Dr. Gurinderjeet Kaur
Data Analysis & Power BI training that’s hands-on & engaging
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Dr. Gurinderjeet Kaur
Doctorate degree
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


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)
Project help
Predictive modeling
Australian Curriculum (AU)
Provincial-specific curriculum (CA)
Paired coding
GCSE (UK)
Data visualization
Assignment help
Data engineering
Statistical analysis
Machine learning

Data Science concepts taught by Dr. Gurinderjeet
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
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
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
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
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
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
Your data science tutor also teaches
Data Analysis
Machine Learning
Power BI
SPSS
Tableau
Microsoft Excel
Learner types for data science class
All Levels
Home schooled
College
Adult / Professional
School
Teaching tools used by data science tutor
Digital whiteboard
Assessments
Presentations
Quizzes
Practice worksheets
Interactive data science classes
Open Q&A
Pets are welcomed
Parent feedback
Chat for quick help
Mobile joining
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