Programming for Data Science with R

Programming for Data Science with R
Programming for Data Science with R
English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 4h 02m | 1.21 GB

Serious Data Science with R

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.

The course follows a practical approach while representing each technique to be applied on data-sets. Each section focuses on building of concept followed by hands-on-programming exercises. Throughout the course we’ll be discussing practical real-life cases for each concept. Our case study approach is designed to enable candidates to develop thorough understanding of concepts and will help them apply using R.

What You Will Learn

  • Start forecasting – Create algorithms to build predictive models
  • Structure your data and most importantly – Wrangle, visualise and explore data
  • Learn from your data – Gain insights and information from data
  • Play with your data – Manipulate and clean it
  • Let your data do the talking – Visualize data using ggplot
  • Deploy the most popular machine algorithms like Random forest, and Decision trees to build powerful models
Table of Contents

1 The Course Overview
2 Importance of Data Science and Its Use Cases
3 What Makes R so Powerful for Data Science
4 Overview of Features and Advanced Libraries in R
5 “Apply” Functions
6 Using “dplyr” Package
7 “data.table” Package
8 Importing Data – The Efficient Way
9 Summarizing the Data Within Groups
10 Merging Dataframes
11 Missing Values and Outliers
12 Reshaping Data
13 Supervised versus Unsupervised Learning
14 Predictive Models Overview – Regression versus Classification
15 Linear Models-Features and Uses
16 Non-Linear Models
17 A Predictive Modeling Use Case
18 Introduction to Machine Learning
19 Decision Trees and Random Forest
20 Support Vector Machines
21 Simple Neural Networks
22 Clustering Overview
23 Building Visualizations-Introduction
24 Geometry Types and Aesthetics
25 Beautifying Your Visualizations
26 Case Study on Visualization
27 Introduction – Housing Price Prediction
28 Exploratory Data Analysis
29 Model 1 – Regression Model
30 Model 2 – ML Approach
31 Final Model Selection


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