Projects
Assignments
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
Prerequisites
Participants are expected to have basic programming knowledge.
Course Outline
Introduction to Data Scienceedforce2021-06-30T13:16:30+05:30
- 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
- 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
- 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
- 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
- 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
- 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
