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Tableau Training Online
Our Tableau Training Online course taught by 15+ years working professionals. 100% practical course with real-time projects. Course Duration will be 20+ hours. We also conduct fast-track/ weekend/ 1 to 1 classes. Additionally, you will get a sample resume, certification guidance, and Free life-time Access to our LMS.
Data Science Training | Tekslate
By taking Data science training from Tekslate, you'll master major data science topics such as data analysis, methods to deploy R statistical computing, important Machine Learning algorithms, and K-Means Clustering. This course also includes time-series analysis, Na�ve Bayes, business analytics and Hadoop framework. Our curriculum is designed by industry experts based on real-time scenarios.
Machine Learning for Everyone
I decided to write a post I've been wishing existed for a long time. A simple introduction for those who always wanted to understand machine learning. Only real-world problems, practical solutions, simple language, and no high-level theorems. One and for everyone. Whether you are a programmer or a manager.
Python Tutorial | AlphaCodingSkills
This tutorial is intended for software programmers interested in studying basic and advanced concepts of Python. This tutorial covers all topics of Python which includes data types, operators, strings, control statements, lists, tuples, sets, dictionary, lambda, functions, classes, object-oriented programming, constructor, destructor, inheritance, exception handling & File IO.
Polling for data in R
Instructor Mark Niemann-Ross begins by sharing a framework for understanding the different types of high-velocity data. He then covers how to use R to acquire high-velocity data, as well as how to leverage profiling tools and optimize R code for use with high-velocity data. He wraps up by exploring how to use R to present data, including how to use Shiny'an R package that allows you to build web apps straight from R'for interactive dashboards.
Four Key Challenges for Business Analytics
This article is co-written with Arild Nebb Ervik. Regardless of how �big� the data are, success in analytics relies at least as much on organizational alignment and process as on the chosen analytical tool. Through distillation of the talk stream in the market we may identify four key challenges to address.