Learning Path: R: Master Statistical Modeling Using R
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Learning Path: R: Master Statistical Modeling Using R

Model your data for powerful data analytics
0.0 (0 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
5 students enrolled
Created by Packt Publishing
Last updated 7/2017
English
Current price: $10 Original price: $200 Discount: 95% off
5 hours left at this price!
30-Day Money-Back Guarantee
Includes:
  • 4 hours on-demand video
  • 1 Supplemental Resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Set up RStudio
  • See how things are done in R, using its particular set of objects
  • Understand how working with complex data is different to standard numerical work
  • Perform feature engineering with tennis data
  • Create Data Visualization using R
  • Work with different data structures such as vectors, lists, matrices, and data frames
  • Employ discrete and continuous distributions for analysing your data
  • Model your data with the help of statistical and parametric modelling
  • Decide better models for analysis by studying information criteria
  • Efficiently predict data with the help of correlation and regression analysis
View Curriculum
Requirements
  • No prior knowledge of R is needed.
  • Basic knowledge of Math and Statistics would be useful.
Description

The R language is best suited for statistical computations and visualization. Even if you do not have any prior experience in programming or statistical software, this Learning Path will help you get you up and running not only with the basics of R but also statistically modeling.

This learning journey begin by introducing R and setting things up so that you are ready to go using RStudio, the associated IDE. Then, you will look at R as a programming language and see how the standard things are done in it. You will obtain a dataset and then learn how to clean the dataset. Data cleaning constitutes almost 80% of data analysis. You will also explore discrete distributions, continuous distributions, and random number generation. Finally, you will see how to model your data and discover hypothesis testing. You will dive into descriptive statistics and graphs, parametric and nonparametric statistical methods, correlation and regression analysis, and time-series analysis.

By the end of this Learning path, you will be able to use R to model the different types of data with ease.

About The Author

Dr. Samik Sen is a Theoretical Physicist and loves hard problems to think about. After his Phd, which was about developing computational methods to solve problems for which no solutions existed, he began thinking about how to tackle the maths problem while lecturing. He developed algorithms to generate problem sets and solutions, and learned how to create video lessons. He has developed a large Facebook community teaching school maths around Ireland, with associated e-learning products and YouTube channel. Samik is currently fascinated by machine and deep Learning.He has developed a machine learning system which is performing better than he can himself which was the hope.

Olgun Aydin is a PhD candidate at Department of Statistics, Mimar Sinan University. He has been working on Deep Learning for his PhD thesis. Also working as Data Scientist.He is so familiar with Big Data technologies like Hadoop, Spark and able to use Hive, Impala. He is a big fan of R. Also he really loves to work with Shiny, SparkR.He has many academic papers and proceedings about applications of statistics on different disciplines. Mr. Olgun really loves statistic and loves to investigate new methods, share his experience with people.


Who is the target audience?
  • This Learning Path is for R developers and data analysts who have basic knowledge of maths and statistics.
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Curriculum For This Course
42 Lectures
03:44:39
+
Speaking ‘R’ - The Language of Data Science
19 Lectures 02:18:40

This video gives an overview of the entire course.

Preview 03:57

The aim of the video is to introduce the section and overview of the language R.

What Is R?
02:12

We need to have the core programs before we can begin and in this video,we show where to get them.

Getting and Setting Up R/Rstudio
02:01

In this video, we look at where to begin, so that we can get started.

Using RStudio
03:52

In this video, you will learn how RStudio has packages which avoid the problems and how we'll work on them.

Packages
08:01

In this video, you will learn how similar R is to other languages.

Preview 07:57

In this video, we see more familiar things in R.

Familiar Building Programming Blocks
07:49

In this video, we are now ready to write programs.

Putting It All Together
11:27

In this video, we will look at R data types which are new.

Core R Types
10:18

In this video, we will introduce some key commands to study data.

Some Useful Operations
05:12

In this video, we willintroduce various commands to help us pick out elements in which we are interested in.

More Useful Operations
03:18

In this video, we will investigate the Titanic dataset to see what it says.

Titanic
11:11

In this video, we willadd a value by processing our data.

Tennis
12:42

In this video,we will download football results from a web page.

It's Mostly Cleaning Up
12:21

In this video, we will use R to do some statistics.

Preview 10:15

In this video, we will work with distributions using R.

Distributions
09:27

In this video, we will see some of R's graphical power.

Time to Get Graphical
06:41

In this video, we will use the plotting package, ggplot2.

Plotting to Another Dimension
03:37

In this video, we will see another plotting technique known as Facets.

Facets
06:22
+
Deep Dive into Statistical Modeling with R
23 Lectures 01:25:59

This video gives an overview of the entire course.

Preview 02:43

This video gives an introduction to R in general.

What Is R?
03:22

This video talks about the benefits of R in general. Why do we use R? Why is R useful?

Benefits of R
01:23

In this video, we will see how big companies use sample R applications

Some Sample R Applications
01:20

In this video, we will show how to make the first applications in R

First Applications in R
01:44

In this video, we will define problems with the installation of R packages

Dealing with Problems in Installing R Packages
01:39

In this video, we will define the usage of vectors and lists in R.

Preview 02:22

In this video, we will define the usage of matrix and dataframes in R.

Matrix and DataFrames
05:47

This video shows the importance of using casting strings

Casting Variables
03:50

This video deals with the concept of data manipulation

Data Manipulation
04:22

This video explains some examples of importing data from third-party sources to R and exporting data from R

Data Importing and Exporting
06:05

In this video, we will import data from PostgreSQL databases and export data from R to PostgreSQL databases

Connecting PostgreSQL Databases
04:37

In this video, we will calculate probabilities for some examples ofbinomial, poisson, and negative binomial distribution

Preview 03:34

In this video, we will calculate probabilities for some examples of normal, exponential, and weibull distribution

Continuous Distributions
04:30

In this video, we will generate random numbers from some discrete and continuous distributions

Random Number Generators
05:21

In this video, we will talk about distribution fitting

Distribution Fitting
02:29

In this video, we will define the p value and its calculation

Some Tricks about Calculating P-values
02:54

In this video, we will show how to calculate some descriptive statistics and draw plots

Preview 04:30

In this video, we will talk about parametric statistical methods and how to apply parametric statistical method

Parametric Statistical Methods
03:50

n this video, we will talk about non-parametric statistical methods and how to apply parametric statistical method

Non-Parametric Statistical Methods
02:38

In this video, we will talk about correlation and regression analysis and how to apply correlation and regression analysis

Correlation and Regression Analysis
08:41

In this video, we will show how do we apply time series analysis

Time Series Analysis
05:32

In this video, we will show how to apply missing value imputation

Missing Value Imputation
02:46
About the Instructor
Packt Publishing
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