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Use a complete set of open source tools for data science in Python, including the Jupyter Notebook, NumPy, Pandas, Seaborn, scikit-learn, Colab, and many others. Cover the various phases of exploratory data analysis: importing data, cleaning and transforming data, algorithmic thinking, grouping, aggregation, reshaping, visualization, time series, statistical modeling, and data exploration and communication of results.

Jupyter Notebook 50.74% Python 0.02% HTML 49.23%

data-science-with-python's Introduction

Hi, I'm Harsh 👋🏻 👨🏻‍💻

I’m a budding Data Scientist adept in collecting, analyzing, and transforming data into insights. My background in Information Technology and Data Analytics provides me with extensive analytical skills to wrestle with large & complex data. I am currently pursuing a Master of Science degree in Business Analytics at Santa Clara University and my passion for Data Science allows me to provide data-driven, action-oriented solutions to challenging business problems.

With deep expertise in Machine Learning, Data Mining, and Statistical Analysis, I’m also proficient in Marketing Analytics, NLP, Deep Learning, and Data Visualization. I am excited to walk the uncanny valley of research and development, and I enjoy learning new things while thriving in challenging environments.

The world today is facing a situation of data deluge, and I believe, through my passion and skills, I can unlock its significant potential and aid businesses into efficient forecasting and planning.

When I’m not studying/working 📚, I love to travel ✈️, explore the world 🏕️, sketch 🎨 and workout 🏋️‍♂️.