Course Description

THE RATIONALE

The Data Science with R programming certification training covers data exploration, data visualization, predictive analytics, and descriptive analytics techniques with the R language. You will learn about R packages, how to import and export data in R, data structures in R, various statistical concepts, cluster analysis, and forecasting.

The Big Data Analytics market is expected to reach $40.6 billion by 2023, at a growth rate of 29.7-percent. Randstad reports that pay hikes in the analytics industry are 50-percent higher than the IT industry. Learning R can help you begin a career in data science.

THE COURSE OBJECTIVES:

By the end of this training, participants will be able to:

• Understand and work on statistical concepts like linear & logistic regression, cluster analysis and forecasting.
• Develop a structured approach to use statistical techniques and R language.
• Work on data exploration, data visualization and predictive modeling techniques with ease.
• Gain fundamental knowledge on Analytics and how it assists in decision making.
• Work with confidence in R language.
• Perform sharp data analysis to make business decisions.

THE COURSE OUTLINE

The course will cover the following:

MODULE 1:  BUSINESS ANALYTICS FOUNDATION WITH R TOOLS14:0

• Business Analytics Foundation With R Tools
• Analytics
• Places Where Analytics is Applied
• Career Path

MODULE 2:  INTRODUCTION TO ANALYTICS

• Introduction To Analytics
• What Is Analytics
• Analytics Vs Analysis
• Role Of A Data Scientist
• Data Analytics Methodology
• Problem Definition
• Summarizing Data
• Data Collection
• Data Dictionary
• Outlier Treatment

MODULE 3: STATISTICAL CONCEPTS & THEIR APPLICATION IN BUSINESS

• Statistical Concepts &Their Application In Business
• Descriptive Statistics
• Probability Theory
• Tests of Significance
• Non-parametric Testing

MODULE 4: BASIC ANALYTIC TECHNIQUES – USING R

• Introduction
• Data Exploration
• Data Visualization
• Pie Charts
• Correlation
• Analysis of variance
• Chi-squared test
• T-test

MODULE 5: PREDICTIVE MODELLING TECHNIQUES I

• Predictive Modelling Techniques
• Regression Analysis & Types Of Regression Models
• Linear Regression
• Coefficient Of Determination R
• How Good Is The Model
• How To Find Liner Regression Equation
• Commands To Perform Linear Regression
• Linear Regression To Predict Sales
• Case Study – Linear Regression
• Case Study – Classification
• Logistic Regression
• Example – Logistic Regression In R
• Logistic Regression Predicting Recurrent Visits To A Web Site

MODULE 6: PREDICTIVE MODELLING TECHNIQUES II

• Cluster Analysis
• Command To Perform Clustering In R
• Hierarchical Clustering
• Case Study – Implement K Means And Hierarchical Clustering
• Time Series
• Cyclical Versus Seasonal Analysis
• Decomposition Of Time Series
• Case Study- Time Series Analysis
• Decomposing Non-Seasonal Time Series

MODULE 7: PREDICTIVE MODELLING TECHNIQUES III

• Exponential Smoothing
• Advantaged And Disadvantages Of Exponential Smoothing
• Exponential Smoothing And Forecasting In R
• Example – Holt Winters
• White Noise
• Correlogram Analysis
• Box-Jenkins Forecasting Models
• Case Study – Time Series Data Using Arma
• Business Case
• Summary

TARGET AUDIENCE

This Data Science with R certification training is beneficial for:

• Aspiring data scientists including, IT professionals or software developers looking to make a career switch into Data analytics
• Professionals working in data and business analysis
• Graduates / students wishing to build a career in Data Science
• Experienced professionals willing to harness Data Science in their fields

Delivery Method: Combines lectures, discussions, group exercises and illustrations

Venue:

Fee:

Duration: 1 week

Course Date: July 10th -14th 2023