Swati Raiwani

About

Mathematics professional transitioning into data analytics and data science with a strong foundation in Python, SQL, statistics, machine learning, and data visualization. Experienced in mathematics teaching, analytical problem solving, and building data-driven projects for predictive modeling and insights communication.

Experience

• Online Mathematics Teacher
  BrainX Learning (Astro Edu Innovations FZ LLC)
  Jun 2026 – Present
• PGT Mathematics Teacher
  HSV Global International School
  Feb 2026 – Jun 2026
• Subject Matter Expert – Mathematics & Quality Analyst
  ACS Networks Technologies
  Feb 2024 – Feb 2026

Education

• Master of Science (M.Sc.) – Mathematics
  Hemwati Nandan Bahuguna Garhwal University
  Mathematics · 2017
• Bachelor of Science (B.Sc. Hons.) – Mathematics
  University of Delhi
  Mathematics · 2015
• Master of Science (M.Sc.) – Data Science and Analytics
  Indira Gandhi National Open University (IGNOU)
  Data Science and Analytics · 2026
• Bachelor of Education (B.Ed.)
  Sanskriti Institute of Advanced Studies (SIAS), Roorkee

Skills

• SQL
• Google Colab
• Teaching
• Mathematics
• Python
• Statistics
• Classification
• Data Cleaning
• Feature Engineering
• Matplotlib
• Model Evaluation
• NumPy
• Pandas
• scikit-learn
• Seaborn
• Plotly
• Random Forest
• Research
• Dashboard Development
• Excel
• XGBoost
• Regression
• SMOTE

Projects

• NLP Sentiment Analysis Project
  Python, NLP, Machine Learning

  Developed a Natural Language Processing pipeline to classify text sentiments. Performed text preprocessing, tokenization, feature extraction, and model evaluation. Applied machine learning algorithms for sentiment classification.

• Sales / Time Series Forecasting and Exploratory Data Analysis
  Python, ARIMA, Prophet

  Conducted exploratory data analysis to identify trends, patterns, and business insights from historical datasets. Performed data cleaning, statistical analysis, and visualization using Python libraries. Developed forecasting models using ARIMA and Prophet for future trend prediction.

• Customer Churn Prediction
  Python, Scikit-learn, SMOTE, Random Forest, XGBoost

  Built a classification model to predict customer churn using machine learning techniques. Performed data preprocessing, exploratory data analysis, feature engineering, and model evaluation. Handled class imbalance using SMOTE and compared multiple models including Random Forest and XGBoost.

Courses & certifications

• Data Visualization and Dashboards with Excel and Cognos · IBM, Coursera · 2025
• Python for Data Science, AI & Development · IBM, Coursera · 2025
• Python Project for Data Science · IBM, Coursera · 2025
• Databases and SQL for Data Science with Python · IBM, Coursera · 2025
• Data Analysis with Python · IBM, Coursera · 2025
• Data Visualization with Python · IBM, Coursera · 2025
• Introduction to Data Analytics · IBM, Coursera · 2024
• Excel Basics for Data Analysis · IBM, Coursera · 2024