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DATA STRUCTURES AND
ALGORITHMS
S.MANIMOZHI
ASSISTANT PROFESSOR,
DEPARTMENT OF CA
BON SECOURS COLLEGE FOR WOMEN,
THANJAVUR
Learn Data Structure and Algorithms
• Data Search
• Processor speed
• Multiple requests
Applications of Data Structure and Algorithms
• Search − Algorithm to search an item in a data structure.
• Sort − Algorithm to sort items in a certain order.
• Insert − Algorithm to insert item in a data structure.
• Update − Algorithm to update an existing item in a data structure.
• Delete − Algorithm to delete an existing item from a data structur
Characteristics of a Data Structure
• Unambiguous − Algorithm should be clear and unambiguous. Each of
its steps (or phases), and their inputs/outputs should be clear and
must lead to only one meaning.
• Input − An algorithm should have 0 or more well-defined inputs.
• Output − An algorithm should have 1 or more well-defined outputs,
and should match the desired output.
• Finiteness − Algorithms must terminate after a finite number of steps.
• Feasibility − Should be feasible with the available resources.
• Independent − An algorithm should have step-by-step directions,
which should be independent of any programming code.
Execution Time Cases
• Worst Case − This is the scenario where a particular data structure
operation takes maximum time it can take. If an operation's worst
case time is ƒ(n) then this operation will not take more than ƒ(n) time
where ƒ(n) represents function of n.
• Average Case − This is the scenario depicting the average execution
time of an operation of a data structure. If an operation takes ƒ(n)
time in execution, then m operations will take mƒ(n) time.
• Best Case − This is the scenario depicting the least possible execution
time of an operation of a data structure. If an operation takes ƒ(n)
time in execution, then the actual operation may take time as the
random number which would be maximum as ƒ(n).
Basic Terminology
• Data − Data are values or set of values.
• Data Item − Data item refers to single unit of values.
• Group Items − Data items that are divided into sub items are called as Group
Items.
• Elementary Items − Data items that cannot be divided are called as Elementary
Items.
• Attribute and Entity − An entity is that which contains certain attributes or
properties, which may be assigned values.
• Entity Set − Entities of similar attributes form an entity set.
• Field − Field is a single elementary unit of information representing an attribute
of an entity.
• Record − Record is a collection of field values of a given entity.
• File − File is a collection of records of the entities in a given entity set.
Write an Algorithm
• There are no well-defined standards for writing algorithms.
• Common constructs can be used to write an algorithm.
• Should know the problem domain, for which we are designing a
solution.
Algorithm Analysis
• A Priori Analysis − This is a theoretical analysis of an algorithm.
• A Posterior Analysis − This is an empirical analysis of an algorithm.
Asymptotic Notations
• Ο Notation
• Ω Notation
• θ Notation
Common Asymptotic Notations
constant − Ο(1)
logarithmic − Ο(log n)
linear − Ο(n)
n log n − Ο(n log n)
quadratic − Ο(n
2
)
cubic − Ο(n
3
)
polynomial − n
Ο(1)
exponential − 2
Ο(n)

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Data strucutre basic introduction

  • 1. DATA STRUCTURES AND ALGORITHMS S.MANIMOZHI ASSISTANT PROFESSOR, DEPARTMENT OF CA BON SECOURS COLLEGE FOR WOMEN, THANJAVUR
  • 2. Learn Data Structure and Algorithms • Data Search • Processor speed • Multiple requests
  • 3. Applications of Data Structure and Algorithms • Search − Algorithm to search an item in a data structure. • Sort − Algorithm to sort items in a certain order. • Insert − Algorithm to insert item in a data structure. • Update − Algorithm to update an existing item in a data structure. • Delete − Algorithm to delete an existing item from a data structur
  • 4. Characteristics of a Data Structure • Unambiguous − Algorithm should be clear and unambiguous. Each of its steps (or phases), and their inputs/outputs should be clear and must lead to only one meaning. • Input − An algorithm should have 0 or more well-defined inputs. • Output − An algorithm should have 1 or more well-defined outputs, and should match the desired output. • Finiteness − Algorithms must terminate after a finite number of steps. • Feasibility − Should be feasible with the available resources. • Independent − An algorithm should have step-by-step directions, which should be independent of any programming code.
  • 5. Execution Time Cases • Worst Case − This is the scenario where a particular data structure operation takes maximum time it can take. If an operation's worst case time is ƒ(n) then this operation will not take more than ƒ(n) time where ƒ(n) represents function of n. • Average Case − This is the scenario depicting the average execution time of an operation of a data structure. If an operation takes ƒ(n) time in execution, then m operations will take mƒ(n) time. • Best Case − This is the scenario depicting the least possible execution time of an operation of a data structure. If an operation takes ƒ(n) time in execution, then the actual operation may take time as the random number which would be maximum as ƒ(n).
  • 6. Basic Terminology • Data − Data are values or set of values. • Data Item − Data item refers to single unit of values. • Group Items − Data items that are divided into sub items are called as Group Items. • Elementary Items − Data items that cannot be divided are called as Elementary Items. • Attribute and Entity − An entity is that which contains certain attributes or properties, which may be assigned values. • Entity Set − Entities of similar attributes form an entity set. • Field − Field is a single elementary unit of information representing an attribute of an entity. • Record − Record is a collection of field values of a given entity. • File − File is a collection of records of the entities in a given entity set.
  • 7. Write an Algorithm • There are no well-defined standards for writing algorithms. • Common constructs can be used to write an algorithm. • Should know the problem domain, for which we are designing a solution.
  • 8. Algorithm Analysis • A Priori Analysis − This is a theoretical analysis of an algorithm. • A Posterior Analysis − This is an empirical analysis of an algorithm.
  • 9. Asymptotic Notations • Ο Notation • Ω Notation • θ Notation
  • 10. Common Asymptotic Notations constant − Ο(1) logarithmic − Ο(log n) linear − Ο(n) n log n − Ο(n log n) quadratic − Ο(n 2 ) cubic − Ο(n 3 ) polynomial − n Ο(1) exponential − 2 Ο(n)