5 articles to learn #statistics for #datascience:

1. Comprehensive Inferential Statistics for Data Science -https://bit.ly/2NUQywr
2. Master Hypothesis Testing for Framing Data Science Problems - https://bit.ly/2u0utmV
3. Introduction to #ANOVA (with practical #Excel examples) - https://bit.ly/2F1ciE5
4. Tutorial for Understanding Non-Parametric Statistical Tests - https://bit.ly/2CcSxrr
5. Learn Statistics using R! - https://bit.ly/2VMOOIr

✴️ @AI_Python_EN

💡 What is a p-value?

When testing an hypothesis, the p-value is the likelihood that we would observe results at least as extreme as our result due purely to random chance if the null hypothesis were true.

💡 What does it mean when a p-value is low?

When the p-value is low, it is relatively rare for the our results to be purely from random variations in observations.

Because of this, we may decide to reject the null hypothesis. If the p-value is below some pre-defined threshold, we say that the result is "statistically significant" and we reject the null hypothesis.

💡 What value is most often used to determine statistical significance?

A value of alpha = 0.05 is most often used as the threshold for statistical significance.

#datascience #statistics

✴️ @AI_Python_EN

برترین مقالات آماری در استنباط آماری

"Statistical Inference in the 21st Century: A World Beyond p < 0.05":

> https://lnkd.in/g4dfjhJ

#Statistics
#منابع #مقاله #آمار #آموزش

❇️ @AI_Python
🗣 @AI_Python_arXiv
✴️ @AI_Python_EN

Important Machine Learning algorithms and their Hyperparameters

#machinelearning #datascience #statistics #algorithms

✴️ @AI_Python_EN

Important Machine Learning algorithms and their Hyperparameters

#machinelearning #datascience #statistics #algorithms

✴️ @AI_Python_EN

Data science is not #MachineLearning .
Data science is not #statistics.
Data science is not analytics.
Data science is not #AI.

#DataScience is a process of:
Obtaining your data
Scrubbing / Cleaning your data
Exploring your data
Modeling your data
iNterpreting your data

Data Science is the science of extracting useful information from data using statistics, skills, experience and domain knowledge.

If you love data, you will like this role....

solving business problems using data is data science. Machine learning/statistics /analytics may come as a way of the solution of a particular business problem. Sometimes we may need all to solve a problem and sometimes even a crosstabs may be handy.

➡️ Get free resources at his site:
www.claoudml.com

❇️ @AI_Python_EN

نقشه‌راه یادگیری علوم داده و مطالب مرتبط به این حوزه

#machinelearning #nlp #datascience #statistics
#منابع #یادگیری_ماشین #علم_داده #آمار

@pythony
github.com/virgili0/Virgilio

❇️ @AI_Python

How to spot a #MachineLearning "guru" without math or statistics knowledge 😁😂

Our advice: Don't be a #fake #AI consultant , influencer or company, take credible steps to become Real!
#datascience #statistics

❇️ @AI_Python_EN

Many Data Science aspirants struggle to find good projects to get a start in Data science or #MachineLearning.

Here is the list of few #DataScience projects (found on kaggle), it covers Basics of Python, Advanced Statistics, #SupervisedLearning (Regression and Classification problems)

1. Basic #python and #statistics

Pima Indians : https://www.kaggle.com/uciml/pima-indians-diabetes-database

Cardio Goodness fit : https://www.kaggle.com/saurav9786/cardiogoodfitness

Automobile : https://www.kaggle.com/toramky/automobile-dataset

2. Advanced Statistics

Game of Thrones:
https://www.kaggle.com/mylesoneill/game-of-thrones

World University Ranking:
https://www.kaggle.com/mylesoneill/world-university-rankings

IMDB Movie Dataset: https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset

3. Supervised Learning

a) Regression Problems

How much did it rain : https://www.kaggle.com/c/how-much-did-it-rain-ii/overview

Inventory Demand: https://www.kaggle.com/c/grupo-bimbo-inventory-demand

Property Inspection predictiion:
https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction

Restaurant Revenue prediction:
https://www.kaggle.com/c/restaurant-revenue-prediction/data

IMDB Box office Prediction:
https://www.kaggle.com/c/tmdb-box-office-prediction/overview

b) Classification problems

Employee Access challenge :
https://www.kaggle.com/c/amazon-employee-access-challenge/overview

Titanic :
https://www.kaggle.com/c/titanic
San Francisco crime:
https://www.kaggle.com/c/sf-crime

Customer satisfcation:
https://www.kaggle.com/c/santander-customer-satisfaction
Trip type classification:

https://www.kaggle.com/c/walmart-recruiting-trip-type-classification

Categorize cusine:
https://www.kaggle.com/c/whats-cooking

#پروژه #منابع #الگوریتمها #یادگیری_ماشین #هوش_مصنوعی #علم_داده #پایتون

❇️ @AI_Python

Interested in a #postdoc in #DataScience and #Statistics education? Come work with me.

Face with monocle work on exciting projects on data science + stats pedagogy
Woman technologist develop #opensource educational resources + tools

https://academicjobsonline.org/ajo/jobs/20125

❇️ @AI_Python
🔰 @DLeX_Apply