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Statistics & Machine Learning for Regression Modeling with R

KEY FEATURES

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

PRODUCT SPECS

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web & mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Updates included
  • Experience level required: all levels

Requirements

  • Prior experience of working w/ R & RStudio
  • Basic knowledge of statistics
  • Prior experience of using simple linear regression modelling

THE EXPERT

Instructor

Minerva Singh is a Ph.D. graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.

Social Media Mining & Text Data Analysis with Natural Language Processing in R

KEY FEATURES

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

PRODUCT SPECS

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web & mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Updates included
  • Experience level required: all levels

Requirements

  • Prior experience of R & RStudio
  • Prior experience of statistical & machine learning techniques will be beneficial

THE EXPERT

Instructor

Minerva Singh is a Ph.D. graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.

Working With Classes: Classify & Cluster Data With R

KEY FEATURES

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

PRODUCT SPECS

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web & mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Updates included
  • Experience level required: beginner

Requirements

  • Able to operate & install software on a computer
  • Prior exposure to common machine learning terms such as unsupervised & supervised learning

THE EXPERT

Instructor

Minerva Singh is a Ph.D. graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.

Pre-Process & Visualize Data With Tidy Techniques in R

KEY FEATURES

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

PRODUCT SPECS

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web & mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Updates included
  • Experience level required: all levels

Requirements

  • Ability to install R & RStudio on your computer/laptop
  • Know how to install & load R packages

THE EXPERT

Instructor

Minerva Singh is a Ph.D. graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.

Practical Data Pre-Processing & Visualisation Training with R

KEY FEATURES

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

PRODUCT SPECS

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web & mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Updates included
  • Experience level required: all levels

Requirements

  • Ability to install R & RStudio on your computer/laptop
  • Know how to install & load R packages

THE EXPERT

Instructor

Minerva Singh is a Ph.D. graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.

Practical Time Series Data Analysis With Statistics and Machine Learning

KEY FEATURES

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

PRODUCT SPECS

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web & mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Updates included
  • Experience level required: intermediate

Requirements

  • Internet access required

THE EXPERT

Instructor

Minerva Singh is a Ph.D. graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.