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Dr. Gurinderjeet Kaur

Data Analysis & Power BI training that’s hands-on & engaging

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Dr. Gurinderjeet Kaur

Doctorate degree

Rated 5 out of 5 stars.
★★★★★
Popular
Highly skilled & top-rated
86 ratings
Ratings

About your data science tutor

Hello! I’m Dr. Gurinderjeet Kaur, a dedicated Data Science tutor with a doctorate in Computer Science Engineering. My teaching philosophy centers on the belief that complex concepts can be made simple and relatable. I strive to engage my students through interactive sessions, real-world examples, and hands-on practice, ensuring that learning is not only effective but also enjoyable. I cover a wide range of subjects essential for today’s data-driven world, including Data Analysis, Databases, Machine Learning, Power BI, Python, SPSS, Statistics, Tableau, and Microsoft Excel. Each of these areas offers unique insights and skills that are vital for success in various fields, whether you are just starting out or looking to enhance your existing knowledge. My approach is tailored to meet the individual needs of my students, whether they are in school, college, or are working professionals seeking to upskill. I believe that education should be a collaborative journey, and I am committed to providing the support and guidance needed to help each student reach their goals. Let’s embark on this learning journey together and unlock your potential in the exciting world of Data Science!

Meet Dr. Gurinderjeet

Dr. Gurinderjeet graduated from Thapar Institute of Engineering and Technology India

Dr. Gurinderjeet graduated from Thapar Institute of Engineering and Technology India
Dr. Gurinderjeet graduated from Thapar Institute of Engineering and Technology India

Data sciece class overview

As a tutor specializing in Data Analysis, Machine Learning, and Statistics, I provide structured and systematic lessons tailored to students at all levels, including school, college, and adult or professional learners. My teaching style combines lecture-based instruction with interactive elements and hands-on practice, ensuring that students not only grasp theoretical concepts but also develop practical problem-solving skills. I emphasize the use of real-world datasets and visualization tools, which makes the learning process more engaging and insightful. By incorporating these elements, I aim to bridge the gap between theoretical knowledge and practical application, allowing students to see the relevance of what they are learning in everyday contexts. Additionally, I encourage students to adopt innovative approaches and delve deeper into the subject matter through practical projects. This not only solidifies their understanding but also fosters a sense of curiosity and exploration. My goal is to ensure a comprehensive understanding of the topics covered, empowering students to confidently apply their knowledge in real-world scenarios and pursue further learning opportunities in the dynamic fields of Data Analysis and Machine Learning.

Data Science tutor skills

International Baccalaureate (IB) icon

International Baccalaureate (IB)

Project help icon

Project help

Predictive modeling icon

Predictive modeling

Australian Curriculum (AU) icon

Australian Curriculum (AU)

Provincial-specific curriculum (CA) icon

Provincial-specific curriculum (CA)

Paired coding icon

Paired coding

GCSE (UK) icon

GCSE (UK)

Data visualization icon

Data visualization

Assignment help icon

Assignment help

Data engineering icon

Data engineering

Statistical analysis icon

Statistical analysis

Machine learning icon

Machine learning

keyLearning

Data Science concepts taught by Dr. Gurinderjeet

Student learned 7 days ago

The student reviewed homework and then learned about the JavaScript `sort`, `every`, and `some` array methods. The Tutor explained the functionality of each method, including examples for sorting numbers, strings, and objects, as well as checking conditions with `every` and `some`. The student will learn DOM manipulation next session.

Every Method

Sort Method

Some Method

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Student learned 8 days ago

The Student practiced deploying a containerized ML model to Kubernetes, including creating a model, building a Flask API, and writing a Dockerfile. The session covered error handling, input validation, and Docker image configuration. The Student will complete the YAML file and test the deployment.

Kubernetes Deployment for ML Models

Flask API for Model Serving

Model Training and Persistence

Input Data Validation in APIs

Containerizing ML Models with Docker

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Student learned 18 days ago

The student and tutor reviewed functions to process lists of any, including calculating depth and removing elements. The student then worked on understanding expression trees and their evaluation using mutual recursion. The student will send notes for the next session, which will cover lambda and abstract functions.

List of Any Definition

Determining Depth of a List of Any

Atoms in Racket

Evaluating Expression Trees (Mutual Recursion)

Expression Trees

Removing Elements from a List of Any

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Student learned 18 days ago

The Tutor and Student reviewed advanced JavaScript concepts, including higher-order functions, callbacks, map, filter, and reduce methods. The Student was assigned practice questions related to these topics as homework. They scheduled their next session to continue practicing and increase the difficulty level.

Higher-Order Functions

Callbacks

Map Method

Filter Method

Reduce Method

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Student learned 21 days ago

The session involved an extra credit assignment focused on creating an ETL and ML pipeline for real-time fraud detection. The student and tutor reviewed the project requirements, data structure, and expected components. The student began setting up the project repository and folder structure, with plans to start filling in the code and addressing the 'todos' outlined in the assignment.

ETL Pipeline

ML Pipeline

Data Quality Issues

Git Branching Strategy

AI Prompt Engineering

Pydantic Schemas

Synthetic Data Generation

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Student learned 21 days ago

The session covered Bayesian statistics, including Poisson and Gamma distributions, and uniform distributions. The Student worked on problems involving finding posterior distributions with Gamma priors and uniform likelihoods. The Tutor assigned the Student the task of watching videos and creating cheat sheets for various distributions for future sessions.

Poisson Distribution

Gamma Distribution

Poisson-Gamma Conjugate Prior

Uniform Distribution

Posterior Distribution Calculation

Likelihood Function for Uniform Distribution

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Your data science tutor also teaches

Data Analysis

Data Analysis

Machine Learning

Machine Learning

Power BI

Power BI

SPSS

SPSS

Tableau

Tableau

Microsoft Excel

Microsoft Excel

Learner types for data science class

All Levels icon

All Levels

Home schooled icon

Home schooled

College icon

College

Adult / Professional icon

Adult / Professional

School icon

School

Teaching tools used by data science tutor

Digital whiteboard image

Digital whiteboard

Assessments image

Assessments

Presentations image

Presentations

Quizzes image

Quizzes

Practice worksheets image

Practice worksheets

Interactive data science classes

Open Q&A icon

Open Q&A

Pets are welcomed icon

Pets are welcomed

Parent feedback icon

Parent feedback

Chat for quick help icon

Chat for quick help

Mobile joining icon

Mobile joining

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Data Science tutors on Wiingy are vetted for quality

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