetes prediction (classification) 5. Assessment & Evaluation Designed quizzes, coding exercises, and project evaluations Conducted mock interviews and resume reviews Provided personalized feedback based on model performance and code quality 6. Outcomes Helped several learners: Get started in Kaggle Land internships and jobs as Junior Data Analysts Build GitHub portfolios Many transitioned from non-coding backgrounds to functional data science skills
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A.Pallipatti, Pappireddipatti 
