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Sonali Kubde

Collaborative Databases & Python lessons with practical focus

5(126)

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Private tutor - Sonali Kubde

Bachelors degree

/ 30 min

Rated 5 out of 5 stars.
★★★★★
Popular
Highly skilled & top-rated
126 ratings
Ratings

Sonali - Know your tutor

I have completed my bachelors(Mumbai University) and Masters(Syracuse, New York) of engineering in computer science. For nearly 2 years, I worked with a private company and had opportunities to teach and deliver the knowledge transfer to the new joinees all across the world. I do consider myself really good at multitasking, delegation, strategic thinking, explaining things in a detailed manner, and making presentations. My area of expertise is Python and SQL. In addition, I have good knowledge of business and data analysis including PowerBI, Tableau, Rstudio, SQL, Database Management System and much more.

Sonali graduated from Mumbai University

Sonali graduated from Mumbai University
Sonali graduated from Mumbai University

Programming class overview

Tutoring is not just about transferring knowledge; it's about fostering an environment where learners can thrive, explore, and become proficient in their chosen fields. As a tutor, my approach is to provide detail-oriented sessions tailored specifically to the needs of my students. Whether their focus is academic or industry-related, my goal is to guide them through the complexities of technology and prepare them for real-world challenges. Structured Learning Path Each tutoring session is structured to progressively build knowledge and skills. The journey begins with foundational concepts, gradually moving towards more complex aspects of the technology. This structured approach ensures that students of all levels can keep pace and thoroughly understand each topic before moving on to the next. Regular quizzes and assignments are integrated to reinforce the material covered and to assess understanding continuously. Customization to Meet Individual Needs Recognizing that each student has unique learning needs, I tailor my sessions to suit individual preferences and requirements. This customization might involve adjusting the pace of the course, focusing on particular areas of interest, or providing additional resources for independent study. I also encourage students to bring their project ideas, which we can develop together during the course, adding personal relevance and motivation to the learning process. Hands-on Project Experience The centerpiece of my tutoring approach is the hands-on project that we develop together. This project not only serves as a practical application of the theoretical knowledge but also helps students understand the workflow of real-world software development. We cover everything from initial planning and design to coding, testing, and deployment. This experience is invaluable as it equips students with the skills needed to handle actual tasks in a professional environment. Regular Feedback and Improvement Feedback is a crucial element of my teaching methodology. Regular feedback sessions help students identify their strengths and areas for improvement. I provide constructive criticism that is meant to encourage and guide students on their learning path. Similarly, I encourage students to give feedback on the tutoring sessions. This two-way feedback process helps maintain a dynamic learning environment that adapts to the needs of the students. Industry-Relevant Skills In addition to technical skills, I emphasize the development of soft skills such as problem-solving, critical thinking, and effective communication. These skills are often overlooked in traditional educational settings but are crucial in the industry. During our project work, students are encouraged to think critically about the solutions they are developing and to communicate their ideas and challenges clearly. Preparation for the Future By the end of the course, students will not only have a thorough understanding of the technology but also a project that they can showcase in their portfolio. This project serves as tangible proof of their capabilities and learning process, which is invaluable when applying for jobs or internships in the technology industry. Additionally, the skills they develop during the course will prepare them for future learning, whether they choose to advance in the same technology or explore new areas. Building a Healthy Learning Environment The pillars of my tutoring sessions—detail orientation, customization, hands-on experience, regular feedback, and a focus on industry-relevant skills—are designed to create a supportive and productive learning environment. My aim is not just to teach technology but to mentor students, guiding them towards achieving their personal and professional goals. Continuous Improvement and Lifelong Learning To further enhance the effectiveness of the tutoring sessions, I commit to continuous improvement in my teaching methods. This involves staying updated with the latest technological advancements and pedagogical strategies, ensuring that my students receive the most current and effective instruction. Additionally, I promote the concept of lifelong learning, encouraging students to view education as an ongoing process that extends beyond formal schooling or industry training.

Programming tutor specialities

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Code Review

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Code Optimization

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Upskilling

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Exam prep

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Job readiness

Paired coding icon

Paired coding

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Homework help

keyLearning

Computer Science concept taught by Sonali

Student learned 9 days ago

The Tutor and Student reviewed key concepts related to decision trees and model evaluation metrics, working through practice quizzes to solidify understanding. They focused on interpreting metrics like precision, recall, and Gini impurity, and understanding how to apply these concepts in machine learning model evaluation. The Student will continue practicing with these concepts in future sessions.

Impurity Measures (Gini and Entropy)

Decision Tree Root Node

Scatter Plot and Correlation

Box Plot and Median

Credit Card Approval Prediction

Drug Response Prediction

Leaf Node and Class Proportion

Accuracy

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Student learned 10 days ago

The Tutor introduced the Student to the basics of decision trees, including their structure and key differences from linear regression. The session covered Gini impurity and entropy as measures for selecting attributes in decision trees. The Student was assigned readings on business process visualization, decision tree overview, impurity measures, and splitting mechanisms from the course website.

Attribute Selection and Splitting Criteria

Gini Index Calculation

Entropy Calculation

Impurity and Purity Explained

Decision Tree Algorithm Fundamentals

Data Scientist vs. Applied Data Scientist

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Student learned 14 days ago

The session covered linear regression, progressing from simple to multiple regression models. The student practiced implementing these models in Python using the Boston dataset and evaluated their performance using mean squared error and R-squared metrics. The Tutor assigned homework to practice multiple linear regression with a new dataset.

Independent and Dependent Variables (X and Y)

Multiple Linear Regression

Model Evaluation

Coefficients and Intercept

Linear Regression Model

Train/Test Split

Target Variable in a Dataset

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Student learned 16 days ago

The Student and Tutor practiced implementing linear regression in Python. They covered importing libraries, loading a dataset from a CSV file, adding a target variable, and splitting the data into training and testing sets. The plan is to continue with model training, evaluation, multiple linear regression, and handling categorical variables in the next session.

Data Loading and Exploration

Train-Test Split

Independent and Dependent Variable Definition

Target Variable Addition

Importing Libraries

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Student learned 17 days ago

The Tutor introduced the Student to machine learning, covering the basics of model training, accuracy testing, and the differences between supervised and unsupervised learning. The Student learned about linear regression, the Pearson correlation coefficient, and key evaluation metrics. The next session will focus on coding and implementation of the concepts discussed.

Machine Learning Training and Testing

Supervised vs. Unsupervised Learning

Linear Regression Fundamentals

Error Minimization: Best Fit Line

Coefficient of Determination (R²)

Pearson Correlation Coefficient (PCC)

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Student learned 24 days ago

The Student and Tutor troubleshooted a display issue with a presentation. They also discussed the newly released machine learning course, including its content on linear regression and associated quizzes and exercises. The Student will prepare notes on the first week's material, and they will schedule a follow-up session to review the notes and engage in hands-on practice.

Prioritizing Tasks and Time Management

Collaborative Learning and Support

Upcoming Machine Learning Course

Presentation Troubleshooting

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Learner for programming class

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All Levels

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Adult / Professional

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College

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School

Your programming tutor also teaches

Databases

Databases

Python

Python

R

R

Microsoft Excel

Microsoft Excel

Teaching tools used by tutor

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Android Studio

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Visual Studio Code

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