IT Training R Programming Courses in Dubai

Course Details

Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce. 

This course is a gentle yet thorough introduction to Data Science, Statistics and R using real-life examples. 

Let’s parse that.

Gentle, yet thorough: This course does not require a prior quantitative or mathematics background. It starts by introducing basic concepts such as the mean, median etc and eventually covers all aspects of an analytics (or) data science career from analysing and preparing raw data to visualise your findings. 

Data Science, Statistics and R: This course is an introduction to Data Science and Statistics using the R programming language. It covers both the theoretical aspects of Statistical concepts and the practical implementation using R. 

Real life examples: Every concept is explained with the help of examples, case studies and source code in R wherever necessary. The examples cover a wide array of topics and range from A/B testing in an Internet company context to the Capital Asset Pricing Model in a quant finance context. 

What's Covered:

Data Analysis with R: Datatypes and Data structures in R, Vectors, Arrays, Matrices, Lists, Data Frames, Reading data from files, Aggregating, Sorting & Merging Data Frames

Linear Regression: Regression, Simple Linear Regression in Excel, Simple Linear Regression in R, Multiple Linear Regression in R, Categorical variables in regression, Robust regression, Parsing regression diagnostic plots

Data Visualization in R: Line plot, Scatterplot, Bar plot, Histogram, Scatterplot matrix, Heatmap, Packages for Data Visualisation: Rcolorbrewer, ggplot2

Descriptive Statistics: Mean, Median, Mode, IQR, Standard Deviation, Frequency Distributions, Histograms, Boxplots

Inferential Statistics: Random Variables, Probability Distributions, Uniform Distribution, Normal Distribution, Sampling, Sampling Distribution, Hypothesis testing, Test statistic, Test of significance

Using discussion forums

Please use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(

We're super small and self-funded with only 2 people developing technical video content. Our mission is to make high-quality courses available at super low prices.

The only way to keep our prices this low is to *NOT offer additional technical support over email or in-person*. The truth is, direct support is hugely expensive and just does not scale.

We understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose.

It is a hard trade-off.

Thank you for your patience and understanding!

Who is the target audience?

• Yep! MBA graduates or business professionals who are looking to move to a heavily quantitative role
• Yep! Engineers who want to understand basic statistics and lay a foundation for a career in Data Science
• Yep! Analytics professionals who have mostly worked in Descriptive analytics and want to make the shift to being modellers or data scientists
• Yep! Folks who've worked mostly with tools like Excel and want to learn how to use R for statistical analysis

BASIC KNOWLEDGE

• No prerequisites: We start from basics and cover everything you need to know. We will be installing R and RStudio as part of the course and using it for most of the examples. Excel is used for one of the examples and basic knowledge of Excel is assumed.

WHAT YOU WILL LEARN

• Harness R and R packages to read, process and visualize data
• Understand linear regression and use it confidently to build models
• Understand the intricacies of all the different data structures in R
• Use Linear regression in R to overcome the difficulties of LINEST() in Excel
• Draw inferences from data and support them using tests of significance
• Use descriptive statistics to perform a quick study of some data and present results

Course Details

R programming

Course description

In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment, discuss generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, and organizing and commenting R code.

Course outline

• Week 1: Overview of R, R data types and objects, reading and writing data
• Week 2: Control structures, functions, scoping rules, dates and times
• Week 3: Loop functions, debugging tools
• Week 4: Simulation, code profiling

Course Details

R is one of the most popular languages used by Data Scientists, Researchers, Statisticians & Data Analysts to retrieve, analyze & visualize the data. It is most commonly used in statistical computing, scientific research & data analytics.

Brief Outline of Course

Reading, Writing of Datasets

Passing functions

Installing Packages & Libraries

Discussion on Libraries

Analysing data

Introduction to filters in R

If Else Function in R, For function in R

Statistical Function in R

Data cleaning on R

Create Array in R

Introduction to Machine Learning

Introduction to Packages (dplyr, SQLDF)

Plots in R

Introduction to Machine Learning Algorithms

Types of Machine Learning Algorithms

Data Types in R

Data/Numeric Frames in R

Project

Course Details

About this Course

This course introduces R programming environment as a way to have hands-on experience with Data Science. It starts with a few basic examples in R before moving onto doing statistical processing. The course then introduces Machine Learning with techniques such as regression, classification, clustering, and density estimation, in order to solve various data problems.

Basic knowledge

• This course is for beginners, but it helps to have some basic understanding of statistics (mean, median, scatter plot) and preliminary knowledge of any programming. The course also assumes that you know how to download and install various programs/apps, and you are able to edit and debug simple programs

What you will learn

• Writing simple R programs to do basic mathematical and logical operations
• Loading structured data in a R environment for processing
• Creating descriptive statistics and visualizations
• Finding correlations among numerical variables
• Using regression analysis to predict the value of a continuous variable
• Building classification models to organize data into pre-determined classes
• Organizing given data into meaningful clusters
• Applying basic machine learning techniques for solving various data problems

Course Details

Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce. 

This course is a gentle yet thorough introduction to Data Science, Statistics and R using real life examples. 

Let’s parse that.

Gentle, yet thorough: This course does not require a prior quantitative or mathematics background. It starts by introducing basic concepts such as the mean, median etc and eventually covers all aspects of an analytics (or) data science career from analysing and preparing raw data to visualising your findings. 

Data Science, Statistics and R: This course is an introduction to Data Science and Statistics using the R programming language. It covers both the theoretical aspects of Statistical concepts and the practical implementation using R. 

Real life examples: Every concept is explained with the help of examples, case studies and source code in R wherever necessary. The examples cover a wide array of topics and range from A/B testing in an Internet company context to the Capital Asset Pricing Model in a quant finance context. 

What's Covered:

Data Analysis with R: Datatypes and Data structures in R, Vectors, Arrays, Matrices, Lists, Data Frames, Reading data from files, Aggregating, Sorting & Merging Data Frames

Linear Regression: Regression, Simple Linear Regression in Excel, Simple Linear Regression in R, Multiple Linear Regression in R, Categorical variables in regression, Robust regression, Parsing regression diagnostic plots

Data Visualization in R: Line plot, Scatter plot, Bar plot, Histogram, Scatterplot matrix, Heat map, Packages for Data Visualisation : Rcolorbrewer, ggplot2

Descriptive Statistics: Mean, Median, Mode, IQR, Standard Deviation, Frequency Distributions, Histograms, Boxplots

Inferential Statistics: Random Variables, Probability Distributions, Uniform Distribution, Normal Distribution, Sampling, Sampling Distribution, Hypothesis testing, Test statistic, Test of significance

Using discussion forums

Please use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(

We're super small and self-funded with only 2 people developing technical video content. Our mission is to make high-quality courses available at super low prices.

The only way to keep our prices this low is to *NOT offer additional technical support over email or in-person*. The truth is, direct support is hugely expensive and just does not scale.

We understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose.

It is a hard trade-off.

Thank you for your patience and understanding!

Who is the target audience?

• Yep! MBA graduates or business professionals who are looking to move to a heavily quantitative role
• Yep! Engineers who want to understand basic statistics and lay a foundation for a career in Data Science
• Yep! Analytics professionals who have mostly worked in Descriptive analytics and want to make the shift to being modelers or data scientists
• Yep! Folks who've worked mostly with tools like Excel and want to learn how to use R for statistical analysis

BASIC KNOWLEDGE

• No prerequisites : We start from basics and cover everything you need to know. We will be installing R and RStudio as part of the course and using it for most of the examples. Excel is used for one of the examples and basic knowledge of excel is assumed.

WHAT YOU WILL LEARN

• Harness R and R packages to read, process and visualize data
• Understand linear regression and use it confidently to build models
• Understand the intricacies of all the different data structures in R
• Use Linear regression in R to overcome the difficulties of LINEST() in Excel
• Draw inferences from data and support them using tests of significance
• Use descriptive statistics to perform a quick study of some data and present results

Course Details

CyberModo has trained many individuals and corporate levels and we are proud for the share of contribution we made in the lives of those who we have trained. 

Learn programming with career-based courses. Specially designed for beginner, professional and growing kids.

We train for C, C++, Java, JavaScript, VB Script, VB dot net, ASP dot net, PHP, Oracle and more.

When does course begin? You can start anytime after the registration, as we do not take more than two candidates in a batch. 

Duration of class: Each class goes for two-hour session, but if needed it can be extended to 4-5 hours to finish the course quickly, Weekend (Fri and Sat only) also available. Flexible days and time are available for training.