SEMINAR PRESENTATION FOR DATA SCIENCE
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It is a seminar project made by me . It is about the the data science and it's uses. As computer science student this is a important to manage the data and how to deal with easily and efficiently.
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The Data Analytics Lifecycle is designed specifically for Big Data problems and data science projects. The lifecycle has six phases, and project work can occur in several phases at once. For most phases in the lifecycle, the movement can be either forward or backward. This iterative depiction of the lifecycle is intended to more closely portray a real project, in which aspects of the project move forward and may return to earlier stages as new information is uncovered and team members learn more about various stages of the project. This enables participants to move iteratively through the process and drive toward operationalizing the project work.
Phase 1—Discovery: In Phase 1, the team learns the business domain, including relevant history such as whether the organization or business unit has attempted similar projects in the past from which they can learn. The team assesses the resources available to support the project in terms of people, technology, time, and data. Important activities in this phase include framing the business problem as an analytics challenge that can be addressed in subsequent phases and formulating initial hypotheses (IHs) to test and begin learning the data.
Phase 2—Data preparation: Phase 2 requires the presence of an analytic sandbox, in which the team can work with data and perform analytics for the duration of the project. The team needs to execute extract, load, and transform (ELT) or extract, transform and load (ETL) to get data into the sandbox. The ELT and ETL are sometimes abbreviated as ETLT. Data should be transformed in the ETLT process so the team can work with it and analyze it. In this phase, the team also needs to familiarize itself with the data thoroughly and take steps to condition the data.
UNIT - 1 : Part 1: Data Warehousing and Data Mining
The document provides an overview of data warehousing and data mining. It discusses how data warehousing transforms data into information to support decision making. It contrasts operational systems optimized for transactions with data warehouses designed for analysis. Data warehouses integrate data from multiple sources and support multidimensional analysis and ad-hoc queries. The document also introduces data mining as a way to extract intelligence from warehouse data.
QuickView #3 - Big Data
Forecast to contribute £216 billion to the UK economy via business creation, efficiency and innovation, and generate 360,000 new jobs by 2020, big data is a key area for recruiters.
In this QuickView:
- Big data in numbers
- Top 10 industries hiring big data professionals
- Top 10 qualifications sought by hirers
- Top 10 database and BI skills sought by hirers
- Getting started in big data: popular big data techniques and vendors
Quick view Big Data, brought by Oomph!, courtesy of our partner Sonovate
Quick view Big Data, brought by Oomph!, courtesy of our partner Sonovate
contact:
contact@oomphrecruitment.com
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Mis 2
This document discusses emerging information technologies that can improve organizational performance, including expert systems, neural networks, geographic information systems, groupware, and internet applications. It provides an overview of key topics such as the evolution of IT infrastructure, advantages and challenges of the internet and groupware, how expert systems and neural networks can be used, applications of geographic information systems, and contemporary trends in hardware/software platforms and management issues when adopting new technologies.
Real World Application of Big Data In Data Mining Tools
The main aim of this paper is to make a study on the notion Big data and its application in data mining tools like R, Weka, Rapidminer, Knime,Mahout and etc. We are awash in a flood of data today. In a broad range of application areas, data is being collected at unmatched scale. Decisions that previously were based on surmise, or on painstakingly constructed models of reality, can now be made based on the data itself. Such Big Data analysis now drives nearly every aspect of our modern society, including mobile services, retail, manufacturing, financial services, life sciences, and physical sciences. The paper mainly focuses different types of data mining tools and its usage in big data in knowledge discovery.
Introduction to Data Analytics and data analytics life cycle
The document provides an overview of data analytics and big data concepts. It discusses the characteristics of big data, including the four V's of volume, velocity, variety and veracity. It also describes different types of data like structured, semi-structured and unstructured data. The document then introduces big data platforms and tools like Hadoop, Spark and Cassandra. Finally, it discusses the need for data analytics in business, including enabling better decision making and improving efficiency.
Complete-SRS.doc
The software development process is complete for computer project analysis, and it is important to the evaluation of the random project. These practice guidelines are for those who manage big-data and big-data analytics projects or are responsible for the use of data analytics solutions. They are also intended for business leaders and program leaders that are responsible for developing agency capability in the area of big data and big data analytics .
For those agencies currently not using big data or big data analytics, this document may assist strategic planners, business teams and data analysts to consider the value of big data to the current and future programs.
This document is also of relevance to those in industry, research and academia who can work as partners with government on big data analytics projects.
Technical APS personnel who manage big data and/or do big data analytics are invited to join the Data Analytics Centre of Excellence Community of Practice to share information of technical aspects of big data and big data analytics, including achieving best practice with modeling and related requirements. To join the community, send an email to the Data Analytics Centre of Excellence
L3 Big Data and Application.pptx
This document provides an introduction to big data and data science from Amity Institute of Information Technology. It defines big data and data science, highlighting that big data is a subset of data science. The key differences between big data and data science are described. Examples of applications of big data in various domains like social media, healthcare, finance, ecommerce and education are outlined. Finally, the skills required to become a data scientist or big data specialist are summarized.
ii mca juno
Data mining involves using analytical techniques to discover patterns in large data sets. It is used to gain insights into business problems like predicting customer behavior or identifying fraud. The key steps in data mining include requirement analysis, data collection/preparation, exploration of techniques, implementation/evaluation, and visualization of results. Applications include prediction, relationship marketing, customer profiling, outlier detection, and customer segmentation.
Unit-1 -2-3- BDA PIET 6 AIDS.pptx
The document discusses the syllabus for a course on Big Data Analytics. The syllabus covers four units: (1) an introduction to big data concepts like distributed file systems, Hadoop, and MapReduce; (2) Hadoop architecture including HDFS, MapReduce, and YARN; (3) Hadoop ecosystem components like Hive, Pig, HBase, and Spark; and (4) new features of Hadoop 2.0 like high availability for NameNode and HDFS federation. The course aims to provide students with foundational knowledge of big data technologies and tools for processing and analyzing large datasets.
13 pv-do es-18-bigdata-v3
1. Determine if a Big Data approach is suitable based on factors like volume, variety and velocity of data as well as the need for iterative, exploratory analysis.
2. Use techniques like Hadoop, MapReduce and NoSQL databases that can analyze large, diverse, unstructured datasets in a distributed, parallel manner.
3. Follow data management best practices like data governance, quality checks, and master data management to ensure clean, well-organized data.
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SEMINAR PRESENTATION FOR DATA SCIENCE
- 1. TITLE:PRESENTATION ON hyper transport Presented by:- Name-Goutam Nayak Section-ECE Semester:-3rd Regd no-2201298516 Guided by-
- 2. What Is Hyper Transport: Hyper Transport is a scalable packet-based, high- bandwidth, and low-latency point-to-point interconnect technology intended to interconnect processors and also link them to I/O peripheral devices. Hyper Transport was initially devised as an efficient replacement for traditional system buses for on-board communications.
- 3. History Of Hyper Transport: • In earlier time there is no much more data of user in the internet so all data are stores in excel form .But gradually number user increases so to store the data of user large no of ware house are made. This data is used to deal with real world problem like to improve the business strategy improve user experience.
- 4. Data collection : The first step of data science is data collection .This involves gathering data from various sources and cleaning it to ensure accuracy .It’s important to use reliable sources and appropriate methods for collecting data. Data is collected from different source like 1.web data, e-commerce 2.Financial transactions, bank/credit transactions 3.Online trading and purchasing 4.Social network
- 5. Data analysis: Data analysis involves exploring and interpreting data to identify patterns and trends. This can be done using statistical methods and visualization tools .It’s important to have a hypothesis and a plan analysis. According to a report Google process 20PB data daily. Facebook has 60TB of daily logs. eBay has 6.5PB of user data.
- 6. Machine learning: Machine learning is a subset of data science that involves using algorithm to learn patterns from data. It can be used for predication and classification tasks. It’s important to choose the right algorithm and evaluates its performance.
- 7. Feature of data science:
- 8. Conclusion: Data science is a powerful tools for extracting insights from data. To be effective, it requires a combination of technical skills and domain expertise . By following the techniques and strategies discussed one can become a more effective data scientist.