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Best Languages for Data Science and Statistic

Hundreds of programming languages dominate the data science and statistics market: Python, R, SAS and SQL are standouts. If you're looking to branch out and add a new programming language to your…

Blog Best Languages for Data Science and Statistic 5 Likes
Correlation does not equal causation but How

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Correlation does not equal causation but How
5 Likes
Rule of thumb: Which AI / ML algorithms to ap

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**How to…**

At the time of writing, I'm a 52 year-old working in the fields of mathematics and data science. In mathematics, that makes me well-seasoned (and probably well-tenured, if I had chosen to continue…

Blog On Being a 50 Year Old Data Scientist 14 Likes
10 Machine Learning Methods that Every Data S
Photo by …
Blog
10 Machine Learning Methods that Every Data S
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Misuses of Statistics: Examples and Solutions

This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, Hadoop, decision trees, ensembles, correlation,…

Blog Misuses of Statistics: Examples and Solutions 4 Likes
State of Data Science & Machine Learning in 2

The results of this Kaggle survey were published recently. The questions addressed include:

- Introduction
- Survey Methodology
- Survey Participants- Basic…

Which machine learning algorithm should I use

*By Hui Li, Principal Staff Scientist, Data Science, at SAS.*

A typical question asked by a beginner, when facing a wide variety of machine learning algorithms, is “which algorithm…

Blog Which machine learning algorithm should I use 10 Likes Exploring Kaggle Titanic data with R PackagesRecently (6/8/2018), I saw a post about a new R package "naniar", which according to the package…

Blog Exploring Kaggle Titanic data with R Packages 2 Likes Google Data Studio in 10 minutes: Step-by-SteGoogle Data Studio is a…

Blog Google Data Studio in 10 minutes: Step-by-Ste 1 Like Difference between Machine Learning, Data SciIn this article, I clarify the various roles of the data scientist, and how data science compares and overlaps with related fields such as machine learning, deep learning, AI, statistics, IoT,…

Blog Difference between Machine Learning, Data Sci 109 Likes Why Logistic Regression should be the last thI recently read a very popular article entitled *5 Reasons “Logistic Regression” should be the first thing you learn when becoming a Data Scientist*. Here I provide my opinion on why this…

**Note:** This is a long post, but I kept it as a single post to maintain continuity of the thought flow

In this longish post, I have tried to explain Deep Learning…

Blog The Mathematics of Data Science: Understandin 10 Likes
DSC Webinar Series: AI Models And Active Lea…
video
DSC Webinar Series: AI Models And Active Lea…
4 Likes
Linear Regression in Tensorflow

Tensorflow is an open source machine learning (ML) library from Google. It has particularly became popular because of the support for Deep Learning. Apart from that it's highly scalable and can…

Blog Linear Regression in Tensorflow 3 Likes An Introduction to Implementing Neural NetworThis article on an introduction to implementing neural networks using TensorFlow, was posted by Faizan Shaikh. Faizan is a Data Science enthusiast and a Deep learning rookie. A…

Blog An Introduction to Implementing Neural Networ 5 Likes Invitation to Join Data Science CentralOver 1,000,000 machine learning, AI, analytics and data science practitioners use our resources. Access high quality, relevant and proprietary content found…

Blog Invitation to Join Data Science Central 97 Likes MBA vs. Data Science qualifications: Does #AILast week, the financial times wrote that there was a…

Blog MBA vs. Data Science qualifications: Does #AI 2 Likes New Machine Learning Cheat Sheet by Emily BarThis blog about machine learning was written by Emily Barry. Emily is a Data Scientist in San Francisco, California. She really loves emoji. Another thing she loves is data science.…

Blog New Machine Learning Cheat Sheet by Emily Bar 15 Likes
One-page R: a survival guide to data science

*This article comes from Togaware.…*

29 Statistical Concepts Explained in Simple E

This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, decision trees, ensembles, correlation, Python, R, Tensorflow,…

Blog 29 Statistical Concepts Explained in Simple E 23 Likes- 10 Scrum benefits and how to realize them
- 5 proven patterns for resilient software architecture design
- Microsoft's acquisition of Nuance clears antitrust hurdle
- The right to disconnect vs. America's always-on culture
- The enterprise advantages of automated data collection
- Today's blockchain use cases and industry applications
- Salesforce Industries rolls out banking AI compliance tools
- DataRobot integrates AI modeling tools with Snowflake
- M&A wave rising in hot AI market
- AWS analytics helps nonprofit fight climate change

Posted 17 June 2021

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