Sonali Kubde
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Sonali Kubde
Bachelors degree
/ 55 min
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 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.
Coding tutor specialities
Assignment help
Homework help
Project help
Exam prep
Debugging
Coding concepts taught by Sonali
The session focused on building and optimizing a decision tree model using Python to predict loan applications. The student reviewed data exploration, preprocessing, EDA, model building, validation, and pre-pruning techniques. The session was cut short, and follow-up to continue working on the code and post-pruning was scheduled for the next session.
Data Preprocessing: Categorical to Category
Exploratory Data Analysis (EDA): Univariate vs. Bivariate Analysis
Feature Correlation and Heatmaps
Decision Tree Classifier and Categorical Data
Gini Impurity Explained with Analogy
Understanding the Confusion Matrix
Overfitting: Recognizing and Avoiding It
Pre-Pruning for Decision Trees
The class covered univariate and bivariate data analysis techniques in Python using libraries like Seaborn. The student practiced creating and interpreting various plots, including histograms, box plots, bar plots, and heatmaps, to understand data distributions and relationships. Data preprocessing steps, such as outlier detection and feature selection, were also discussed to prepare for model building in the next session.
Bivariate Analysis and Stacked Bar Plots
Identifying and Handling Outliers with Quantiles
Heatmaps for Correlation Analysis
Labeled Bar Plots for Categorical Data
Univariate Analysis with Histograms and Box Plots
The Tutor and Student reviewed Python functions, including their purpose, types (built-in and user-defined), and immutability. The Student learned about the concept of a stack and how it relates to function execution. The session concluded with an introduction to lambda functions and a brief overview of upcoming topics: Pandas and NumPy for data processing.
Function Execution with Stack (First In
Last Out)
Functions: Purpose and Significance
Built-in vs. User-Defined Functions
Immutability of Functions
Lambda Functions (Anonymous Functions)
Parameters and Arguments in Functions
Pop Function
The Student and Tutor worked through a data pre-processing project in Python using a provided Jupyter Notebook. They loaded and explored a loan dataset, handled missing or incorrect values, and converted relevant columns into categorical data types. The next session will cover exploratory data analysis, including univariate and bivariate analysis, missing data handling, and outlier detection.
Descriptive Statistics for Data Understanding
Understanding Categorical Data
Feature Engineering: Transforming Zip Codes
Handling Invalid Data and Data Type Conversion
Dropping Irrelevant Columns
Data Copying for Safe Manipulation
Data Loading and Inspection with Pandas
The Student and Tutor reviewed Python slicing, control flow (sequential, conditional, and looping), and list manipulation (append, insert, clear). The Student practiced using loops and indexing with lists and was assigned homework from Python Refresher 2, skipping nested loops but completing exercises from page 14 onwards, focusing on Python list methods.
Control Flow: Looping (for and while)
List Methods
Control Flow: Conditional Execution (if/else)
Control Flow: Sequential Execution
Indexing
Slicing
The session covered Python lists, tuples, sets, and dictionaries, explaining their properties and use cases. The Student learned how to create and manipulate these data structures, including indexing and mutability. The Tutor assigned homework involving implementing a while loop for a code that they built together during the class.
Functions
Match-Case Statements
DataFrames from Dictionaries
Dictionaries and Key-Value Pairs
Sets and Deduplication
Indexing
Lists vs. Tuples
Approach & tools used by coding tutor
Postman
Git & GitHub
Visual Studio Code
Xcode
Bitbucket
Learner types for coding classes
Coding for Beginners
Coding for College students
Coding for Adults
Coding for School students
Coding for Kids
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