Stats/Code/ML/AI

If you are preparing to make a career in data or are looking for opportunities to skill-up in your current data-centric role, then this analysis of in-demand skills for 2021, based on over 17,000 Data Engineer job postings, should offer you a good idea as to which programming languages and software tools are increasing and decreasing in importance.

Every human being needs oxygen to survive. Just think for a ... The post R or Python? Reasons behind this Cloud...

Monitor the health of your Snowflake data pipelines with these simple queriesImage courtesy of Sydney Rae on Unsplash.Your team just migrated to Snowflake. Your CTO is all in on this “modern data stack,” or as he calls it: “The Enterprise Data Discovery.” But as any data engineer will tell you, not even the best tools will save you from broken pipelines.

What did Jeff Wilke, among key Amazon executives trusted by Jeff Bezos, do first after leaving the technology giant? Learn a creative computer coding skill.

If your raw data is in a SQL-based data lake, why spend the time and money to export the data into a new platform for data prep?

Weighted average ensembles assume that some models in the ensemble have more skill than others and give them more contribution […]

Reduce the size of your dataset while keeping as much of the variation as possiblePhoto by Nika Benedictova on UnsplashIn both Statistics and Machine Learning, the number of attributes, features or input variables of a dataset is referred to as its dimensionality. For example, let’s take a very simple dataset containing 2 attributes called Height and Weight. This is a 2-dimensional dataset and any observation of this dataset can be plotted in a 2D plot.

A complete tool for exploratory data analysisPhoto by Isaac Smith on UnsplashAltair is a statistical data visualization library for Python. It provides a simple and easy-to-understand syntax for creating both static and interactive visualizations. What I think separates Altair from other common data visualization libraries is that it integrates data analysis components into the visualizations seamlessly. Thus, it serves as a highly practical tool for data exploration.

Analytics Insight has listed the top 50 developer communities that are helping programmers in many ways. Programming and coding is an art! Expert sometimes points that programmers are born with the skill. Even though coders

This article was published as a part of the Data Science Blogathon. R you ready? Let’s learn clustering in R. The post Beginner’s Guide to Clustering in R...

Python is an open-source programming language that can be used for a wide variety of applications such as data analysis, data science, and data visualization, software and web development, and writing scripts for systems. Python is so powerful that some parts of the MacOS actually rely on it, and the combination of this power and Python’s intuitive, user-friendly syntax make it one of the most popular programming languages in the world. If this sounds like something you want to learn — especially within the context of analyzing hockey statistics — you’ve come to the right place. By end of this tutorial, you will have not only a base-level understanding of Python as a programming language, but you will be comfortable enough in Python to perform small-scope data analysis on your own.

ArticleVideo Book This article was published as a part of the Data Science Blogathon. Introduction R is one most famous programming languages for statistical ... The post Top 10 R Packages for Data...

Normalization, standardization, and regularization all sound similar. However, each plays a unique role in your data preparation and model building process, so you must know when and how to use these important procedures.

Here some off-the-beaten-path options to consider, when looking for a first job, a new job or extra income by leveraging your machine learning experience. Many were offers that came to my mailbox at some point in the last 10 years, mostly from people looking at my LinkedIn profile. Thus the importance of growing your network and visibility, write…

Applied machine learning, or applying artificial intelligence to practical applications, poses serious challenges. The book "Real World AI" explores these challenges in depth.

ArticleVideo Book This article was published as a part of the Data Science Blogathon. Creating a machine learning model is a wholesome process involving ... The post The Easiest Way To Deploy...

Tableau is making a case for business science as new discipline for putting control over AI models directly in the hands of end users.

Semi-supervised learning is a learning problem that involves a small number of labeled examples and a large number of unlabeled examples. Learning problems of this type are challenging as neither supervised nor unsupervised learning algorithms are able to make effective use of the mixtures of labeled and untellable data. As such, specialized semis-supervised learning algorithms […]

In the April release of Tableau Prep, you can now invoke the power of Salesforce Einstein Discovery to bulk score your data directly in your flow. Bringing these powerful predictive models into Tableau Prep will help people closer to the business to use advanced analytics techniques to uncover practical insights, inform proactive decisions, and solve problems faster. Rapinder Jawanda Kristin Adderson April 6, 2021 - 7:31pm April 7, 2021 In the April release of Tableau Prep, you can now invoke the power of Salesforce Einstein Discovery to bulk score your data directly in your flow. Bringing these powerful predictive models into Tableau Prep will help people closer to the business to use advanced analytics techniques to uncover practical insights, inform proactive decisions, and solve problems faster. With this integration, you…

Part 2 of this short series focused on fundamental techniques, see here. In this Part 3, you will find several…