In this you will learn about
1. Definitions
2. Introduction to Data Structures
3. Classification of Data structures
a. Primitive Data structures
i. int
ii. Float
iii. char
iv. Double
b. Non- Primitive Data structures
i. Linear Data structures
1. Arrays
2. Linked Lists
3. Stack
4. Queue
ii. Non Linear Data structures
1. Trees
2. Graphs
DEFINITION OF DATA STRUCTURES & ALGORITHM
OVERVIEW OF DATA STRUCTURES
TYPES OF DATA STRUCTURE
LINEAR DATA STRUCTURE
NON-LINEAR DATA STRUCTURE
ABSTRACT DATA TYPE.
In this you know about
Types of Data Structures / Data structures types in C++
1.Primitive and non-primitive data structure
2.Linear and non-linear data structure
3.Static and dynamic data structure
4.Persistent and ephemeral data structure
5.Sequential and direct access data structure
Abstract: Every program whether in c, java or any other language consists of a set of commands which are based on the logic behind the program as well as the syntax of the language and does the task of either fetching or storing the data to the computer, now here comes the role of the word known as “data structure”. In computer science, a data structure is a particular way of organizing data in a computer so that it can be used efficiently. Data structures provide a means to manage large amounts of data efficiently, such as large databases and internet indexing services. Usually, efficient data structures are a key in designing efficient algorithms. Some formal design methods and programming languages emphasize data structures, rather than algorithms, as the key organizing factor in software design. Storing and retrieving can be carried out on data stored in both main memory and in secondary memory. Now as different data structures are having their different usage and benefits, hence selection of the same is a task of importance. “Therefore the paper consists of the basic terms and information regarding data structures in detail later on will be followed by the practical usage of different data structures that will be helpful for the programmer for selection of a perfect data structure that would make the programme much more easy and flexible.Keywords: Data structures, Arrays, Lists, Trees.
Title: Data Structure the Basic Structure for Programming
Author: Shubhangi Johri, Siddhi Garg, Sonali Rawat
International Journal of Recent Research in Mathematics Computer Science and Information Technology
ISSN: 2350-1022
Paper Publications
In this you will learn about
1. Definitions
2. Introduction to Data Structures
3. Classification of Data structures
a. Primitive Data structures
i. int
ii. Float
iii. char
iv. Double
b. Non- Primitive Data structures
i. Linear Data structures
1. Arrays
2. Linked Lists
3. Stack
4. Queue
ii. Non Linear Data structures
1. Trees
2. Graphs
DEFINITION OF DATA STRUCTURES & ALGORITHM
OVERVIEW OF DATA STRUCTURES
TYPES OF DATA STRUCTURE
LINEAR DATA STRUCTURE
NON-LINEAR DATA STRUCTURE
ABSTRACT DATA TYPE.
In this you know about
Types of Data Structures / Data structures types in C++
1.Primitive and non-primitive data structure
2.Linear and non-linear data structure
3.Static and dynamic data structure
4.Persistent and ephemeral data structure
5.Sequential and direct access data structure
Abstract: Every program whether in c, java or any other language consists of a set of commands which are based on the logic behind the program as well as the syntax of the language and does the task of either fetching or storing the data to the computer, now here comes the role of the word known as “data structure”. In computer science, a data structure is a particular way of organizing data in a computer so that it can be used efficiently. Data structures provide a means to manage large amounts of data efficiently, such as large databases and internet indexing services. Usually, efficient data structures are a key in designing efficient algorithms. Some formal design methods and programming languages emphasize data structures, rather than algorithms, as the key organizing factor in software design. Storing and retrieving can be carried out on data stored in both main memory and in secondary memory. Now as different data structures are having their different usage and benefits, hence selection of the same is a task of importance. “Therefore the paper consists of the basic terms and information regarding data structures in detail later on will be followed by the practical usage of different data structures that will be helpful for the programmer for selection of a perfect data structure that would make the programme much more easy and flexible.Keywords: Data structures, Arrays, Lists, Trees.
Title: Data Structure the Basic Structure for Programming
Author: Shubhangi Johri, Siddhi Garg, Sonali Rawat
International Journal of Recent Research in Mathematics Computer Science and Information Technology
ISSN: 2350-1022
Paper Publications
Abstract: Every program whether in c, java or any other language consists of a set of commands which are based on the logic behind the program as well as the syntax of the language and does the task of either fetching or storing the data to the computer, now here comes the role of the word known as “data structure”. In computer science, a data structure is a particular way of organizing data in a computer so that it can be used efficiently. Data structures provide a means to manage large amounts of data efficiently, such as large databases and internet indexing services. Usually, efficient data structures are a key in designing efficient algorithms. Some formal design methods and programming languages emphasize data structures, rather than algorithms, as the key organizing factor in software design. Storing and retrieving can be carried out on data stored in both main memory and in secondary memory. Now as different data structures are having their different usage and benefits, hence selection of the same is a task of importance. “Therefore the paper consists of the basic terms and information regarding data structures in detail later on will be followed by the practical usage of different data structures that will be helpful for the programmer for selection of a perfect data structure that would make the programme much more easy and flexible.
Keywords: Data structures, Arrays, Lists, Trees.
Opendatabay - Open Data Marketplace.pptxOpendatabay
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
1. Data Structures & Algorithms
Lecture 1: Foundational Data
Structures
Ms. Joan Wakasa Murumba
2. Definitions
A data type simply refers to a defined kind of
data, that is, a set of possible values & basic
operations on those values.
When applied in programming languages, a
data type defines a set of values & the
allowable operations on those values.
Types of data in programming languages
include integers, floating point numbers or
characters, tuples, records, ADT, etc.
3. Definitions
Integers: Numbers that do not have decimal
places
Floating point number: A real number (that is,
a number that can contain a fractional part).
The following are floating-point numbers:
3.0 , -111.5 , ½ , 3E-5 etc.
4. A Tuple
A tuple is an ordered set of values.
The separator for each value is often a comma
(depending on the rules of the particular
language).
Common uses for the tuple as a data type are
(1) for passing a string of parameters from one
program to another, and (2) representing a set
of value attributes in a relational database.
5. A Tuple
An example of a tuple that emphasizes the
different data types that may exist within a
tuple data type: 17,*,2.49,Seven
The above example is sometimes referred to
as a 4-tuple, since it contains four values.
6. ADT
An Abstract Data type is defined as a
mathematical model of the data objects that
make up a data type as well as the functions
that operate on these objects.
A set of data values and associated
operations that are precisely specified
independent of any particular implementation
A data type is abstract in the sense that it is
independent of various concrete
implementations
7. Examples of Abstract Data Type
Common abstract data types (ADT)
typically implemented in programming
languages include:
Arrays,
Lists,
Queues,
Stacks and
Trees.
8. Data Types
Data types are important in computer
programmes because they classify data so
that a translator can reserve appropriate
memory storage to hold all possible values,
e.g. integers, real numbers, characters,
strings, and Boolean values
9. A Data Type Consists of:
A domain (a set of values)
A set of operations that may be applied to
the values.
10. Data Type Classification
Simple Data Type: This is where some data
items may be used singly
The simple data types are classified as
follows:
a. Character
b. Numeric integer
c. Numeric real
d. Boolean (logical).
Data Structures: May be combined together
and arranged to form other data items
11. Data Structure
A data structure is the implementation of an
abstract data type in a particular programming
language.
Data structures can also be referred to as “data
aggregate”.
A data structure is an organization of
information, usually in memory, for better
algorithm efficiency, such as queue, stack, list,
and tree, or conceptual unity, such as the name
and address of a person.
12. Data Structure
Data structures are collections of data values,
the relationships among them and functions or
operations that can be applied to that data.
Classification of Data Structures
Data structures are broadly divided into two:
Linear Data Structures
Non-Linear Data Structures.
13. Linear Data Structures
Linear data structures are data structures in
which individual data elements are stored and
accessed linearly in the computer memory.
Examples include: lists, stacks, queues and
arrays
14. Non-Linear Data Structures
A non-linear data structure, as the name
implies, is a data structure in which the data
items are not stored linearly in the computer
memory, but data items can be processed
using some techniques or rules.
Typical non-linear data structures are Trees.
15. Purposes of Data Structures
For implementation of databases, e.g.,
trees
Provide a means of handling large amounts of
data efficiently, e.g. Large database, indexing
services, etc.
Efficient data structures are a key to designing
efficient algorithms
16. A data structure example
Name Age Address Town position
Joshua 22 P. 0 BOX
2285
NYERI student
17. From the above data structure, data
types can be
Data item Data type
Name Text, string
Age Integer, number,
Address Text, Alpha-numeric
Town Text
Position Text
18. Take Away Assignment
Q1. Computer Storage and management is key
in programming and computing. Find out the
relationship it has with data structures and
algorithms. Use suitable illustrations
Q2. Discuss in details A Stack data structure,
giving its components, functions, operations,
strengths and weaknesses and applications
Assignment Due on 13th
July, 2014