Garimidi sivasree
Interactive Data Analysis & Machine Learning lessons with problem solving
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Garimidi sivasree
Bachelors degree
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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
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/ lesson
1 lessons
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/ lesson
About your data science tutor
"Master statistics with expert guidance in SPSS, Excel, Python, Stata, machine learning, deep learning, and LLMs. Achieve practical, real-world skills through interactive online tutoring tailored to your learning style!" Hello, I am Dr. Siva Sree. I have completed my Ph.D. in the field of human resources. I have more than 5 years of research experience. I have more than two years of teaching experience in business administration subjects. Along with this, i also teach subjects like statistics and mathematics. I teach statistics and predictive analytics, combining theoretical knowledge with practical applications. My approach focuses on using tools like SPSS, Excel, Python, and Stata to provide hands-on learning experiences. Whether it's understanding statistical concepts or building predictive models, I ensure that students gain both foundational knowledge and the skills needed to analyze real-world data effectively. I have research publications in several national and international journals. I have been currently working as an assistant professor. I teach the concepts in simple and easily understandable ways while laying emphasis on connecting theory to practice. I focus on interactive, hands-on learning, where students apply these tools to real-world datasets. This approach ensures not only theoretical understanding but also practical skills development.
Garimidi graduated from JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY


Data Science tutor skills
Data engineering
Predictive modeling
Assignment help
Statistical analysis
Project help
Upskilling
Data visualization
Learner types for data science class
All Levels
Anxiety or Stress Disorders
Adult / Professional
CollegeSchool
Data sciece class overview
As an online tutor specializing in statistics and mathematics, my teaching methodology is designed to make complex statistical and math concepts accessible, practical, and engaging. Statistics can be an intimidating subject for many learners, but with the right approach, it becomes a powerful tool for solving real-world problems. My goal as an online tutor is to empower students with the confidence and skills they need to master statistics, whether they are beginners or advanced learners. Student-Centered Approach At the core of my teaching methodology is a student-centered approach. I recognize that every student is unique, with different learning styles, needs, and levels of understanding. As an online tutor, I take the time to assess each student’s strengths and weaknesses before tailoring my lessons to meet their specific needs. Whether you are preparing for an exam, learning statistics or any mathematical concepts for the first time, or looking to deepen your understanding for professional or academic purposes, I will develop a personalized learning plan that is aligned with your goals. In conclusion, my teaching methodology as an online statistics and mathematics tutor is centered on creating an interactive, personalized, and practical learning experience. By combining hands-on learning with statistical tools, real-world applications, problem-solving exercises, and continuous feedback, I help students build the skills they need to succeed in both their academic and professional endeavors. Whether you’re a beginner looking to build a solid foundation or an advanced learner seeking to deepen your understanding, my teaching approach is designed to help you master statistics with confidence and ease.
Your data science tutor also teaches
Data Analysis
Machine Learning
Python
SPSS
Statistics
Microsoft Excel

Data Science concepts taught by Garimidi
The student and tutor worked through practice problems related to hypothesis testing and confidence intervals for proportions. They reviewed p-values, error types (Type I and Type II), and assumptions for statistical tests. The session concluded with formula-based calculations for sample size and combined mean/standard deviation.
Two Proportion Hypothesis Testing
Pooled Sample Proportion
Interpreting P-values
Confidence Intervals and Hypothesis Testing
Type I and Type II Errors
Sample Size Calculations
Combined Mean and Standard Deviation
The Student and Tutor reviewed hypothesis testing, Z-test statistics, Type I and Type II errors, and confidence intervals. The Student practiced problems related to these concepts. The Tutor assigned practice problems for the Student to complete before the next session.
P-value and Significance Level (α)
Confidence Intervals
Power of a Test
Type I and Type II Errors
Z-Test Statistic
Hypothesis Testing: Null and Alternate Hypotheses
The session covered normal distributions, the 68-95-99.7 rule, and standard deviation calculations. The Student practiced applying these concepts to probability problems. They will review chapter theory and examples in preparation for their exam, and potentially schedule another session to cover specific questions.
Standard Deviation of the Difference Between Two Random Variables
Z-Score Calculation in Difference of Means
68-95-99.7 Rule (Empirical Rule)
Conditions for Using a Normal Model for Proportions
Standard Deviation of a Sample Proportion
Calculating Probabilities Using the Normal Model for Proportions
The Student received instruction on hypothesis testing, including null and alternative hypotheses, significance levels, and one-tailed versus two-tailed tests. The Student then worked through a problem involving strikeout rates in baseball, calculating the test statistic and interpreting the P-value. The next session will focus on reviewing earlier chapters of the textbook.
Null Hypothesis (H₀)
Alternate Hypothesis (H₁ or Hₐ)
Significance Level (α)
P-value
Critical Region (Rejection Region)
One-Tailed vs. Two-Tailed Tests
Standard Error
The session focused on random variables, distinguishing between continuous and discrete variables, and calculating expected values and standard deviations for discrete random variables. The Student practiced applying these concepts to a word problem involving expected profit. As homework, the Student will attempt additional problems related to discrete and continuous random variables, and is expected to ask any questions in the next session.
Probability Models for Discrete Random Variables
Applying Expected Value to Decision-Making
Variance and Standard Deviation of a Discrete Random Variable
Random Variables: Discrete vs. Continuous
Expected Value (Mean) of a Discrete Random Variable
Slope Interpretation
Coefficient of Determination (R²)
Regression Equation
Linear Regression
Correlation Coefficient (R)
Correlation
Scatter Plots
Teaching tools used by data science tutor
Digital whiteboard
Quizzes
Practice worksheets
Assessments
Presentations
Interactive data science classes
Note taking
Mobile joining
Pets are welcomed
Record lessons
Open Q&A
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