📘 AM512PE

Introduction to Data Science

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IDS Unit 1: Introduction

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IDS Unit 2: Data Types & Statistical Description

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IDS Unit 3.1: R Data Structures

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IDS Unit 3.2: R Data Structures

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IDS Unit 4: Control Flow and Functions

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IDS Unit 5: Data Visualization and Regression

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Syllabus Overview

UNIT - I: Introduction

Definition of Data Science

  • Big Data and Data Science Hype
  • Datafication and Current Landscape

Statistical Inference

  • Populations and Samples
  • Statistical Modeling and Probability Distributions
  • Model Fitting and Overfitting

Basics of R

  • Introduction to R
  • R-Environment Setup
  • Programming with R
  • Basic Data Types

UNIT - II: Data Types & Statistical Description

Types of Data

  • Attributes and Measurement
  • Different Types of Attributes: Asymmetric, Binary, Nominal, Ordinal, Numeric
  • Discrete vs. Continuous Attributes

Basic Statistical Descriptions

  • Central Tendency: Mean, Median, Mode
  • Dispersion: Range, Quartiles, Variance, Standard Deviation, Interquartile Range
  • Graphic Displays of Statistical Descriptions

UNIT - III: R Data Structures

Vectors

  • Creating and Naming Vectors
  • Vector Arithmetic and Subsetting

Matrices

  • Creating and Naming Matrices
  • Matrix Subsetting and Arrays

Factors and Data Frames

  • Introduction to Factors: Factor Levels, Ordered Factors
  • Introduction to Data Frames: Subsetting, Extending, Sorting

Lists

  • Creating and Accessing Lists
  • Manipulating, Merging Lists, and Converting to Vectors

UNIT - IV: Control Flow and Functions

Conditionals and Control Flow

  • Relational Operators and Vectors
  • Logical Operators and Conditional Statements

Iterative Programming in R

  • While Loop, For Loop, Looping Over Lists

Functions in R

  • Writing Functions: Nested Functions, Scoping, Recursion
  • Loading R Packages
  • Mathematical Functions in R

UNIT - V: Data Visualization and Regression

Charts and Graphs

  • Pie Chart, Bar Chart, Box Plot, Histogram, Line Graph, Scatter Plot

Regression

  • Linear Regression Analysis
  • Multiple Linear Regression
Introduction to Data Science Notes