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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

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Each lesson is 55 min

50 lessons


20% off

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30 lessons


15% off

/ lesson

20 lessons


10% off

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10 lessons


5% off

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5 lessons


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1 lessons


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Rated 5 out of 5 stars.
★★★★★
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Highly skilled & top-rated
86 ratings
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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 Science tutor skills

Paired coding icon

Paired coding

New Zealand Curriculum - NZC (NZ) icon

New Zealand Curriculum - NZC (NZ)

Machine learning icon

Machine learning

Statistical analysis icon

Statistical analysis

Assignment help icon

Assignment help

Data visualization icon

Data visualization

GCSE (UK) icon

GCSE (UK)

A-Levels (UK) icon

A-Levels (UK)

Predictive modeling icon

Predictive modeling

Australian Curriculum (AU) icon

Australian Curriculum (AU)

Project help icon

Project help

Upskilling icon

Upskilling

Learner types for data science class

Adult / Professional icon

Adult / Professional

College icon

College

Home schooled icon

Home schooled

School icon

School

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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

Data Analysis

Machine Learning

Machine Learning

Power BI

Power BI

SPSS

SPSS

Tableau

Tableau

Microsoft Excel

Microsoft Excel

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Data Science concepts taught by Dr. Gurinderjeet

Student learned about 1 month ago

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

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Student learned about 1 month ago

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

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Student learned about 1 month 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 about 1 month 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.

Flask API for Model Serving

Containerizing ML Models with Docker

Kubernetes Deployment for ML Models

Model Training and Persistence

Input Data Validation in APIs

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Student learned about 2 months 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.

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

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Student learned about 2 months 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.

Filter Method

Higher-Order Functions

Callbacks

Map Method

Reduce Method

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Teaching tools used by data science tutor

Assessments image

Assessments

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Digital whiteboard

Quizzes image

Quizzes

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Practice worksheets

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Presentations

Interactive data science classes

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Note taking

Open Q&A icon

Open Q&A

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Chat for quick help

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Mobile joining

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Parent feedback

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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.