Data Science Process.pptx

This document discusses different types of concept hierarchies that can be used in data warehousing including schema hierarchies, set grouping hierarchies, and operation-derived hierarchies. It also discusses techniques for data discretization such as binning methods and concept hierarchies that reduce data by replacing low-level concepts with higher-level concepts. Finally, it briefly mentions histogram analysis, clustering analysis, and different ways concept hierarchies can be specified including explicitly by users or generated automatically through analysis.

The document provides an overview of data science applications and use cases. It defines data science as using computer science, statistics, machine learning and other techniques to analyze data and create data products to help businesses make better decisions. It discusses big data challenges, the differences between data science and software engineering, and key areas of data science competence including data analytics, engineering, domain expertise and data management. Finally, it outlines several common data science applications and use cases such as recommender systems, credit scoring, dynamic pricing, customer churn analysis and fraud detection with examples of how each works and real world cases.

Defining Data Science • What Does a Data Science Professional Do? • Data Science in Business • Use Cases for Data Science • Installation of R and R studio

Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on November 20, 2013, at the "IBM Developer Days 2013" in Zurich, Switzerland. ABSTRACT There is no question that big data has hit the business, government and scientific sectors. The demand for skills in data science is unprecedented in sectors where value, competitiveness and efficiency are driven by data. However, there is plenty of misleading hype around the terms big data and data science. This presentation gives a professional statistician's view on these terms and illustrates the connection between data science and statistics. The presentation is also available at http://www.statoo.com/BigDataDataScience/.

This document summarizes key aspects of data integration and transformation in data mining. It discusses data integration as combining data from multiple sources to provide a unified view. Key issues in data integration include schema integration, redundancy, and resolving data conflicts. Data transformation prepares the data for mining and can include smoothing, aggregation, generalization, normalization, and attribute construction. Specific normalization techniques are also outlined.

The document discusses major issues in data mining including mining methodology, user interaction, performance, and data types. Specifically, it outlines challenges of mining different types of knowledge, interactive mining at multiple levels of abstraction, incorporating background knowledge, visualization of results, handling noisy data, evaluating pattern interestingness, efficiency and scalability of algorithms, parallel and distributed mining, and handling relational and complex data types from heterogeneous databases.

The document discusses spatial data and spatial databases. It defines spatial data as data related to space, including location, shape, size and orientation of objects. It discusses the types of spatial data like points, lines, polygons and pixels. It also discusses non-spatial data and how spatial data is organized using coordinates. The key properties of spatial data are geometry, distribution of objects in space, temporal changes and data volume. Spatial databases allow for efficient storage and querying of spatial data through the use of spatial data types and indexes.

The document outlines the typical lifecycle of a data science project, including business requirements, data acquisition, data preparation, hypothesis and modeling, evaluation and interpretation, and deployment. It discusses collecting data from various sources, cleaning and integrating data in the preparation stage, selecting and engineering features, building and validating models, and ultimately deploying results.

This document provides an overview of data science including what is big data and data science, applications of data science, and system infrastructure. It then discusses recommendation systems in more detail, describing them as systems that predict user preferences for items. A case study on recommendation systems follows, outlining collaborative filtering and content-based recommendation algorithms, and diving deeper into collaborative filtering approaches of user-based and item-based filtering. Challenges with collaborative filtering are also noted.

Reporting involves users selecting predefined reports to view results in standardized formats like tables and graphs. No person is involved beyond the user requesting the report. Reports have limited flexibility. Analysis provides answers to specific questions by taking any necessary steps through a guided process. It is customized and flexible to the questions being addressed. Modern data analytics tools use statistical modeling approaches like probability, sampling, inference, and machine learning algorithms like neural networks and support vector machines to gain insights from data.

Big data analytics tools from vendors like IBM, Tableau, and SAS can help organizations process and analyze big data. For smaller organizations, Excel is often used, while larger organizations employ data mining, predictive analytics, and dashboards. Business intelligence applications include OLAP, data mining, and decision support systems. Big data comes from many sources like web logs, sensors, social networks, and scientific research. It is defined by the volume, variety, velocity, veracity, variability, and value of the data. Hadoop and MapReduce are common technologies for storing and analyzing big data across clusters of machines. Stream analytics is useful for real-time analysis of data like sensor data.

Data Warehouse Physical Design,Physical Data Model, Tablespaces, Integrity Constraints, ETL (Extract-Transform-Load) ,OLAP Server Architectures, MOLAP vs. ROLAP, Distributed Data Warehouse ,

This document discusses spatial databases and spatial data mining. It introduces spatial databases as databases that store large amounts of space-related data with special data types for spatial information. Spatial data mining extracts patterns and relationships from spatial data. The document also discusses spatial data warehousing with dimensions and measures for spatial and non-spatial data, mining spatial association patterns from spatial databases, techniques for spatial clustering, classification, and trend analysis.

Data mining is the process of automatically discovering useful information from large data sets. It draws from machine learning, statistics, and database systems to analyze data and identify patterns. Common data mining tasks include classification, clustering, association rule mining, and sequential pattern mining. These tasks are used for applications like credit risk assessment, fraud detection, customer segmentation, and market basket analysis. Data mining aims to extract unknown and potentially useful patterns from large data sets.

Outline § Performance scalability § Analytical performance measures § Amdahl’s law and Gustafson-Barsis’ law Performance Analysis

The document discusses frequent itemset mining methods. It describes the Apriori algorithm which uses a candidate generation-and-test approach involving joining and pruning steps. It also describes the FP-Growth method which mines frequent itemsets without candidate generation by building a frequent-pattern tree. The advantages of each method are provided, such as Apriori being easily parallelized but requiring multiple database scans.

This document provides an introduction and overview of data science. It discusses Ravishankar Rajagopalan's educational and professional background working in data science. It then covers various topics related to data science including common applications, required skills, the typical project lifecycle, team aspects, career progression, interviews, and resources for learning. Examples of unusual real-world applications are also summarized, such as using machine learning to optimize inventory levels for an oil and gas company and implementing speech recognition to predict customer intent for a call center.

The document discusses a framework for extracting information from resumes. Resumes are semi-structured documents that contain varying information like different fields, field names, and formats, making them difficult to parse. The proposed framework uses text mining and rule-based parsing to extract keywords from resumes, scores qualifications and skills, clusters the extracted information using DBSCAN, and classifies the resumes using gradient boosting machines. It aims to help recruiters filter and categorize large numbers of resumes more efficiently.

Python is open source and has so many libraries for data wrangling and visualization that makes life of data scientists easier. For data wrangling pandas is used as it represent tabular data and it has other function to parse data from different sources, data cleaning, handling missing values, merging data sets etc. To visualize data, low level matplotlib can be used. But it is a base package for other high level packages such as seaborn, that draw well customized plot in just one line of code. Python has dash framework that is used to make interactive web application using python code without javascript and html. These dash application can be published on any server as well as on clouds like google cloud but freely on heroku cloud.

This document outlines key concepts related to data processing including: - Data refers to facts and observations represented by symbols. Data processing manipulates data to transform it into useful information. - Data processing activities include tools to convert data into information, from manual to electronic tools. - The data processing cycle includes input, processing, output, and storage steps. - Data hierarchy shows the arrangement of data from fields to records to files to databases.

Hardware and networking service is very important in ICT.

Documents contain metadata and hidden information that can be used to disclose private data and to fingerprint an organisation and its network computers. This document shows what kinds of data can be found, how to extract them and proposes some solutions to the problem stated here.

This document provides an overview of the topics that will be covered in a database systems textbook. It introduces the major parts of the book, including relational databases, database design, data storage and querying, transaction management, and database architectures. Each chapter is briefly described to give the reader an understanding of what concepts will be discussed in more depth throughout the textbook.

The document provides an introduction to databases including: - The structure of databases and the hierarchy of data types - The differences between file-based and database approaches - The components of a database system including the database, DBMS, applications, users, and tools - The purposes of using a database including storing, finding, and analyzing information

This document provides an overview of data science and key concepts in data. It defines data science and describes the data value chain, which identifies the main activities in generating value from data: data acquisition, analysis, curation, storage, and usage. It also defines different data types such as structured, unstructured, and semi-structured data. The document discusses characteristics of big data, including the 3Vs of volume, velocity, and variety as well as other characteristics like veracity and variability. Finally, it outlines the typical big data lifecycle of ingesting, persisting, computing/analyzing, and visualizing data.

Data science involves extracting knowledge from data to solve business problems. The data science life cycle includes defining the problem, collecting and preparing data, exploring the data, building models, and communicating results. Data preparation is an essential step that can consume 60% of a project's time. It involves cleaning, transforming, handling outliers, integrating, and reducing data. Models are built using machine learning algorithms like regression for continuous variables and classification for discrete variables. Results are visualized and communicated effectively to clients.

The document introduces databases and their components. It defines key terms like data, information, database, DBMS, and discusses the evolution from file-based systems to database systems. The main advantages of database systems are minimal data redundancy, sharing of data across systems, improved data consistency when values are stored and updated in one place.

This is prepared from the book- Database System Concepts, written by ©Silberschatz, Korth and Sudarshan.

Description

The document describes a business intelligence software called Qiagram that allows non-technical domain experts to easily explore and query complex datasets through a visual drag-and-drop interface without SQL or programming knowledge. It provides centralized data management, integration with various data sources, and self-service visual querying capabilities to help researchers gain insights from their data.

The document provides an overview of fundamentals of database design including definitions of key concepts like data, information, and databases. It discusses the purpose of databases and database management systems. It also covers topics like selecting a database system, database development best practices, and data entry considerations.

This document discusses best practices for data organization, documentation, and metadata. It recommends using open standard file formats that will remain readable over time, consistent file naming conventions with descriptive names, and version control for files. Metadata should include descriptive, technical, and administrative information to document the data and ensure it can be understood and managed. Good documentation involves information on the data collection process and dataset structure.

Techedo Technologies provides the best Data Science Course in Chandigarh. Both offline/online classes are available here. Data science is a term used to explain a whole branch of Scouring | extraction | Retrieving | Processing | Taking output of data using different tolls, programming, platforms etc. Data science is all about taking out, use and process data or any one among mentioned tasks irrespective to the software or platform you are using. You may use MS-excel, SQL, R-Language, Python or any other medium for it. Anything you will to work on data comes under data science. Data science is one of the most processing career today. For More Details: Visit: https://www.techedo.com/data-Science-course-chandigarh.php Call: 7837505001, 7717255001, 8198055001, 0172-5275001, 0172-5265001

Watch here: https://bit.ly/3cZGCxr For their machine learning and data science projects to be successful, data scientists need access to all of the enterprise data delivered through their myriad of data models. However, gaining access to all data, integrated into a central repository has been a challenge. Often 80% of the project time is spent on these tasks. But, a virtual layer can help the data scientist speed up some of the most tedious tasks, like data exploration and analysis. At the same time, it also integrates well with the data science ecosystem. There is no need to change tools and learn new languages. The data virtualization platform helps data scientists offload these data integration tasks, allowing them to focus on advanced analytics. In this session, you will learn how data virtualization: - Provides all of the enterprise data, in real-time, and without replication - Enables data scientists to create and share multiple logical models using simple drag and drop - Provides a catalog of all business definitions, lineage, and relationships

EndNote is a reference management software that allows users to: - Search online databases and import references into a personal library - Insert formatted citations from the library directly into a research paper - Automatically generate bibliographies in the desired style While its interface is easy to use, it takes time to learn to fully utilize EndNote's features for organizing, sorting, and formatting references. Only EndNote can open compressed EndNote libraries for sharing full libraries.

This document discusses using the Natural Language Toolkit (NLTK) for keyword research and analysis. It provides instructions on installing NLTK and other Python libraries, preparing keyword data, and running scripts to classify and cluster keywords to identify trends and topics. The document demonstrates how to automate aspects of keyword research using NLTK to help analyze large datasets.

Page 1/8 Goal: Implement a complete search engine. Milestones Overview Milestone Goal #1 Produce an initial index for the corpus and a basic retrieval component #2 Complete Search System Page 2/8 PROJECT: SEARCH ENGINE Corpus: all ICS web pages We will provide you with the crawled data as a zip file (webpages_raw.zip). This contains the downloaded content of the ICS web pages that were crawled by a previous quarter. You are expected to build your search engine index off of this data. Main challenges: Full HTML parsing, File/DB handling, handling user input (either using command line or desktop GUI application or web interface) COMPONENT 1 - INDEX: Create an inverted index for all the corpus given to you. You can either use a database to store your index (MongoDB, Redis, memcached are some examples) or you can store the index in a file. You are free to choose an approach here. The index should store more than just a simple list of documents where the token occurs. At the very least, your index should store the TF-IDF of every term/document. Sample Index: Note: This is a simplistic example provided for your understanding. Please do not consider this as the expected index format. A good inverted index will store more information than this. Index Structure: token – docId1, tf-idf1 ; docId2, tf-idf2 Example: informatics – doc_1, 5 ; doc_2, 10 ; doc_3, 7 You are encouraged to come up with heuristics that make sense and will help in retrieving relevant search results. For e.g. - words in bold and in heading (h1, h2, h3) could be treated as more important than the other words. These are useful metadata that could be added to your inverted index data. Optional (1 point for each meta data item up to 2 points max):: Extra credit will be given for ideas that improve the quality of the retrieval, so you may add more metadata to your index, if you think it will help improve the quality of the retrieval. For this, instead of storing a simple TF-IDF count for every page, you can store more information related to the page (e.g. position of the words in the page). To store this information, you need to design your index in such a way that it can store and retrieve all this metadata efficiently. Your index lookup during search should not be horribly slow, so pay attention to the structure of your index COMPONENT 2 – SEARCH AND RETRIEVE: Your program should prompt the user for a query. This doesn’t need to be a Web interface, it can be a console prompt. At the time of the query, your program will look up your index, perform some calculations (see ranking below) and give out the ranked list of pages that are relevant for the query.   COMPONENT 3 - RANKING: At the very least, your ranking formula should include tf-idf scoring, but you should feel free to add additional components to this formula if you think they improve the retrieval. Optional (1 point for each parameter up to 2 points max): Extra credit will be given if your ranking formula includes par.

Pyspark

This study primarily aimed to determine the best practices of clothing businesses to use it as a foundation of strategic business advancements. Moreover, the frequency with which the business's best practices are tracked, which best practices are the most targeted of the apparel firms to be retained, and how does best practices can be used as strategic business advancement. The respondents of the study is the owners of clothing businesses in Talavera, Nueva Ecija. Data were collected and analyzed using a quantitative approach and utilizing a descriptive research design. Unveiling best practices of clothing businesses as a foundation for strategic business advancement through statistical analysis: frequency and percentage, and weighted means analyzing the data in terms of identifying the most to the least important performance indicators of the businesses among all of the variables. Based on the survey conducted on clothing businesses in Talavera, Nueva Ecija, several best practices emerge across different areas of business operations. These practices are categorized into three main sections, section one being the Business Profile and Legal Requirements, followed by the tracking of indicators in terms of Product, Place, Promotion, and Price, and Key Performance Indicators (KPIs) covering finance, marketing, production, technical, and distribution aspects. The research study delved into identifying the core best practices of clothing businesses, serving as a strategic guide for their advancement. Through meticulous analysis, several key findings emerged. Firstly, prioritizing product factors, such as maintaining optimal stock levels and maximizing customer satisfaction, was deemed essential for driving sales and fostering loyalty. Additionally, selecting the right store location was crucial for visibility and accessibility, directly impacting footfall and sales. Vigilance towards competitors and demographic shifts was highlighted as essential for maintaining relevance. Understanding the relationship between marketing spend and customer acquisition proved pivotal for optimizing budgets and achieving a higher ROI. Strategic analysis of profit margins across clothing items emerged as crucial for maximizing profitability and revenue. Creating a positive customer experience, investing in employee training, and implementing effective inventory management practices were also identified as critical success factors. In essence, these findings underscored the holistic approach needed for sustainable growth in the clothing business, emphasizing the importance of product management, marketing strategies, customer experience, and operational efficiency.

Advances in Detect and Avoid for Unmanned Aircraft Systems and Advanced Air Mobility

CSS chapter 1 notes

Talk covering Guardrails , Jailbreak, What is an alignment problem? RLHF, EU AI Act, Machine & Graph unlearning, Bias, Inconsistency, Probing, Interpretability, Bias

VLSI design 21ec63 MOS TRANSISTOR THEORY

CCS367-STORAGE TECHNOLOGIES QUESTION BANK

The rapid advancements in artificial intelligence and natural language processing have significantly transformed human-computer interactions. This thesis presents the design, development, and evaluation of an intelligent chatbot capable of engaging in natural and meaningful conversations with users. The chatbot leverages state-of-the-art deep learning techniques, including transformer-based architectures, to understand and generate human-like responses. Key contributions of this research include the implementation of a context- aware conversational model that can maintain coherent dialogue over extended interactions. The chatbot's performance is evaluated through both automated metrics and user studies, demonstrating its effectiveness in various applications such as customer service, mental health support, and educational assistance. Additionally, ethical considerations and potential biases in chatbot responses are examined to ensure the responsible deployment of this technology. The findings of this thesis highlight the potential of intelligent chatbots to enhance user experience and provide valuable insights for future developments in conversational AI.

Pre-trained Large Language Models (LLM) have achieved remarkable successes in several domains. However, code-oriented LLMs are often heavy in computational complexity, and quadratically with the length of the input code sequence. Toward simplifying the input program of an LLM, the state-of-the-art approach has the strategies to filter the input code tokens based on the attention scores given by the LLM. The decision to simplify the input program should not rely on the attention patterns of an LLM, as these patterns are influenced by both the model architecture and the pre-training dataset. Since the model and dataset are part of the solution domain, not the problem domain where the input program belongs, the outcome may differ when the model is trained on a different dataset. We propose SlimCode, a model-agnostic code simplification solution for LLMs that depends on the nature of input code tokens. As an empirical study on the LLMs including CodeBERT, CodeT5, and GPT-4 for two main tasks: code search and summarization. We reported that 1) the reduction ratio of code has a linear-like relation with the saving ratio on training time, 2) the impact of categorized tokens on code simplification can vary significantly, 3) the impact of categorized tokens on code simplification is task-specific but model-agnostic, and 4) the above findings hold for the paradigm–prompt engineering and interactive in-context learning and this study can save reduce the cost of invoking GPT-4 by 24%per API query. Importantly, SlimCode simplifies the input code with its greedy strategy and can obtain at most 133 times faster than the state-of-the-art technique with a significant improvement. This paper calls for a new direction on code-based, model-agnostic code simplification solutions to further empower LLMs.

Kiln

..

Very Important design

Biomass energy

Notes of Construction management and entrepreneurship

Image recognition, which comes under Artificial Intelligence (AI) is a critical aspect of computer vision, enabling computers or other computing devices to identify and categorize objects within images. Among numerous fields of life, food processing is an important area, in which image processing plays a vital role, both for producers and consumers. This study focuses on the binary classification of strawberries, where images are sorted into one of two categories. We Utilized a dataset of strawberry images for this study; we aim to determine the effectiveness of different models in identifying whether an image contains strawberries. This research has practical applications in fields such as agriculture and quality control. We compared various popular deep learning models, including MobileNetV2, Convolutional Neural Networks (CNN), and DenseNet121, for binary classification of strawberry images. The accuracy achieved by MobileNetV2 is 96.7%, CNN is 99.8%, and DenseNet121 is 93.6%. Through rigorous testing and analysis, our results demonstrate that CNN outperforms the other models in this task. In the future, the deep learning models can be evaluated on a richer and larger number of images (datasets) for better/improved results.

SCADAmetrics Instrumentation for Sensus Water Meters - Core and Main Training 2024 July 09

Introduction to information retrieval, Major challenges in IR

Cybersecurity breaches are a growing threat in today’s interconnected digital landscape, affecting individuals, businesses, and governments alike. These breaches compromise sensitive information and erode trust in online services and systems. Understanding the causes, consequences, and prevention strategies of cybersecurity breaches is crucial to protect against these pervasive risks. Cybersecurity breaches refer to unauthorized access, manipulation, or destruction of digital information or systems. They can occur through various means such as malware, phishing attacks, insider threats, and vulnerabilities in software or hardware. Once a breach happens, cybercriminals can exploit the compromised data for financial gain, espionage, or sabotage. Causes of breaches include software and hardware vulnerabilities, phishing attacks, insider threats, weak passwords, and a lack of security awareness. The consequences of cybersecurity breaches are severe. Financial loss is a significant impact, as organizations face theft of funds, legal fees, and repair costs. Breaches also damage reputations, leading to a loss of trust among customers, partners, and stakeholders. Regulatory penalties are another consequence, with hefty fines imposed for non-compliance with data protection regulations. Intellectual property theft undermines innovation and competitiveness, while disruptions of critical services like healthcare and utilities impact public safety and well-being.

Trends in CAD CAM

Syllabus