Sonali Kubde

Collaborative Databases & Python lessons with practical focus

5(126)

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Sonali Kubde

Bachelors degree

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Each lesson is 55 min

50 lessons


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30 lessons


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20 lessons


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10 lessons


5% off

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5 lessons


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1 lessons


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Rated 5 out of 5 stars.
★★★★★
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Highly skilled & top-rated
126 ratings
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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 tutor specialities

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Paired coding

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Project help

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Upskilling

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Homework help

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Assignment help

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Code Review

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Job readiness

Learner for programming class

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Adult / Professional

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All Levels

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College

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School

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.

Your programming tutor also teaches

Databases

Databases

Python

Python

R

R

Microsoft Excel

Microsoft Excel

Flexible Scheduling

Allows 1h early scheduling

Allows 1h early rescheduling

Can wait for 20 mins after joining

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Computer Science concepts taught by Sonali

Student learned 19 days ago

The Student revised data analysis techniques using Pandas, focusing on data cleaning and preparation. They worked through a pre-existing notebook on Uber data, converting data types, handling missing values, and extracting date components. The next session will cover Matplotlib and Seaborn for data visualization.

Data Shape vs. Data Insights

Data Type Conversion: Datetime

Feature Engineering: Extracting Date Components

Handling Missing Values (NaNs)

Data Correlation with Heatmaps

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Student learned 20 days ago

The Student and Tutor reviewed cross-validation techniques, including different types like k-fold and stratified cross-validation. They also discussed methods for handling imbalanced datasets such as over-sampling and under-sampling. The Tutor assigned further review of sampling techniques and model tuning in preparation for the upcoming quiz.

SMOTE (Synthetic Minority Over-sampling Technique)

Holdout Validation

Imbalanced Data Handling

Stratified Cross Validation

Leave-One-Out Cross Validation (LOOCV)

Cross Validation

K-Fold Cross Validation

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Student learned about 1 month ago

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.

File Path Handling in Pandas

Concatenate vs. Merge

Group By Operations

Inner

Left

Right

and Outer Joins

Understanding `axis` in Pandas Operations

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Student learned about 2 months ago

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

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Student learned about 2 months ago

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

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Student learned about 2 months ago

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

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Teaching tools used by tutor

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Android Studio

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Visual Studio Code

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