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analysizing_police_activity

You will explore the Stanford Open Policing Project dataset and analyze the impact of gender on police behavior. During the course, you will gain more practice cleaning messy data, creating visualizations, combining and reshaping datasets, and manipulating time series data.

animals

api

appiledai

autograd

awesome-mlops

:sunglasses: A curated list of awesome MLOps tools

aws

buildsystems

c--

calculus

clean_data

compilation

cudagemmoptimization

cudaprogramming

cudareduction

cuptiprofiler

cutlassprogramming

data-science-algorithms-in-a-week-second-edition

Data Science Algorithms in a Week, Second Edition, published by Packt

datacamp

devcontainer

dimensionalityreduction

High-dimensional datasets can be overwhelming and leave you not knowing where to start. Typically, you’d visually explore a new dataset first, but when you have too many dimensions the classical approaches will seem insufficient. Fortunately, there are visualization techniques designed specifically for high dimensional data

dsa

eda

extendingpython

feature-selection-for-machine-learning

Code repository for the online course Feature Selection for Machine Learning

flask

function

hands-on-exploratory-data-analysis-with-python

Hands-on Exploratory Data Analysis with Python, published by Packt

hypothesis_testing

Hypothesis testing lets you answer questions about your datasets in a statistically rigorous way. In this course, you'll grow your Python analytical skills as you learn how and when to use common tests like t-tests, proportion tests, and chi-square tests. Working with real-world data, including Stack Overflow user feedback, you'll gain a deep understanding of how these tests work, and the key assumptions that underpin them. You'll also discover how different tests are related using the “there is only one test" framework, before learning how to use non-parametric tests to go beyond the limitations of side-step the requirements of hypothesis tests.

importing

As a data scientist, you will need to clean data, wrangle and munge it, visualize it, build predictive models, and interpret these models. Before you can do so, however, you will need to know how to get data into Python. In this course, you'll learn the many ways to import data into Python: from flat files such as .txt and .csv; from files native to other software such as Excel spreadsheets, Stata, SAS, and MATLAB files; and from relational databases such as SQLite and PostgreSQL.