Sonali Kubde
Engaging GRE Mathematics lessons with problem solving approach
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Sonali Kubde
Bachelors degree
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Each lesson is 55 min
50 lessons
20% off
/ lesson
30 lessons
15% off
/ lesson
20 lessons
10% off
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10 lessons
5% off
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5 lessons
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1 lessons
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Sonali - Know your GED 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


GRE tutor specialities
Test prep
Concepts learning
Test prep 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.

GRE concepts taught by Sonali
The session involved guided practice on Pandas, focusing on merging, concatenating, and data exploration techniques. The student worked through a worksheet in Google Colab, loading datasets and performing various data analysis tasks. The student was assigned to review the material covered in the session before the next class.
Inner
Left
Right
and Outer Joins
Descriptive Statistics with `.describe()`
Data Exploration with Head
Tail
and Shape
The Student reviewed a data analysis project focused on predicting customer satisfaction. They covered data cleaning, exploratory data analysis with visualizations, linear regression, and Lasso regularization. The Student plans to review the material again before submitting the project.
Linear Regression and Feature Selection
Logistic Regression and Churn Prediction
Lasso Regression with Cross-Validation
Regularization: Lasso Regression
Data Visualization: Summarized vs. Raw Data
Train-Test Split
Data Quality Issues and Handling
Data Preparation: Concatenation and Merging
The Student reviewed advanced Pandas concepts with the Tutor, including merging, concatenation, groupby operations, and pivot tables. The Student practiced these concepts using code examples, manipulating dataframes, and addressing errors. The next session is scheduled to continue reviewing Pandas and data manipulation techniques, including topics in week five.
Removing Rows and Columns
Pivot Tables
GroupBy Operations
Concatenating DataFrames
Merging DataFrames
DataFrame Manipulation: Renaming and Column Operations
Handling Missing Data
The session involved a detailed explanation of the random forest algorithm, differentiating it from the bagging method, and discussing strategies to avoid overfitting. The Student also took a quiz to test their knowledge, achieving a perfect score. The Student and Tutor scheduled another session to work on the code implementation of the random forest algorithm.
Bagging vs. Random Forest: Core Difference
Overfitting in Bagging and Random Forests
Feature Selection and Missing Data
Random Forest Algorithm Steps
Out-of-Bag (OOB) Error
Class Weight in Random Forest
The session served as a review of Python fundamentals, including lists, tuples, dictionaries, conditional statements, and loops, using a credit card application dataset. The Student practiced using Google Colab to run code and manipulate data structures. The Tutor plans to cover Pandas in the next session and work on assignments related to the topics reviewed.
Dictionaries
Functions
Looping Statements (for loop)
Conditional Statements (if/else)
Tuples
Lists
Variables and Data Types
The session focused on ensemble learning methods, specifically bagging and bagging classifiers. The Student learned the difference between bagging and decision tree classifiers, and how bagging helps reduce overfitting, along with coding implementations in Python. The next session will cover Random Forests and review advanced concepts.
Two Closet Analogy
Ensemble Learning
Sampling Techniques
Base Classifiers
Bagging Classifier vs. Bagging
Bagging: Bootstrap Aggregating
Addressing Bias in Data
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