Fundamentals of Data Science
INTRODUCTION OF DATA SCIENCE
• Data Science : Definition
• Data Science Venn Diagram : Diagram, Labels, Details
• Basic Terminology – List, Definition
• Data Science Case Studies: 3
• Types of Data – List, Definition, Details, Examples
• Levels of Data – List, Definition with Measures & Examples
• Types of Data Analytics – List, Definition
• Five Steps of Data Science
• Asking an interesting question, Obtain the data, Exploring the data, Modeling the data,
Communicating and visualizing the results
MATHEMATICAL PRELIMINARIES
Basic Maths
• Symbols and Terminology
• Vectors and matrices - Arithmetic symbols – Summation - Dot product – Graphs Logarithms/exponents
• Probability
• Definitions, procedure, event, sample space, notation, frequency, universe
• Bayesian Vs Frequentist
• theoretical Vs experiment, relative frequency, law of large numbers
• Compound Events
• Two or more events, mean, P(A u B), P(A n B)
• Conditional Probability
• Transforming, P(A|B)
• Rules of Probability
• Addition Rule, Mutual Exclusivity, Multiplication Rule, Independence,
Complementary Events (Confusion Matrix/ Binary Classifier)
DATA MINING AND DATA WAREHOUSING
• Introduction to Data Warehousing
• Design Consideration of Data Warehouse
• Data Loading Process
• Case Study
Data Structures and Algorithms
Basic Concepts
• Algorithms
• Basic steps in complete development of Algorithm
• Analysis and complexity of Algorithm
• Asymptotic notations
• Problem Solving techniques and examples
• Data Structures
• List ADT
• Stacks ADT
• Queue ADT
Algorithm Design Model & Trees
• Algorithm Design Model
• Greedy Method
• Divide and Conquer
• Dynamic Programming
• Backtracking
• Branch and Bound.
• Trees
• Preliminaries Binary Tree
• Search Tree ADT
• Binary Search Trees
• AVL Trees
• Tree Traversals
• B-Trees.