Pranav Saluja
From Beginner to Confident Developer | DSA, Java & Real-World Projects . Innovative Computer Science Guidance for You.




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Pranav Saluja
Bachelors degree
/ 55 min
Pranav - Know your tutor
I’m Pranav Saluja, a Computer Science tutor with a strong foundation in Data Structures & Algorithms, Java, and Full Stack Development. Over the past few years, I’ve worked closely with students from different backgrounds, helping them move from confusion to clarity and from theory to real confidence in coding. My teaching style is simple: break complex concepts into small, understandable pieces and build them step by step until everything makes sense. I focus heavily on fundamentals because I believe strong basics are what truly differentiate a good developer from an average one. Whether it’s understanding recursion, mastering problem-solving patterns, or writing clean and optimized code, I ensure that students don’t just memorize solutions but actually understand the “why” behind everything they do. I also emphasize thinking like an interviewer — writing code that is not just correct, but also efficient and explainable. My sessions are highly interactive. I encourage students to ask questions, think out loud, and even make mistakes — because that’s where real learning happens. Instead of spoon-feeding solutions, I guide them with hints and structured thinking approaches so they can solve problems independently. This builds long-term confidence, which is crucial for cracking interviews and excelling in real-world development. I also integrate real-world examples and practical applications into my teaching. Whether it's building projects using React and backend technologies or preparing for coding interviews, I ensure that learning is aligned with industry expectations. I adapt my pace and teaching strategy based on each student’s level, making sure no one feels left behind or overwhelmed.
Meet Pranav
Programming tutor specialities
Upskilling
Project help
Exam prep
Debugging
Homework help
Paired coding
Code Review
AI modules
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Focused on real-world coding applications
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Your programming tutor also teaches
Computer Science
Databases
Web Development

Computer Science concepts taught by Pranav
The Tutor and Student reviewed data visualization techniques using Matplotlib for plotting sensor data (temperature, vibration, load). They practiced creating labeled graphs and focused on the implementation of highlighting anomalies by plotting red dots, preparing for further discussion on metrics and assignment completion.
Handling Multiple Data Series
Introduction to Libraries: `random`
Customizing Plot Appearance
Matplotlib Plotting Basics
Visualizing Anomalies
The Student and Tutor completed the Python coding for a sensor monitoring system, covering sensor data generation, anomaly detection, and CSV logging with real-time timestamps and automated data collection. They also implemented functions for statistical analysis of the logged data and received an introduction to Matplotlib for data visualization. The next session will focus on completing the visualization aspect of the assignment.
Real-time Data Logging to CSV
Automating Data Generation & Logging
Calculating Basic Data Statistics
Introduction to Data Visualization with Matplotlib
Student and Tutor covered Python file handling, including writing, reading, and appending to text files, as well as working with CSV files. They also learned about functions and applied these concepts to build functions for generating mock sensor data and detecting anomalies, directly addressing the requirements of the Student's assignment. The plan is to complete the assignment in the next class and then move on to data visualizations.
Introduction to File Handling & Data Persistence
Working with CSV Files in Python
Python Functions: Reusability and Structure
Simulating Sensor Data & Anomaly Detection
The Tutor and Student began reviewing a Python assignment to build a robotic arm monitoring system. They covered simulating sensor data using Python's `random` module, implementing anomaly detection with conditional logic, and discussed data logging into JSON/CSV files and the use of the `matplotlib` library for visualization. Future sessions will focus on file handling and data visualization theory.
Python Lists for Data Collection
Introduction to Data Visualization with Matplotlib
Structured Data Logging (JSON & CSV Concepts)
Anomaly Detection using Control Logic
Simulating Sensor Data with Python's `random` Module
Robotic Arm Monitoring System Overview
The Student and Tutor reviewed key concepts of distributed databases in preparation for an upcoming exam. They covered the definition, problems with centralized systems, advantages and disadvantages of distributed databases, and the distinction between distributed processing and distributed databases. The session also detailed the components of a distributed database system and different types of transparency, with the Tutor sharing notes for revision.
Components: Transaction Processor (TP) & Data Processor (DP)
Distributed Processing vs. Distributed Databases
Distributed Databases: Core Concepts & Rationale
Types of Transparency: Location
Fragmentation
Replication
Distributed Database Transparency
The Student and Tutor reviewed various database concurrency control mechanisms, focusing on Optimistic Concurrency Control (OCC) and its three phases. They then discussed database recovery management, covering causes of failure, the role of transaction logs, and key logging concepts such as Write-Ahead Logging and checkpoints. The session concluded by comparing Right-Through and Deferred-Write recovery techniques, and defining serializability, with a plan to briefly cover deadlock prevention schemes in the next class.
Optimistic Concurrency Control (OCC)
Phases of Optimistic Concurrency Control
Comparison of Concurrency Control Methods
Database Recovery Management Principles
Transaction Logging & Key Recovery Concepts
Recovery Procedures: Write-Through vs. Deferred Write
Serializability
Teaching tools used by tutor
PyCharm
Jupyter Notebook
Visual Studio Code
Postman
Git & GitHub

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