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Description

Regression analysis is one of the central aspects of both statistical and machine learning-based analysis. This course will teach you regression analysis for both statistical data analysis and machine learning in R in a practical hands-on manner. This course can help you achieve better grades, give you new analysis tools for your academic career, implement your knowledge in a work setting or make business forecasting related decisions.

  • Access 50 lectures & 6 hours of content 24/7
  • Implement & infer Ordinary Least Square (OLS) regression using R
  • Build machine learning-based regression models & test their robustness in R
  • Apply statistical and machine learning-based regression models to deals with problems such as multicollinearity
  • Learn when & how machine learning models should be applied

Note: Software not included

Mining unstructured text data and social media is the latest frontier of machine learning and data science. This course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real life. After taking this course, you’ll easily use packages like the caret and dplyr to work with real data in R.

  • Access 77 lectures & 7 hours of content 24/7
  • Be able to read in data from different sources including databases
  • Learn basic web scraping—extracting text & tabular data from HTML pages
  • Learn social media mining from Facebook & Twitter
  • Analyze text data for emotions
  • Extract information relating to tweets & posts
  • Carry out Sentiment analysis

Note: Software not included

In this course, you’ll learn to implement R methods using real data obtained from different sources. After this course, you’ll understand concepts like unsupervised learning, dimension reduction, and supervised learning.

  • Access 56 lectures & 7 hours of content 24/7
  • Learn how to harness the power of R for practical data science
  • Read-in data into the R environment from different sources
  • Carry out basic data pre-processing & wrangling in R studio
  • Implement unsupervised/clustering techniques such as k-means clustering
  • Explore supervised learning techniques/classification such as random forests
  • Evaluate model performance & learn best practices for evaluating machine learning model accuracy

Note: Software not included

With 39 lectures, this course will tackle the most fundamental building block of practical data science—data wrangling and visualization. It will take you from a basic level of performing some of the most common data wrangling tasks in R with two of the most important R data science packages, Tidyverse and Dplyr. It will introduce you to some of the most important data visualization concepts and techniques that will suit and apply to your data.

  • Read-in data into the R environment from different sources
  • Learn how to use some of the most important R data wrangling & visualization packages such as Dpylr and Ggplot2
  • Carry out basic data pre-processing & wrangling in R studio
  • Gain proficiency in data pre-processing, wrangling & data visualization in R

Software not included

This course is designed to equip you to use some of the most important R data wrangling and visualization packages such as dplyr and ggplot2. You’ll discover data visualization concepts in a practical manner that will help you apply them for practical data analysis and interpretation. You’ll also be able to determine which wrangling and visualization techniques are best suited to specific problems.

  • Access 51 lectures & 6 hours of content 24/7
  • Read in data into the R environment from different sources
  • Carry out basic data pre-processing & wrangling in R Studio
  • Learn to identify which visualizations should be used in any given situation
  • Build powerful visualizations & graphs from real data

Note: Software not included

In this course, you’ll use easy-to-understand, hands-on methods to absorb the most valuable R Data Science basics and techniques. After this course, you’ll understand the underlying concepts to understand what algorithms and methods are best suited for your data.

  • Access 52 lectures & 5 hours of content 24/7
  • Get an introduction to powerful R-based packages for time series analysis
  • Learn commonly used techniques, visualization methods & machine/deep learning techniques that can be implemented for time series data
  • Apply these frameworks to real-life data including temporal stocks & financial data

Note: Software not included

You are allowed to use this product only within the laws of your country/region. SharewareOnSale and its staff are not responsible for any illegal activity. We did not develop this product; if you have an issue with this product, contact the developer. This product is offered "as is" without express or implied or any other type of warranty. The description of this product on this page is not a recommendation, endorsement, or review; it is a marketing description, written by the developer. The quality and performance of this product is without guarantee. Download or use at your own risk. If you don't feel comfortable with this product, then don't download it.

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