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data science, statistics and machine learning

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# DataCamp Course

### Introduction to Data Visualization with Python  (Data_Visualization.ipynb)

Course Description
This course extends Intermediate Python for Data Science to provide a stronger foundation in data visualization in Python. The course provides a broader coverage of the Matplotlib library and an overview of Seaborn (a package for statistical graphics). Topics covered include customizing graphics, plotting two-dimensional arrays (e.g., pseudocolor plots, contour plots, images, etc.), statistical graphics (e.g., visualizing distributions & regressions), and working with time series and image data.

Certificate Link:https://www.datacamp.com/statement-of-accomplishment/course/94650feffd4bc66c6ec850d428773174947de668

Course Link:https://www.datacamp.com/courses/introduction-to-data-visualization-with-python

### Statistical Thinking in Python (Part 1)

Course Description
After all of the hard work of acquiring data and getting them into a form you can work with, you ultimately want to make clear, succinct conclusions from them. This crucial last step of a data analysis pipeline hinges on the principles of statistical inference. In this course, you will start building the foundation you need to think statistically, to speak the language of your data, to understand what they are telling you. The foundations of statistical thinking took decades upon decades to build, but they can be grasped much faster today with the help of computers. With the power of Python-based tools, you will rapidly get up to speed and begin thinking statistically by the end of this course.

Certificate Link:https://www.datacamp.com/statement-of-accomplishment/course/5358f80278af8131b4486707626d3c183acd3590

Course Link:https://www.datacamp.com/courses/statistical-thinking-in-python-part-1

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