π Mathematics Graduate | π Data Analyst & Data Scientist
π‘ Strong interest in data analysis, statistics, and machine learning
- Programming: Python
- Data Analysis: SQL, Excel, Pandas, NumPy
- Visualization: Power BI, Matplotlib, Seaborn
- Machine Learning: Linear Regression, KNN Regression, Decision Tree, SVM, Random Forest (scikit-learn)
- Concepts: EDA, Statistics, Data Cleaning
- Tools: GitHub, Jupyter Notebook
(Conceptual understanding / coursework level)
- Neural Networks
- Deep Learning fundamentals
- Natural Language Processing (NLP)
- Generative AI concepts
- Unsupervised Learning (Clustering basics)
- πΉ Exploratory Data Analysis (EDA) β Data cleaning, visualization, and insights using Python
- πΉ Regression Model Comparison β Linear vs KNN Regression evaluated using RMSE, MAE, and RΒ²
- πΉ Power BI Dashboard β Interactive dashboard for business performance analysis
π Check my repositories to explore project code and notebooks.
- Entry-level Data Analyst / Associate Data Analyst roles
- Internships in Data Analytics / Data Science
- π LinkedIn: https://www.linkedin.com/in/mohdwalidansari