Syllabus

unit 1

• Data science in real life
• defining data science
• what data science people do
• data science in Business

• use case for data science

• Intro to R

• importance of R
• Datatype
• variables
• operators
• conditional statements
• loops
• R script
• functions in R

Unit 2

• Data Preprocessing
• Discrete random variables
• Continuous random variable
• Missing values
• Data-collection
• staging
• cleaning
• transformation
• consolidation
• data consistency

Unit 3

• Into to data visualization
• data visualization using Graphics in R
• use of colors, size, shape
• function of ggplot
• file formats of graphic Outputs

unit 4

• intro machine learning
• intro modern data analysis
• definition of machine learning
• classification
• K-nearest
• naive bayes
• decision tree
• evaluation of performance Measure
• Accuracy
• precision
  Recall
  F-measure
  confusion-matrix
  Kappa
  learning curves-ROC
• Regression Analysis
• Clustering
• Association rules

unit 5

• statistics for DAta Science
• discrete random variable
• continuos random variable
• MArkov-chain monte Cairo
• Descriptive Statistics
• sample covariance
• sample covariance matrix
• outlier
• bayesian classification
• central limit theorem
• data exploration and preparation
• confidence interval
• hypothesis-testing
• Z-score