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
Advanced Placement (AP) Program (USA)
Debugging
New Zealand Curriculum - NZC (NZ)
Business intelligence
Data engineering
Upskilling
Statistical analysis
Job readiness
Machine learning
Next Generation Science Standards - NGSS (USA)
Predictive modeling
Assignment help
Learner types for data science class
Home schooled
College
School
Adult / Professional
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
Flexible Scheduling
Allows 1h early scheduling
Allows 1h early rescheduling
Can wait for 20 mins after joining

10 day Refund
Free Tutor Swap

Data Science concepts taught by Dr. Gurinderjeet
The student and tutor worked on debugging a MATLAB script for numerical equation solving, specifically addressing issues with the `f0` function and handling invalid input ranges. They implemented strategies for element-wise division and introduced error handling for non-positive values of `x3`, and developed a multi-interval search strategy for `x4`. The next session will focus on curve fitting and understanding lecture slides.
Element-wise Operations in MATLAB
Handling Numerical Instability with Safe Intervals
Iterative Refinement and Interval Testing for Root Finding
Robust Root Finding with Error Handling
The student and tutor worked on practice problems for an upcoming third-quarter exam, which is a written-based test. They focused on solving coding problems manually without a compiler, emphasizing accuracy and the avoidance of common errors. The tutor planned to share detailed solutions and assign more practice problems for the next session.
Exam Strategy: Avoiding Rushing and Silly Mistakes
Data Structures: Arrays and Their Manipulation
Understanding Loops and Conditional Statements
Code Execution Without a Compiler: Manual Simulation
The student and tutor reviewed Python programming exercises related to conditional statements and then transitioned to learning about dictionaries. They discussed dictionary creation, accessing elements, and iterating through keys and values, with the student practicing these concepts and preparing to work through provided files and the textbook.
Case-Insensitive Username Checking
Dictionary Comprehensions
Iterating Through Dictionaries
Python Dictionaries: Key-Value Pairs
The Tutor guided the Student through understanding and applying MATLAB's `fsolve` function for solving systems of nonlinear equations, particularly in the context of chemical reaction engineering. They also introduced the "tearing method" for complex systems and debugged the Student's code, identifying an error in the Jacobian matrix calculation and suggesting a review of the equations.
Reactor Modeling and Mass Balance
Nonlinear Equation Solvers in MATLAB
Tearing Method for Complex Systems
fsolve Function in MATLAB
The Tutor and Student worked on implementing the Secant method in MATLAB to solve a system of non-linear equations. The discussion involved understanding the theoretical underpinnings of the method, translating lecture slide concepts into code, and debugging specific implementation details related to Jacobian approximation. They planned to continue working on the assignment and explore other methods in future sessions.
Jacobian Matrix Approximation via Finite Differences
Secant Method for Multiple Variables
Iterative Refinement of Solutions
The tutor and student reviewed concepts related to multiple linear regression, specifically Extra Sum of Squares (ESS), marginal effects, and hypothesis testing (Global F-test, marginal tests). They discussed the decomposition of sums of squares and the F-statistic calculation, with plans for the student to review the notes.
Marginal Effect and Marginal F-Test
Global F-Test
Sum of Squares Decomposition
Extra Sum of Squares (ESS)
Teaching tools used by data science tutor
Practice worksheets
Digital whiteboard
Assessments
Quizzes
Presentations
Interactive data science classes
Mobile joining
Record lessons
Parent feedback
Chat for quick help
Pets are welcomed
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