Dr. Gurinderjeet Kaur
Data Analysis & Power BI training that’s hands-on & engaging
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Dr. Gurinderjeet Kaur
Doctorate 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 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 Science tutor skills
Paired coding
New Zealand Curriculum - NZC (NZ)
Machine learning
Statistical analysis
Assignment help
Data visualization
GCSE (UK)
A-Levels (UK)
Predictive modeling
Australian Curriculum (AU)
Project help
Upskilling
Learner types for data science class
Adult / Professional
College
Home schooled
School
All Levels
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.
Your data science tutor also teaches
Data Analysis
Machine Learning
Power BI
SPSS
Tableau
Microsoft Excel

Data Science concepts taught by Dr. Gurinderjeet
The Student worked on a merging sorted arrays problem, focusing on in-place modification and pointer manipulation. The Tutor explained the logic behind starting from the end of the arrays and using three pointers for comparison and placement. The Student will continue practicing similar algorithmic problems.
Merging Sorted Arrays
Handling Remaining Elements
Comparison and Assignment
Pointer Placement
The Student and Tutor reviewed probability concepts, including independent events, Venn diagrams, conditional probability, tree diagrams, and combinations. The Student practiced solving problems related to these concepts. They were assigned to review the material and prepare for the next session.
Combinations: Selecting Groups When Order Doesn't Matter
Probability Basics and the Lottery
Independent Events and Combined Probability
Venn Diagrams and Probability
Conditional Probability
Tree Diagrams and Joint Probabilities
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.
Flask API for Model Serving
Containerizing ML Models with Docker
Kubernetes Deployment for ML Models
Model Training and Persistence
Input Data Validation in APIs
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.
Atoms in Racket
Evaluating Expression Trees (Mutual Recursion)
Expression Trees
Determining Depth of a List of Any
List of Any Definition
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.
Filter Method
Higher-Order Functions
Callbacks
Map Method
Reduce Method
Teaching tools used by data science tutor
Assessments
Digital whiteboard
Quizzes
Practice worksheets
Presentations
Interactive data science classes
Note taking
Open Q&A
Chat for quick help
Mobile joining
Parent feedback
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