loading="eager" fetchpriority="high" decoding="sync" /> Sector 17, Panchkula, India - 134109.
9 yrs of Exp
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Hindi Mother Tongue (Native)
English Basic
Jaypee university 2016
Master of Engineering - Master of Technology (M.E./M.Tech.)
IBM 2022
Post graduate in data analytics
Sector 17, Panchkula, India - 134109
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Class Location
Online class via Zoom
Student's Home
Tutor's Home
Years of Experience in BTech Tuition
9
BTech Electrical & Electronics subjects
Analog And Digital Communication, Applications of Digital Signal Processing (DSP), Microprocessors, Communication Systems, Circuit Theory, Discrete Fourier Transforms And Digital Filter Design, Computer Networks, Control Systems, Signal Processing
BTech Electrical & Communication
Spread Spectrum Communication, Microcontrollers and Applications, Processors and Controllers, Digital Electronics, Object Oriented Programming, Semiconductor Device Physics, Error Control Coding, Linear Systems & Signals, Embedded System Design, Wireless Sensor Networks, Linear and Digital Control Systems, Mobile Communication, Wireless Communication, (VLSI/ULSI) Very Large Scale Integration Process Technology, Information Theory and Coding
BTech Computer Science subjects
Computer Networks, Design Of Digital Systems, Database Management Systems, Big Data Analytics, Wireless Networks
BTech Branch
BTech Electrical & Electronics, BTech Electrical & Communication Engineering, Telecom Engineering, BTech Computer Science Engineering
Type of class
Regular Classes
Class strength catered to
One on one/ Private Tutions, Group Classes
Taught in School or College
Yes
Class Location
Online class via Zoom
Student's Home
Tutor's Home
Years of Experience in Class I-V Tuition
9
Board
CBSE, ICSE, State, International Baccalaureate
Subjects taught
Mathematics, Computer Science, Science, Computers
Taught in School or College
Yes
Class Location
Online class via Zoom
Student's Home
Tutor's Home
Years of Experience in Coding for Kids
3
Age groups catered to
6 to 12 years
Class Location
Online class via Zoom
Student's Home
Tutor's Home
Years of Experience in Microsoft Power BI classes
9
Teaching Experience in detail in Microsoft Power BI classes
I have extensive experience teaching Microsoft Power BI, guiding learners through every stage of mastering this powerful business intelligence tool. My teaching covers: Introduction to Power BI: Explaining its importance in data visualization and decision-making. Data Connectivity: Demonstrating how to import data from various sources like Excel, SQL Server, and cloud services. Data Transformation: Teaching data cleaning and shaping using Power Query Editor. Data Modeling: Creating relationships, using DAX (Data Analysis Expressions) for calculated columns, measures, and KPIs. Visualization: Designing interactive dashboards with charts, tables, slicers, and custom visuals. Publishing and Sharing: Guiding students on how to publish reports to the Power BI Service, set up gateways, and collaborate through workspaces. Real-World Projects: Incorporating hands-on projects, like sales analysis, customer segmentation, and retail data dashboards, to give practical exposure. I tailor lessons to suit beginners and advanced users, ensuring concepts are clear and applicable to real-world business scenarios.
Class Location
Online class via Zoom
Student's Home
Tutor's Home
Years of Experience in Data Science Classes
9
Data science techniques
Python
Teaching Experience in detail in Data Science Classes
I have extensive experience teaching Python for Data Science, focusing on equipping learners with both fundamental concepts and advanced techniques. My teaching approach covers the full data science pipeline, including: Introduction to Python: Explaining Python's role in data science, setting up Jupyter notebooks, and using essential libraries like NumPy, Pandas, Matplotlib, and Seaborn. Data Manipulation: Teaching data cleaning, wrangling, and transformation using Pandas — handling missing data, merging datasets, and performing aggregations. Exploratory Data Analysis (EDA): Guiding learners through data visualization techniques to uncover patterns and insights, using libraries like Matplotlib and Seaborn. Statistical Analysis: Introducing statistical concepts like mean, median, standard deviation, correlation, and hypothesis testing, all implemented with Python. Machine Learning: Covering supervised and unsupervised learning techniques using Scikit-learn — including regression, classification, clustering, and model evaluation. Feature Engineering & Selection: Teaching how to preprocess data — scaling, encoding categorical variables, and selecting relevant features for model training. Model Building & Evaluation: Explaining train-test splits, cross-validation, hyperparameter tuning, and performance metrics like accuracy, precision, recall, and F1 score. Real-World Projects: Assigning hands-on projects — such as predicting house prices, classifying customer segments, and analyzing crime data — to solidify concepts. Visualization & Reporting: Integrating Python with tools like Matplotlib, Seaborn, and Plotly to create insightful, interactive visualizations. Version Control & Collaboration: Introducing Git and GitHub for collaborative data science projects. I focus on making complex concepts simple by using real-world datasets and practical examples, ensuring learners can confidently apply Python in their data science careers.
Upcoming Live Classes
1. Which BTech branches do you tutor for?
BTech Electrical & Electronics, BTech Electrical & Communication Engineering, Telecom Engineering and others
2. Do you have any prior teaching experience?
Yes
3. Which classes do you teach?
I teach BTech Tuition, Class I-V Tuition, Coding for Kids, Data Science and Microsoft Power BI Classes.
4. Do you provide a demo class?
Yes, I provide a free demo class.
5. How many years of experience do you have?
I have been teaching for 9 years.
Answered on 11/03/2020
Class Location
Online class via Zoom
Student's Home
Tutor's Home
Years of Experience in BTech Tuition
9
BTech Electrical & Electronics subjects
Analog And Digital Communication, Applications of Digital Signal Processing (DSP), Microprocessors, Communication Systems, Circuit Theory, Discrete Fourier Transforms And Digital Filter Design, Computer Networks, Control Systems, Signal Processing
BTech Electrical & Communication
Spread Spectrum Communication, Microcontrollers and Applications, Processors and Controllers, Digital Electronics, Object Oriented Programming, Semiconductor Device Physics, Error Control Coding, Linear Systems & Signals, Embedded System Design, Wireless Sensor Networks, Linear and Digital Control Systems, Mobile Communication, Wireless Communication, (VLSI/ULSI) Very Large Scale Integration Process Technology, Information Theory and Coding
BTech Computer Science subjects
Computer Networks, Design Of Digital Systems, Database Management Systems, Big Data Analytics, Wireless Networks
BTech Branch
BTech Electrical & Electronics, BTech Electrical & Communication Engineering, Telecom Engineering, BTech Computer Science Engineering
Type of class
Regular Classes
Class strength catered to
One on one/ Private Tutions, Group Classes
Taught in School or College
Yes
Class Location
Online class via Zoom
Student's Home
Tutor's Home
Years of Experience in Class I-V Tuition
9
Board
CBSE, ICSE, State, International Baccalaureate
Subjects taught
Mathematics, Computer Science, Science, Computers
Taught in School or College
Yes
Class Location
Online class via Zoom
Student's Home
Tutor's Home
Years of Experience in Coding for Kids
3
Age groups catered to
6 to 12 years
Class Location
Online class via Zoom
Student's Home
Tutor's Home
Years of Experience in Microsoft Power BI classes
9
Teaching Experience in detail in Microsoft Power BI classes
I have extensive experience teaching Microsoft Power BI, guiding learners through every stage of mastering this powerful business intelligence tool. My teaching covers: Introduction to Power BI: Explaining its importance in data visualization and decision-making. Data Connectivity: Demonstrating how to import data from various sources like Excel, SQL Server, and cloud services. Data Transformation: Teaching data cleaning and shaping using Power Query Editor. Data Modeling: Creating relationships, using DAX (Data Analysis Expressions) for calculated columns, measures, and KPIs. Visualization: Designing interactive dashboards with charts, tables, slicers, and custom visuals. Publishing and Sharing: Guiding students on how to publish reports to the Power BI Service, set up gateways, and collaborate through workspaces. Real-World Projects: Incorporating hands-on projects, like sales analysis, customer segmentation, and retail data dashboards, to give practical exposure. I tailor lessons to suit beginners and advanced users, ensuring concepts are clear and applicable to real-world business scenarios.
Class Location
Online class via Zoom
Student's Home
Tutor's Home
Years of Experience in Data Science Classes
9
Data science techniques
Python
Teaching Experience in detail in Data Science Classes
I have extensive experience teaching Python for Data Science, focusing on equipping learners with both fundamental concepts and advanced techniques. My teaching approach covers the full data science pipeline, including: Introduction to Python: Explaining Python's role in data science, setting up Jupyter notebooks, and using essential libraries like NumPy, Pandas, Matplotlib, and Seaborn. Data Manipulation: Teaching data cleaning, wrangling, and transformation using Pandas — handling missing data, merging datasets, and performing aggregations. Exploratory Data Analysis (EDA): Guiding learners through data visualization techniques to uncover patterns and insights, using libraries like Matplotlib and Seaborn. Statistical Analysis: Introducing statistical concepts like mean, median, standard deviation, correlation, and hypothesis testing, all implemented with Python. Machine Learning: Covering supervised and unsupervised learning techniques using Scikit-learn — including regression, classification, clustering, and model evaluation. Feature Engineering & Selection: Teaching how to preprocess data — scaling, encoding categorical variables, and selecting relevant features for model training. Model Building & Evaluation: Explaining train-test splits, cross-validation, hyperparameter tuning, and performance metrics like accuracy, precision, recall, and F1 score. Real-World Projects: Assigning hands-on projects — such as predicting house prices, classifying customer segments, and analyzing crime data — to solidify concepts. Visualization & Reporting: Integrating Python with tools like Matplotlib, Seaborn, and Plotly to create insightful, interactive visualizations. Version Control & Collaboration: Introducing Git and GitHub for collaborative data science projects. I focus on making complex concepts simple by using real-world datasets and practical examples, ensuring learners can confidently apply Python in their data science careers.
Answered on 11/03/2020
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Panchkula Extension, Panchkula 
