Mohit Kadwe
Computer Science Tutor for Python, Java, Web Development & More
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Mohit Kadwe
Masters 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
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/ lesson
Mohit - Know your tutor
Hello, I'm Mohit Kadwe, a Computer Science and Coding tutor with a Bachelors degree in Computer Engineering. My teaching philosophy focuses on making complex topics simple and engaging for students. I use interactive methods to ensure effective learning and student engagement. I specialize in teaching a variety of subjects like Java, Python, SQL, and more to students of all levels - school, college, and adult/professional learners. Let's embark on this learning journey together! Students can take my help for debugging any code, for their projects, assignments etc. I can take you though step-by-step to make you understand the concepts and feeling confident. I like to solve problems and i am sure you will find my lessons useful.
Programming tutor specialities
Australian Curriculum (AU)
Homework help
Debugging
Project help
Assignment help
Upskilling
Advanced Placement (AP) Program (USA)
Learner for programming class
Learning Disabilities
Computer Science for School students
Computer Science for College students
Home schooled
ADHD
ASD
Anxiety or Stress Disorders
Programming class overview
I am a Computer Science tutor with expertise in various programming languages and technologies. My teaching style is tailored to students of all levels, from school to adult/professional. I believe in an elaborative and structured approach, making learning hands-on and practical. I encourage collaboration and demonstrate concepts effectively. In my classes, I focus on enabling students to write efficient code by assigning small, real-world coding projects that spark creativity. I personalize each session based on the student's interests, whether it's building a game, website, or app. Additionally, I review the student's coding experience and preferences to create a customized learning experience. I am also accessible to help my students as and when they need any help. You can always reach out to me to ask any help. Lets start learning.
Hands-on learning
Students gain confidence applying coding skills to real projects.
Project-based learning for real-world skills
90% of students complete relevant coding projects.
Interactive debugging sessions
Students debug and improve their own code in real-time.
Your programming tutor also teaches
Computer Science
CSS
Databases
DOS
HTML
Java
Computer Science concepts taught by Mohit
The Student and Tutor collaborated on refining SAS code for analyzing experimental data, focusing on visualization and statistical significance. They interpreted graphs, identified errors, and adjusted code parameters. The Student will send example graphs and a summary of adjustments needed, aiming for a final code version to start writing their results section.
Code Modification and Testing
Iterative Development and Collaboration
Graph Interpretation and Adjustment
Data Comparison and Analysis
Error Identification and Resolution
The session involved the student and tutor debugging SAS code for hypothesis testing on provided data. The student reviewed the initial outputs and identified areas for further analysis, including feedback time differences between blocks and the potential correlation between EEG and EDA measures. Follow-up actions include the tutor adding comments to the code, addressing issues with labels, incorporating data validation steps, and providing a detailed explanation of the student's desired visualizations for section 11.
Physiological Data Analysis (EDA
HRV
EEG)
Graph Customization and Data Representation
Interpretation of Statistical Output and Graphs
Data Validation and Completeness
Data File Handling and Paths in SAS
SAS Code for Hypothesis Testing
The session involved reviewing the analysis plan for a dataset related to an adaptive learning experiment. The student provided the tutor with updated data files and explained the hypotheses to be tested, including performance metrics, physiological data (EDA, HRV, pupillometry), and EEG data. The next steps involve the tutor performing the data analysis, with a follow-up meeting scheduled to discuss the results.
Descriptive Statistics for Participant Demographics
System Usability Scale (SUS) Benchmarking
Analyzing Feedback Duration Across Blocks
Task Performance Accuracy Comparison
EDA and Task Load Correlation
Alpha and Theta EEG Activity
The Student and Tutor worked on debugging and deploying changes to a web application, specifically focusing on resolving database permission errors and deployment issues. The Student learned how to use the cloud platform to identify errors and apply migrations, as well as how to test builds locally before deploying. The next steps involve incorporating YouTube videos and Google Docs, and the tutor will set up a proxy to enable access.
Cloud-Based Development Assistance
Client-Side Caching
Build Process Validation
Role-Based Access Control (RBAC)
Database Migrations
API Proxies for External Resources
The student and tutor discussed and detailed specific hypotheses and their analytical requirements for a research project. The student provided an overview of planned analyses for various hypotheses involving physiological data, task difficulty, and performance metrics, with a plan to send detailed documentation.
Descriptive Statistics
Interaction Effects
Physiological Variables and Task Load
Brainwave Frequencies (Alpha & Theta)
Task Difficulty and Habituation
The Student and Tutor reviewed SAS code for analyzing feedback time data related to hypothesis 2, discussing data pre-processing, logistic regression models, and ordinal glimmix models. They determined key sections of the code for analyzing the data and planned to discuss the remaining hypotheses in the next session. The student will send additional information, and they scheduled a follow-up call to discuss the analysis of H1 A, B, C, and D.
Comparing Different Analytical Approaches
Assumption Checks in Statistical Modeling
Mixed Models for Continuous Data
Data Preprocessing
Logistic Regression Model for Binary Outcomes
Random Intercept in Statistical Models
Ordinal Regression Models
Teaching tools used by tutor
Android Studio
Xcode
Bitbucket
NetBeans
Jupyter Notebook
Dynamic programming classes
Weekend lessons
Record lessons
Parent feedback
Chat for quick help
Mobile joining
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