Anjana

Years of Experience in BTech Tuition

9

Years of Experience in Class I-V Tuition

9

Years of Experience in Coding for Kids

3

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.

Years of Experience in Data Science Classes

9

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.