Data Science using R Instructor-led Training

Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data.

Data science can be defined as a blend of mathematics, business acumen, tools, algorithms and machine learning techniques, all of which help us in finding out the hidden insights or patterns from raw data which can be of major use in the formation of big business decisions.

Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. In order to uncover useful intelligence for their organizations, data scientists must master the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process.

Data scientists, statisticians, and analysts—anyone who needs to make sense of data, really—can use R for statistical analysis, data visualization, and predictive modeling.

Data science is an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge. The goal of “R for Data Science” is to help you learn the most important tools in R that will allow you to do data science.

Data Science with R Course Course SyllabusModule 1: Introduction to Data Science

• What is Data Science?
• What is Machine Learning?
• What is Deep Learning?
• What is AI?
• Data Analytics & it’s types

Module 2: Introduction to R

• What is R?
• Why R?
• Installing R
• R environment
• How to get help in R
• R Studio Overview

Module 3: R Basics

• Environment setup
• Data Types
• Variables Vectors
• Lists
• Matrix
• Array
• Factors
• Data Frames
• Loops
• Packages
• Functions
• In-Built Data sets

Module 4: R Packages

• DMwR
• Dplyr/plyr
• Caret
• Lubridate
• E1071
• Cluster/FPC
• Data.table
• Stats/utils
• ggplot/ggplot2
• Glmnet

Module 5: Importing Data

• Reading CSV files
• Saving in Python data
• Loading Python data objects
• Writing data to CSV file

Module 6: Manipulating Data

• Selecting rows/observations
• Rounding Number
• Selecting columns/fields
• Merging data
• Data aggregation
• Data munging techniques

Module 7: Statistics Basics

• Central Tendency
  
  • Mean
  • Median
  • Mode
  • Skewness
  • Normal Distribution
• Probability Basics
  
  • What does it mean by probability?
  • Types of Probability
  • ODDS Ratio?
• Standard Deviation
  
  • Data deviation & distribution
  • Variance
• Bias variance Tradeoff
  
  • Underfitting
  • Overfitting
• Distance metrics
  
  • Euclidean Distance
  • Manhattan Distance
• Outlier analysis
  
  • What is an Outlier?
  • Inter Quartile Range
  • Box & whisker plot
  • Upper Whisker
  • Lower Whisker
  • Scatter plot
  • Cook’s Distance
• Missing Value treatments
  
  • What is an NA?
  • Central Imputation
  • KNN imputation
  • Dummification
• Correlation
  
  • Pearson correlation
  • Positive & Negative correlation

Module 8: Error MetricsClassification

• • Confusion Matrix
  • Precision
  • Recall
  • Specificity
  • F1 Score
• Regression
  
  • MSE
  • RMSE
  • MAPE

Module 9: Machine LearningModule 10: Supervised Learning

• Linear Regression
  
  • Linear Equation
  • Slope
  • Intercept
  • R square value
• Logistic regression
  
  • ODDS ratio
  • Probability of success
  • Probability of failure
  • ROC curve
  • Bias Variance Tradeoff

Module 11: Unsupervised Learning

• K-Means
• K-Means ++
• Hierarchical Clustering

Module 12: Machine Learning using R

• Linear Regression
• Logistic Regression
• K-Means
• K-Means++
• Hierarchical Clustering – Agglomerative
• CART
• 5.0
• Random forest
• Naïve Bayes

Everyone

Passion to achieve your goals

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Uplatz Certificate of Completion

Digital certificate - Included