Pinned Repositories
acceleratoRs
Data science and AI solution accelerator suite that provides templates for prototyping, reporting, and presenting data science analytics of specific domains
applied_data_science_with_python
This repository contains my work while completing the specialization created by University of Michigan on Coursera. The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have basic a python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.
LeetCode
:pencil: Python / C++ 11 Solutions of All 468 LeetCode Questions
Segmentation-for-Credit-Risk-Prediction
Risk-based Segmentation of Bank Credit Card Customers
study_group
study group for MPO
kevinwowo's Repositories
kevinwowo/acceleratoRs
Data science and AI solution accelerator suite that provides templates for prototyping, reporting, and presenting data science analytics of specific domains
kevinwowo/applied_data_science_with_python
This repository contains my work while completing the specialization created by University of Michigan on Coursera. The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have basic a python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.
kevinwowo/LeetCode
:pencil: Python / C++ 11 Solutions of All 468 LeetCode Questions
kevinwowo/Segmentation-for-Credit-Risk-Prediction
Risk-based Segmentation of Bank Credit Card Customers
kevinwowo/study_group
study group for MPO