Skills
I've developed these skills in Data Science and Machine Learning through coursework and personal projects.
Python Programming
Advanced proficiency in Python for statistical analysis, machine learning, and automation.
Key Libraries:
NumPy
Pandas
Scikit-learn
Statsmodels
Machine Learning
Skilled in developing and evaluating ML models for various statistical problems.
Techniques:
Regression
Classification
Clustering
Feature Engineering
Data Visualization
Creating insightful visualizations to communicate statistical findings effectively.
Tools:
Matplotlib
Seaborn
Plotly
Tableau
Power BI
Computer Vision
Understanding and applying techniques to enable computers to "see" and interpret visual data.
Areas & Tools:
OpenCV
Image Classification
Object Detection
CNNs
Deep Learning
Experience with neural networks and deep learning frameworks for complex problems.
Frameworks:
TensorFlow
Keras
PyTorch
Natural Language Processing
Applying statistical methods to text data for analysis and modeling.
Techniques:
Text Classification
Sentiment Analysis
Word Embeddings
Transformers
Data Wrangling
Cleaning, transforming and preparing raw data for analysis.
Tools:
Pandas
SQL
Excel
Statistical Analysis
- Probability & Inference
- Hypothesis Testing
- Regression Analysis
- Time Series Analysis
- Experimental Design
Statistical Software
R Programming
Python (Stats)
Minitab
SPSS