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Programming for Data Science with R

Video Description

Serious Data Science with R

About This Video

  • Analyze and visualize data to further apply machine learning to interpret your data
  • Discover structure and unstructured data by using unique functionalities of R
  • Load your tool kit with various machine learning methods to help you create algorithms for a business

In Detail

Data is everywhere and growing faster than ever before. It has now become challenge to deal with such huge amount of data as it is highly time-consuming. This has created a huge demand for people who can mine and interpret data. There is enormous value in data processing and analysis—and that is where a data scientist steps into the spotlight.

This course will help candidates having basic knowledge of R Programming elevate to the next level. R can be used to tease actionable insights out of gigabytes of data, and this course will show you exactly how to do it. Here, we will be building on the advanced and efficient ways of doing different parts of analytics- right from data cleaning, visualizing to building high performing models You’ll start your journey by loading data, visualizing it and interpreting it while providing intuitive solutions. Further, you will learn to apply machine learning algorithms to real-world problems in R.

By the end of the course, get geared up to tackle real-life data challenges by analyzing complex datasets. This in turn will bring out insights that companies can convert into actions.

You can find the codes of this course on GitHub: https://github.com/PacktPublishing/Programming-for-Data-Science-with-R-

Downloading the example code for this course: You can download the example code files for all Packt video courses you have purchased from your account at http://www.PacktPub.com. If you purchased this course elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you.

Table of Contents

  1. Chapter 1 : Your Data Science Toolkit R
    1. The Course Overview 00:03:42
    2. Importance of Data Science and Its Use Cases 00:04:50
    3. What Makes R so Powerful for Data Science? 00:05:58
  2. Chapter 2 : Working with Important Libraries in R
    1. Overview of Features and Advanced Libraries in R 00:06:54
    2. “Apply” Functions 00:12:17
    3. Using “dplyr” Package 00:12:28
    4. “data.table” Package 00:09:42
  3. Chapter 3 : Manipulating and Cleaning Data
    1. Importing Data – The Efficient Way 00:04:47
    2. Summarizing the Data Within Groups 00:07:55
    3. Merging Dataframes 00:06:56
    4. Missing Values and Outliers 00:09:30
    5. Reshaping Data 00:05:36
  4. Chapter 4 : Building Predictive Models
    1. Supervised versus Unsupervised Learning 00:05:10
    2. Predictive Models Overview: Regression versus Classification 00:03:21
    3. Linear Models-Features and Uses 00:07:34
    4. Non-Linear Models 00:05:27
    5. A Predictive Modeling Use Case 00:15:31
  5. Chapter 5 : Various Techniques to Boost Your Model
    1. Introduction to Machine Learning 00:04:15
    2. Decision Trees and Random Forest 00:10:28
    3. Support Vector Machines 00:05:26
    4. Simple Neural Networks 00:08:11
    5. Clustering Overview 00:11:48
  6. Chapter 6 : Let the Data Talk – Visualizing Data
    1. Building Visualizations-Introduction 00:09:40
    2. Geometry Types and Aesthetics 00:13:05
    3. Beautifying Your Visualizations 00:04:02
    4. Case Study on Visualization 00:11:07
  7. Chapter 7 : Integrating Different Components of Data Science – A Use Case
    1. Introduction – Housing Price Prediction 00:03:37
    2. Exploratory Data Analysis 00:17:31
    3. Model 1: Regression Model 00:08:34
    4. Model 2: ML Approach 00:02:48
    5. Final Model Selection 00:04:14