Tools: Jupyter / JupyterLab, GitHub, R Studio, and Watson Studio 

Libraries: Pandas, NumPy, Matplotlib, Seaborn, Folium, ipython-sql, Scikit-learn, ScipPy, etc. 

Projects: random album generator, predict housing prices, best classifier model, Predicting successful rocket landing, dashboa rd and interactive map

Utilizes statistical and machine learning techniques and high-performance data architectures and technologies such as SAS, R, Python, Spark, and SPSS, to address business problems and analyze large, unstructured data sets.

Technical Skills:

• Artificial Intelligence
• Bokeh
• Classification
• Clustering
• Data Analysis
• Database
• Data Science
• Data Visualization
• Db2
• Folium
• Foursquare
• IBM Cloud
• Jupyter
• Location
• Machine Learning
• Matplotlib
• Methodology
• ML
• Notebook
• Numpy
• Pandas
• Python
• Recommender Systems
• Regression
• RStudio
• Scikit-learn
• SCIPy
• Seaborn
• SQL
• Studio
• Watson
• Zeppelin