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Sonali Kubde

Hands-on Data Analysis lessons with problem solving focus

5(126)

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Data Science tutor - Sonali Kubde

Bachelors degree

/ 30 min

Rated 5 out of 5 stars.
★★★★★
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Highly skilled & top-rated
126 ratings
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Data sciece 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.

Data Science tutor skills

Data visualization icon

Data visualization

Machine learning icon

Machine learning

Statistical analysis icon

Statistical analysis

Business intelligence icon

Business intelligence

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Assignment help

Sonali graduated from Mumbai University

Sonali graduated from Mumbai University
Sonali graduated from Mumbai University

About your data science 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.

keyLearning

Data Science concept taught by Sonali

Student learned about 11 hours ago

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.

Feature Engineering: Transforming Zip Codes

Data Loading and Inspection with Pandas

Data Copying for Safe Manipulation

Descriptive Statistics for Data Understanding

Dropping Irrelevant Columns

Handling Invalid Data and Data Type Conversion

Understanding Categorical Data

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Student learned 3 days ago

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.

Slicing

List Methods

Control Flow: Looping (for and while)

Control Flow: Conditional Execution (if/else)

Control Flow: Sequential Execution

Indexing

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Student learned 5 days ago

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.

Sets and Deduplication

Lists vs. Tuples

Functions

Match-Case Statements

DataFrames from Dictionaries

Dictionaries and Key-Value Pairs

Indexing

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Student learned 10 days ago

The Student and Tutor discussed the t-SNE method for dimensionality reduction and then completed a graded quiz on K-means clustering. They reviewed the importance of scaling data and the interpretation of elbow plots in determining the optimal number of clusters. The session concluded with the Tutor introducing a coding example that implemented t-SNE, which will be continued in a future session.

K-Means Clustering Algorithm

T-distributed Stochastic Neighbor Embedding (t-SNE)

Dimensionality Reduction

The Elbow Method

Silhouette Score

Scaling Variables in K-Means

Data Analysis Workflow for Machine Learning Models

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Student learned 11 days ago

The session covered different clustering techniques, including hard and soft clustering, centroid-based clustering, and the K-means algorithm. The student learned how to determine the optimal number of clusters using the elbow method and silhouette scores. The next session will cover a new concept and a quiz.

Hard Clustering

Soft Clustering

Centroid-Based Clustering

Converting Categorical Data for Clustering

Intra-cluster and Inter-cluster Distances

Elbow Method for Optimal Number of Clusters

Silhouette Score

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Student learned 11 days ago

The session reviewed the concept of clustering as an unsupervised machine learning technique, focusing on how data points are grouped based on similarity and distance measures. The student and tutor discussed the differences between unsupervised and supervised learning. The tutor went through the student's notes on the topic.

Unsupervised Learning

Clustering Defined

No Predefined Categories

Similarity and Distance Measures

Cluster ID

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Learner types for data science class

Adult / Professional icon

Adult / Professional

College icon

College

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School

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All Levels

Your data science tutor also teaches

Data Analysis

Data Analysis

Power BI

Power BI

Tableau

Tableau

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Every tutor is interviewed and selected for subject expertise and teaching skill.