Sonali Kubde
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Sonali Kubde
Bachelors degree
Enroll after the free trial
Each lesson is 55 min
50 lessons
20% off
/ lesson
30 lessons
15% off
/ lesson
20 lessons
10% off
/ lesson
10 lessons
5% off
/ lesson
5 lessons
-
/ lesson
1 lessons
-
/ lesson
About your coding tutor - Sonali
I am a passionate coding tutor with over 2 years of teaching experience. Armed in masters degree, I have excelled Java and spring boot. My expertise lies in Java, spring boot. I can do the assignments for Java in limited time. I am pretty good with Jenkins and postman which goes hand in hand with Java
Sonali graduated from Mumbai University
Coding tutor specialities
Homework help
Project help
Debugging
Exam prep
Assignment help
Learner types for coding classes
Coding for Beginners
Coding for School students
Coding for College students
Coding for Kids
Coding for Adults
Coding class highlights
My teaching style is clear, adaptive, and goal-focused. I simplify complex ideas into easy-to-understand steps, using real-world examples and analogies when helpful. I adjust the pace to match your learning speed and provide just the right amount of challenge to keep you engaged without feeling overwhelmed. I encourage active thinking and questions, helping you build a strong understanding rather than just memorizing. Whether you prefer visuals, practice problems, or step-by-step explanations, I tailor the approach to suit your learning style and goals. The focus is always on making learning effective, engaging, and empowering for you.
Flexible Scheduling
Allows 1h early scheduling
Allows 1h early rescheduling
Can wait for 20 mins after joining
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10 day Refund
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Free Tutor Swap
Coding concepts taught by Sonali
The session focused on relational database concepts, specifically one-to-one, one-to-many, and many-to-many relationships between entities. The Student practiced identifying these relationships using real-world examples and learned about cardinality as a tool for determining relationship types. As homework, the Student was assigned to create one example for each relationship type (one-to-one, one-to-many, and many-to-many).
Cardinality in Relationships
One-to-One Relationship (1:1)
One-to-Many Relationship (1:M)
Many-to-Many Relationship (M:N)
Database Representation of 'Many'
Importance of Foundational Database Concepts
The session introduced the fundamental concepts of Relational Database Management Systems (RDBMS), specifically focusing on the types of relationships between tables: one-to-one, one-to-many, and many-to-many. The student practiced identifying and understanding these relationships through examples and learned about the importance of atomicity in database design, with a plan to cover normalization and Power BI modeling in subsequent sessions.
Atomicity in Databases
Database Relationships: Many-to-Many
Database Relationships: One-to-Many
Database Relationships: One-to-One
RDBMS: Relational Database Management System
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
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.
Cross Validation
Holdout Validation
Leave-One-Out Cross Validation (LOOCV)
K-Fold Cross Validation
Stratified Cross Validation
Imbalanced Data Handling
SMOTE (Synthetic Minority Over-sampling Technique)
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
Data Exploration with Head
Tail
and Shape
Descriptive Statistics with `.describe()`
Understanding `axis` in Pandas Operations
Concatenate vs. Merge
Inner
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.
Data Quality Issues and Handling
Logistic Regression and Churn Prediction
Lasso Regression with Cross-Validation
Regularization: Lasso Regression
Linear Regression and Feature Selection
Data Visualization: Summarized vs. Raw Data
Train-Test Split
Data Preparation: Concatenation and Merging
Approach & tools used by coding tutor
Postman
Xcode
Visual Studio Code
Bitbucket
Git & GitHub
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