Data Science with R

Overview

Data Science with R course covers topics like exploratory data analysis, statistics fundamentals, hypothesis testing, regression & classification modeling techniques and machine learning algorithms. Participants will learn how to create R programs that will help discover and interpret relationships in complex information and solve real world problems.

Objectives

At the end of Data Science with R training course, participants will

Get acquainted with various analysis and visualization tools such as Ggplot and plotly
Understand the behavior of data; build significant models to understand Statistics Fundamentals
Learn about the various R libraries like Dplyr, Data.table used to manipulate data
Use R libraries and work on data manipulation, data preparation and data explorations
Use of R graphics libraries like Ggvis, Plotly etc.
Learn Supervised and Unsupervised Machine Learning Algorithms

Prerequisites

Participants are expected to have basic programming knowledge.

Course Outline

Introduction to Data Scienceedforce2021-06-30T13:16:30+05:30

Introduction to Data Science

What is Data Science?
Analytics Landscape
Life Cycle of a Data Science Project
Data Science Tools & Technologies

Mastering Redforce2021-06-30T13:17:11+05:30

Mastering R

Intro to R Programming
Installing and Loading Libraries
Data Structures in R
Control & Loop Statements in R
Functions in R
Loop Functions in R
String Manipulation & Regular Expression in R
Working with Data in R
Data Visualization in R
Case Study

Probability & Statisticsedforce2021-06-30T13:18:00+05:30

Probability & Statistics

Measures of Central Tendency
Measures of Dispersion
Descriptive Statistics
Probability Basics
Marginal Probability
Bayes Theorem
Probability Distributions
Hypothesis Testing

Advanced Statistics & Predictive Modeling – Iedforce2021-06-30T13:18:19+05:30

Advanced Statistics & Predictive Modeling – I

ANOVA
Linear Regression (OLS)
Case Study: Linear Regression
Principal Component Analysis
Factor Analysis
Case Study: PCA/FA

Advanced Statistics & Predictive Modeling – IIedforce2021-06-30T13:18:36+05:30

Advanced Statistics & Predictive Modeling – II

Logistic Regression
Case Study: Logistic Regression
K-Nearest Neighbor Algorithm
Case Study: K-Nearest Neighbor Algorithm
Decision Tree
Case Study: Decision Tree

Time Series Forecastingedforce2021-06-30T13:19:07+05:30

Time Series Forecasting

Understand Time Series Data
Visualizing TIme Series Components
Exponential Smoothing
Holt’s Model
Holt-Winter’s Model
ARIMA
Case Study: Time Series Modeling on Stock Price

2023-01-06T14:06:50+05:30

I recently attended the AWS Security Training with edForce and had a great first-hand experience. The trainer Anurag, and facilitator Ashutosh, both delivered the content effectively, ensuring that I thoroughly understood the concepts. The training tools and materials were comprehensive and effective in helping me grasp the content. I am looking forward to attending additional trainings with edForce to continue improving my professional knowledge. – Lavkesh Jain

I have attended a session regarding Docker and Kubernetes with edForce. It was a very good experience. In a short duration of session, instructor explained almost every detail about the topics to at least start with that tech stack. I am looking forward for more session with edForce in future. – Digvijay Singh Parmar

I have attended few AWS sessions conducted by edForce. Trainers have always been knowledgeable, they covered concepts starting from basics of AWS. The presentations of topics are always very good and in nutshell I found the sessions were very helpful. Thanks you!! – Vinoth Shanmugam

Data Science with R

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