Showing posts with label R. Show all posts
Showing posts with label R. Show all posts

Become a master of Web Analytics Using R Tool in 4 hours

increase-your-traffic-by-learning-web-analytics-with-r-tool
Become a master of Web Analytics Using R Tool in 4 hours, Get More Out your Website By Learning How To Analyse the Website Data Using R Analytic Tool.


  • Created by Easylearning guru
  • 4.5 hours on-demand video
  • 68 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion


Description
This course will give you a better insights of your site visitors in order to make more profits by analyzing web data. Participants, at the end of the training, will be technically competent in essential as well as advanced concepts of Web Analysis such as:


  • Using flow visualization to see how visitors navigate through a site
  • Understanding goals and conversions
  • Understanding KPIs and Metrics
  • Performing real-time analysis using R and Rstudio
  • Analyzing visitor and traffic source reports
  • Tracking AdWords and other marketing campaigns
  • Utilizing segmentation for deeper analysis
  • Understanding the raw data and how it's collected
  • Selecting and comparing date ranges
  • Identifying slow-performing pages
  • Configuring and analyzing internal site search
  • Working with reports and Graphs
  • Cleaning up data with inline filters
  • Forecasting Bounce rate and other metrics on website
  • Plotting data on Maps

Who is the target audience?

  • If you are Mr. /Ms. Web Interested, this course is for you because you will learn how you can quickly get started with web analytics. It can help inform decisions you make, no matter how small or big, and can help you be significantly more effective with your web analytics–informed actions.
  • Anyone can go for this course. Anyone is including students who want to choose Analytics as their career option, employees of organization, CEOs, Managers, SEOs, Business Analyst, Digital Analyst, Web Analyst, everybody who wants to optimize website performance and gain profits from it.

Preview This Course - GET COUPON CODE

R Programming for Statistics and Data Science

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R Tidyverse Reporting and Analytics for Excel Users
From Excel To Big Data and Interactive Dashboard Visualizations in 5 HoursCreated by 365 Careers, 

Created by Jonathan Ng
2.5 hours on-demand video
3 Articles
Full lifetime access
Access on mobile and TV
Certificate of Completion

Description
If you use Excel for any type of reporting or analytics then this course is for you. There are a lot of great courses teaching R for statistical analysis and data science that can sometimes make R seem a bit too advanced for every day use. Also since there are many different ways of using R that can often add to the confusion. The reality is that R can be used to make your every day reporting analytics that you do in Excel much faster and easier without requiring any complex statistical techniques while at the same time giving you a solid foundation to expand into those areas if you so wish. This course uses the Tidyverse standards for using R which provides a single, comprehensive and easy to understand method for using R without complicating things via multiple methods. It's designed to build upon the the skills you are already familiar with in Excel to shortcut your learning journey. 

When I first started using R I thought that it could be a good replacement for the automation type processes I used to write in VBA. This can be quite off putting for a lot of Excel users as VBA often adds an extra layer of complexity to your work and is often only something which is done to automate a process which has already been established in Excel. One of the key benefits of Excel is that you are working directly with the data without having to go through the complexity and overhead of using a programming language. 

Programming languages such as VBA are actually very difficult for working with data as there isn't even any concise way of referencing common data elements such as named table columns. To carry out an operation on every row would take several lines of code which runs slow and ends up hiding your formula which actually contains your business logic. 

Despite all of this people use VBA anyway as once you invest the time to setup your processes you can run the exact same steps thousands of times with a click of a button. 

What if there were a way to work directly with your data as simply as Excel but also have more programming power than VBA? That's what R can do for you. 

Since I've started using R people have asked me when it would be beneficial to use R instead of Excel. Here are some examples

1. vlookups and sumifs on large datasets can run very slowly in Excel. I've helped people to replace multiple lines of vlookups that take 80 minutes to run in Excel with a single function in R that takes less than 1/10th of a second. 

2. Exploring and analysing your data in R can be Viewed in a simple table like Excel but also has a wide range of other methods which can be more effective.

3. Dashboards and visualisations are much richer and easier to construct than in Excel

4. Distributing your work in Excel can be beneficial since almost everyone has Excel installed. The problems with this are that not everyone always has the same version of Excel or addins installed which means your work might not be compatible. Also files are usually emailed around which can very quickly lead to hundreds of untracked copies of your Excel files with slight variations in them. The outputs from R can be simple Excel or csv files however your output can also be a web app that can be centrally stored and tracked on a server compatible with any web browser on your computer or smart phone. 

5. Team collaboration and version control in Excel is done via shared workbooks and track changes. Turning on these features in Excel disables some of Excels best features and still results in file locking. Team collaboration in R is done on github which allows you to easily work across teams without file locking issues and full audit histories of your work. 

The beauty of R is that once you start using it you will no longer have to make a special investment of time to automate your processes after your analysis is done. Practically anything that you can do in Excel you'll be able to do faster and better for even your first round of analysis and will leave you with an script which means your work is reproducible and automated from the very beginning. 

Even though your existing Excel skills will help you to pick up R one of the hardest things is that you're so familiar with Excel that it's too easy to keep on using it. I used Excel for years and spent thousands of hours studying how to use it more efficiently, I even taught advanced courses in it. It seemed obvious to me at the time that it was one of the most efficient ways to work with data. Even though working with a programming language might be more powerful it often had too much over head and was too removed from the actual data analysis. R is the programming language I wish I learnt 20 years ago. Perhaps somewhat counter intuitively you'll end up spending less time thinking about how to put a piece  of work together than Excel and more time looking at your data in new ways that you've probably never even thought of. 

Who is the target audience?

  • If you are currently using Excel to deliver a regular reporting requirement this course will be a good fit for you. This course is designed to introduce R to non programmer Excel users who are already familiar with Pivot Tables, vlookups ,if statements and charts.

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Statistics for Data Analysis Using R

statistics-using-r
Statistics for Data Analysis Using R
Learn Programming in R & R Studio • Descriptive, Inferential Statistics • Plots for Data Visualization • Data Science

Created by Sandeep Kumar
12.5 hours on-demand video
13 Articles
Full lifetime access
Access on mobile and TV
Certificate of Completion

Description
Perform simple or complex statistical calculations using R Programming! - You don't need to be a programmer for this :)

Learn statistics, and apply these concepts in your work place using R.

The course will teach you the basic concepts related to Statistics and Data Analysis, and help you in applying these concept. Various examples and data-sets are used to explain the application.

I will explain the basic theory first, and then I will show you how to use R to perform these calculations.

Following areas of statistics are covered:

Descriptive Statistics - Mean, Mode, Median, Quartile, Range, Inter Quartile Range, Standard Deviation. (Using base R function and the psych package)

Data Visualization - 3 commonly used charts: Histogram, Box and Whisker Plot and Scatter Plot (using base R commands)

Probability - Basic Concepts, Permutations, Combinations (Basic theory only)

Population and Sampling - Basic concepts (theory only)

Probability Distributions - Normal, Binomial and Poisson Distributions (Base R functions and the visualize package)

Hypothesis Testing - One Sample and Two Samples - z Test, t Test, F Test, Chi Square Test

ANOVA - Perform Analysis of Variance (ANOVA) step by step doing manual calculation and by using R.

Who is the target audience?

  • Anyone who want to use statistics to make fact based decisions.
  • Anyone who wants to learn R and R Studio for career in data science.
  • Anyone who thinks Statistics is confusing and wants to learn it in plain and simple language.

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Introduction to Time Series Analysis and Forecasting in R

Coupon Details

Introduction to Time Series Analysis and Forecasting in R, Work with time series and all sorts of time related data in R - Forecasting, Time Series Analysis, Predictive Analytics
Created by R-Tutorials Training
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time-series-analysis-and-forecasting-in-r


What Will I Learn?

  • use R to perform calculations with time and date based data
  • create models for time series data
  • use models for forecasting
  • identify which models are suitable for a given dataset
  • visualize time series data
  • transform standard data into time series format
  • clean and pre-process time series
  • create ARIMA and exponential smoothing models
  • know how to interpret given models
  • identify the best time series libraries for a given problem
  • compare the accuracy of different models