Data Science - R Programming certification eLearning

Data science - Programming R certification eLearning

Learn how to extract knowledge and ideas from structured and unstructured data.

COURSE OVERVIEW

R is a programming language and free software environment for statistical computing. This data science course teaches you various data analysis techniques using the R programming language. You will also master data mining, visualization, predictive and descriptive analytics techniques.

During the course, you will receive hands-on training by implementing various real-world industry-based projects in healthcare, retail, insurance, and many others.

This data science course is an ideal package for aspiring data analysts who aspire to a successful career in analytics/data science. You will gain a 360-degree overview of business analysis and R, using real-life projects and case studies.

WHAT IS INCLUDED?

• Course and material are in english
• Beginner - intermediate level
• 1 year access to the self-paced study eLearning platform 24/7 
• 6 hours of eLearning video content
• 40 hours recommended study time & practices
• Virtual labs, Quizzes, Test simulation, End-Projects
• No exam for the course but student will get certification of training completion

COURSE OBJECTIVES You will learn:

• By the end of the course, you will be able to use:
• The different R graphs for data visualization
• The method of hypothesis testing to drive business decisions
• Linear and non-linear regression models and classification techniques for data analysis
• The different association rules and the Apriori algorithm
• Clustering methods, including K-means, DBSCAN and hierarchical clustering.
• Acquire a basic understanding of business analysis and the various statistical concepts
• Install R, R-studio and configure the workspace
• Master R programming and understand how the various instructions are executed in R
• Understand the data structure used in R and import/export data
• Define, understand and use the various application functions and DPYR functions
• Understand the various statistical concepts and hypothesis tests

Who Should Enroll in this Program?

There is an increasing demand for skilled data scientists across all industries, making this data science certification course well-suited for participants at all levels of experience.

• IT professionals
• Analytics Professionals
• Software Developers
• Data scientist
• Business Intelligent

There is no formal requirement for this course. However, it is recommended to have:

Basic Statistics: A fundamental understanding of statistics (mean, median, standard deviation, etc.) will aid in grasping the course content, especially when learning data analysis techniques.

Mathematics Fundamentals: Basic math skills, especially in areas like algebra and probability, will help in understanding some of the more advanced data analysis and modeling topics.

Familiarity with Data: A basic understanding of datasets, data types (numerical, categorical), and structures like tables will be useful.

Course content

Introduction to Business Analytics

• Business Decisions and Analytics
• Types of Business Analytics
• Applications of Business Analytics
• Data Science Overview

Introduction to R Programming

• Importance of R
• Data Types and Variables in R
• Operations in R
• Conditional Statements in R
• Loops in R

Data Structures

• Identify Data Structures
• Demo: Identify Data Structures
• Assigning Values to Data Structures
• Data Manipulation
• Demo: Assigning Values and Applying Functions

Data Visualization

• Introduction to Data Visualization
• Data Visualization Using Graphics in R
• Ggplot2
• File Formats of Graphic Outputs R

Statistics for Data Science-I

• Introduction to Hypothesis
• Types of Hypothesis
• Data Sampling
• Confidence and Significance Levels

Statistics for Data Science - II

• Hypothesis Test
• Parametric Test
• Non-Parametric Test
• Hypothesis Tests about Population Means
• Hypothesis Tests about Population Variance
• Hypothesis Tests about Population Proportions

Regression Analysis

• Introduction to Regression Analysis
• Types of Regression Analysis Models
• Linear Regression
• Demo: Simple Linear Regression
• Non-Linear Regression
• Demo: Regression Analysis with Multiple Variables
• Cross Validation
• Non-linear to Linear Models
• Principal Component Analysis
• Factor Analysis

Classification

• Classification and its Types
• Logistic Regression
• Support Vector Machines
• Demo: Naive Bayes Classifier
• Demo: Naive Bayers Classifier
• Decision: Tree Classification
• Demo: Decision Tree Classification
• Random Forest Classification
• Evaluating Classifier Models
• Demo: K-Fold Cross Validation

Clustering

• Introduction to Clustering
• Clustering Methods
• Demo: K-means Clustering
• Demo: Hierarchical Clustering

Association

• Association Rule
• Apriori Algorithm
• Demo: Apriori Algorithm