Introduction To Data Science [2016, ENG] torrent

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Introduction To Data Science
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Год выпуска: 2016
Производитель: Udemy
Сайт производителя: www.udemy.com
Продолжительность: 05:52:02
Тип раздаваемого материала: Видеоурок
Язык: Английский
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Описание: С помощью языка программирования R для выполнения научных проектов данных и стать ученым данных.
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Use the R Programming Language to execute data science projects and become a data scientist. Implement business solutions, using machine learning and predictive analytics.

The R language provides a way to tackle day-to-day data science tasks, and this course will teach you how to apply the R programming language and useful statistical techniques to everyday business situations.

With this course, you'll be able to use the visualizations, statistical models, and data manipulation tools that modern data scientists rely upon daily to recognize trends and suggest courses of action.

Understand Data Science to Be a More Effective Data Analyst

●Use R and RStudio

●Master Modeling and Machine Learning

●Load, Visualize, and Interpret Data

Use R to Analyze Data and Come Up with Valuable Business Solutions

This course is designed for those who are analytically minded and are familiar with basic statistics and programming or scripting. Some familiarity with R is strongly recommended; otherwise, you can learn R as you go.

You'll learn applied predictive modeling methods, as well as how to explore and visualize data, how to use and understand common machine learning algorithms in R, and how to relate machine learning methods to business problems.

All of these skills will combine to give you the ability to explore data, ask the right questions, execute predictive models, and communicate your informed recommendations and solutions to company leaders.
[spoiler="Содержание"]
├── 01 Course Overview
│ ├── 001 Course Introduction.mp4
│ ├── 002 Walk-through of a data science project.mp4
│ ├── 003 Starting with R and data.mp4
│ ├── homedata_lesson1_clean.rds
│ ├── homedata_lesson1_dirty.rds
│ └── Sec1_Lesson1_Walkthru.Rmd
├── 02 Modeling and Machine Learning
│ ├── 001 Mapping Business to Machine Learning Tasks.mp4
│ ├── 002 Validating Models.mp4
│ ├── 003 Naive Bayes background.mp4
│ ├── 004 Naive Bayes practice.mp4
│ ├── 005 Linear Regression background.mp4
│ ├── 006 Linear Regression practice.mp4
│ ├── 007 Logistic Regression background.mp4
│ ├── 008 Logistic Regression practice.mp4
│ ├── 009 Decision Trees and Random Forest background.mp4
│ ├── 010 Random Forest practice.mp4
│ ├── 011 Generalized Additive Models.mp4
│ ├── 012 Support Vector Machines.mp4
│ ├── 013 Gradient Boosting.mp4
│ ├── 014 Regularization for Linear and Logistic Regression.mp4
│ ├── 015 Evaluating Models.mp4
│ ├── Examples.R
│ ├── salaryData.rds
│ ├── TestTrainExample.Rproj
│ └── TestTrainSplit.Rmd
├── 03 Data
│ ├── 001 Loading Data in R.mp4
│ ├── 002 Visualizing Data.mp4
│ ├── 003 Missing Values.mp4
│ ├── 004 The Shape of Data.mp4
│ ├── 005 Dealing with Categorical Variables.mp4
│ └── 006 Useful Data Transformations.mp4
└── 04 Moving On
├── 001 Recommended Books.mp4
├── 002 Further Topics.mp4
└── 003 Next Steps.mp4
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Файлы примеров: отсутствуют
Формат видео: MP4
Видео : AVC, 1280x720 (16:9), 30.000 fps, ~864 Kbps avg, 0.031 bit/pixel
Аудио: 44.1 KHz, AAC LC, 2 ch, ~58.1 Kbps
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