Maths.pptx
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Data analysis involves several iterative phases including data collection from various sources, data processing and cleaning to organize and correct data, and analysis of the cleaned datasets using various methods to understand characteristics. Visualization using graphs is also an important analysis tool. For example, Lawson continues to sell one of its products ranked 31st in sales because exploratory data analysis of consumer data showed that 10% of heavy users accounted for 60% of sales.
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Milestone 2 Guidance For your final project… Most importa.docx
Milestone 2 Guidance
For your final project… Most important…. KEEP IT SIMPLE
• Thank about the equations you are most comfortable with when it comes to calculating velocities and
energy transfer. Build your device around those situations.
• Start with a pencil sketch!
• Use at least three steps. Your “selected step” for analysis cannot be your first or last step because you
need to analyze the transitions before and after your selected step.
Example:
The only time the initial velocity is zero is if gravity starts the step’s motion. If there is any sort of interaction
between two objects, the initial velocity of the step is the velocity of the object AFTER that interaction. In the
above example, the red ball is at rest when the blue ball starts. The selected step, though, starts with the
velocity that the red ball has picked up from the interaction with the blue ball.
Any transition that involves contact with moving objects will have a transfer of energy. For an object that flips
a switch and releases a ball allowing gravity to act on it, there is work done to flip the switch, but no energy
transferred to the next step.
For Milestone Two, if you chose the step that you will analyze for your final project, you can use the work you
do for Milestone Two and my feedback to make it all perfect for the final submission. For Part 1 of this
milestone, you will describe the selected step.
For Part 2 of Milestone 2, we look to the transition between the previous step and the selected step:
This section has three parts. First, describe in words what is happening between the two objects at the
transition and how the energy is being transferred. This is just to set the stage, do not include equations and
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equations to match those two scenarios. The assignment also asks you to explain “the connection between
the basic physics concepts in the equations and the interaction of the object and force(s) from step to step.”
That means you need to explain what numbers you will plug into the equations, where you will find them and
what you will solve for. The final part of this section is to perform the calculations and describe how those
numbers indicate the future path of the object in the selected step.
In Part 3 of Milestone 2, we look at how the energy distribution changes throughout the course of the
selected step:
We are looking at the changes in kinetic and potential energy as the ball travels through the selected step.
The total energy r ...
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- Common data mining tasks include classification, regression, clustering, association analysis, and anomaly detection. These can be used to solve problems like product recommendations, student enrollment predictions, and fraud detection.
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implementation of each technique. Finally, the paper has presented some valuable recommendations in this field.
Milestone 2 Guidance For your final project… Most importa.docx
Milestone 2 Guidance
For your final project… Most important…. KEEP IT SIMPLE
• Thank about the equations you are most comfortable with when it comes to calculating velocities and
energy transfer. Build your device around those situations.
• Start with a pencil sketch!
• Use at least three steps. Your “selected step” for analysis cannot be your first or last step because you
need to analyze the transitions before and after your selected step.
Example:
The only time the initial velocity is zero is if gravity starts the step’s motion. If there is any sort of interaction
between two objects, the initial velocity of the step is the velocity of the object AFTER that interaction. In the
above example, the red ball is at rest when the blue ball starts. The selected step, though, starts with the
velocity that the red ball has picked up from the interaction with the blue ball.
Any transition that involves contact with moving objects will have a transfer of energy. For an object that flips
a switch and releases a ball allowing gravity to act on it, there is work done to flip the switch, but no energy
transferred to the next step.
For Milestone Two, if you chose the step that you will analyze for your final project, you can use the work you
do for Milestone Two and my feedback to make it all perfect for the final submission. For Part 1 of this
milestone, you will describe the selected step.
For Part 2 of Milestone 2, we look to the transition between the previous step and the selected step:
This section has three parts. First, describe in words what is happening between the two objects at the
transition and how the energy is being transferred. This is just to set the stage, do not include equations and
numbers in this intro section. The second part is to introduce the equations you will be using to track the
energy and momentum. Conservation of momentum tells us that the total momentum before the collision
will equal that total momentum after the collision. Conservation of energy tells us that the total kinetic
energy before the collision is equal to the total kinetic energy after the collision. The trick is to write the
equations to match those two scenarios. The assignment also asks you to explain “the connection between
the basic physics concepts in the equations and the interaction of the object and force(s) from step to step.”
That means you need to explain what numbers you will plug into the equations, where you will find them and
what you will solve for. The final part of this section is to perform the calculations and describe how those
numbers indicate the future path of the object in the selected step.
In Part 3 of Milestone 2, we look at how the energy distribution changes throughout the course of the
selected step:
We are looking at the changes in kinetic and potential energy as the ball travels through the selected step.
The total energy r ...
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The past two decades has seen a dramatic increase in the amount of information or data being stored in electronic format. This accumulation of data has taken place at an explosive rate. It has been estimated that the amount of information in the world doubles every 20 months and the size and number of databases are increasing even faster. The increase in use of electronic data gathering devices such as point-of-sale or remote sensing devices has contributed to this explosion of available data. Figure 1 from the Red Brick company illustrates the data explosion.
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Attività didattiche trasversali per i dottori di ricerca - UNIPI a.a. 2018-2019
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The projects in the list below are also designed to help you get a job! Each project was designed by a data scientist on our content team, and they’re representative examples of the real projects working data analysts and data scientists do every day. They’re designed to guide you through the process while also challenging your skills, and they’re open-ended so that you can put your own twist on each project and use it for your data science portfolio.
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There are different types of guided projects. One of them is a guided project for
There are a lot of benefits for it:
It removes the barriers between you and doing projects
Saves you much time thinking about the project and preparing the data.
It allows you to apply the theoretical knowledge without getting distracted by obstacles.
Practical tips that can save your effort and time in the future.
#datasciencefree
#rohitdubey
#teachtechtoe
#linkedin.com/in/therohitdubey
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Delve into the art of exploratory data analysis (EDA), where data visualization and statistical techniques help uncover patterns and relationships. Gain expertise in feature engineering and selection, as well as the fundamentals of machine learning, including supervised and unsupervised learning, deep learning, and reinforcement learning.
Uncover the specifics of various machine learning algorithms, from linear regression to neural networks, and understand when and how to use them. Learn how to rigorously evaluate and select models using performance metrics, cross-validation, and hyperparameter tuning while considering bias and fairness.
The book goes beyond model building by addressing the deployment of models in real-world applications, including recommendation systems, fraud detection, and natural language processing. It also explores ethical considerations related to data science.
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Stay ahead of the curve by examining the future of data science, including emerging trends like artificial intelligence, deep learning, and responsible data science practices.
"Mastering Data Science" isn't just a book; it's your key to unlocking the potential of data in the 21st century. Whether you're a novice or an experienced data scientist, this comprehensive guide equips you to navigate the ever-expanding world of data and make meaningful contributions to various domains.
Lesson 1 - Overview of Machine Learning and Data Analysis.pptx
This document provides an overview of machine learning and data analysis. It defines machine learning as a field of artificial intelligence that enables computers to learn from data without being explicitly programmed. The main types of machine learning are supervised, unsupervised, and reinforcement learning. Data analysis is the process of extracting meaningful insights from data through techniques like cleaning, exploring for patterns/trends, statistical analysis, and visualization. Machine learning automates many data analysis tasks and can be applied through techniques like classification, clustering, and regression. The relationship between machine learning and data analysis fuels discovery, with data analysis providing foundation and machine learning generating insights.
Data Science Unit1 AMET.pdf
Data science involves extracting meaningful insights from raw data through scientific methods and algorithms. It is an interdisciplinary field that focuses on analyzing large datasets using skills from computer science, mathematics, and statistics. Python is a commonly used programming language for data science due to its powerful libraries for tasks like data analysis, machine learning, and visualization. Key Python libraries include NumPy, Pandas, Matplotlib, Scikit-learn, and SciPy. The document then discusses tools, applications, and basic concepts in data science and Python.
EDA-Unit 1.pdf
This document provides an overview of exploratory data analysis (EDA). It discusses the key stages of EDA including data requirements, collection, processing, cleaning, exploration, modeling, products, and communication. The stages involve examining available data to discover patterns and relationships. EDA is the first step in data mining projects to understand data without assumptions. The document also outlines the problem definition, data preparation, analysis, and result development and representation steps of EDA. Finally, it discusses different types of data like numeric, categorical, and the importance of understanding data types for analysis.
Unit 1.pptx
This document outlines the course structure for a structured data analytics course. It is a 3 credit course that includes practice sessions, assessments like quizzes and assignments. The course covers topics like data wrangling, classification/regression algorithms, association analysis, time series, and recommender systems. It also discusses popular machine learning algorithms and reading resources. Finally, it provides an overview of the first unit which is on data analytics from a business perspective.
Data Science-1 (1).ppt
The document provides an overview of data science and what it entails. It discusses the hype around big data and data science, and how data science has evolved due to improvements in technology that allow for large-scale data processing. It defines data science as a process that involves collecting, cleaning, analyzing and extracting meaningful insights from data. Data scientists come from a variety of academic backgrounds and work in both industry and academia developing solutions to real-world problems using data-driven approaches.
Guide to data analytics
Data science has become essential for businesses to analyze large amounts of data. While machine learning seems promising, there are no shortcuts - data exploration is needed to improve model accuracy. The key steps of data exploration are identifying variables, univariate and bivariate analysis, and treating missing values and outliers to clean the data before building predictive models. Understanding the data generation process is important for leaders to evaluate the quality of analytics.
Data collection, Data Integration, Data Understanding e Data Cleaning & Prepa...
Data collection, Data Integration, Data Understanding e Data Cleaning & Prepa...Laboratorio di Cultura Digitale, labcd.humnet.unipi.it
Attività didattiche trasversali per i dottori di ricerca - UNIPI a.a. 2018-2019
Moduli offerti dal LabCDData analysis aug-11
The document discusses various methods for analyzing and interpreting data. It describes descriptive analysis which helps summarize data patterns. Statistical analysis techniques like clustering, regression, and cohorts are explained. Inferential analysis makes judgments about differences between groups. Qualitative and quantitative methods are outlined for interpreting data through coding and establishing relationships. The purpose of data analysis and interpretation is to answer research questions and determine trends to support decision making.
OpenML data@Sheffield
OpenML aims to enable collaborative machine learning by allowing researchers to explore data and algorithms together online at web scale in real time. It seeks to remove friction from the machine learning process by making it easy to contribute and reuse datasets, algorithms, and experiments. This will allow research to progress faster and at larger scale through open sharing and automated collaboration across fields and laboratories.
cap.doc
This document discusses data mining and summarizes a research article on the topic. It defines data mining as extracting useful patterns from large databases. The data mining process involves selecting, transforming, and mining data, then interpreting and validating the results. Various data mining techniques are described, including summarization, clustering, classification, regression, and detecting patterns and deviations. The goal of data mining is to better understand data and support decision making.
lec1.pdf
This summary provides an overview of the key points from the document:
1) The document is a lecture introduction on the topic of data mining given by Professor Pabitra Mitra. It discusses the motivation for data mining, the knowledge discovery process, and types of patterns that can be discovered.
2) Large volumes of data are being collected from various sources like e-commerce, social media, and scientific experiments, but this data is not providing enough useful knowledge. Data mining aims to automatically analyze massive data to extract meaningful knowledge.
3) The knowledge discovery process involves selecting relevant data from databases, preprocessing and integrating it into a data warehouse, then applying data mining techniques to discover patterns and knowledge.
Data Science Demystified_ Journeying Through Insights and Innovations
In the digital age, data has emerged as one of the most valuable resources, driving decision-making processes across industries. Data science, the interdisciplinary field that extracts insights and knowledge from structured and unstructured data, plays a pivotal role in leveraging this resource. This section provides an overview of data science, its importance, and its applications in various domains.
Motivations of Early Adopters of Technology - The case of Social Networks
This document provides a literature review on the motivations of early adopters of technology, specifically social networks. It discusses four key areas of literature:
1) Diffusion of innovations theory which examines how new ideas and technologies are adopted over time. Important factors that influence adoption are relative advantage, compatibility, complexity, trialability, observability.
2) Literature on early adopters which finds they are often less technically savvy than assumed and more interested in exploring new solutions for their needs.
3) Consumer motivation literature which looks at psychological drivers for consumption.
4) Networks literature with a focus on social networks, which are the subject of the study.
The literature review aims to understand the motiv
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Social media has changed communication but also raises ethical issues around privacy, misinformation, and mental health. Key debates focus on balancing free speech against harmful content. Filter bubbles and confirmation bias can limit exposure to diverse views through algorithms that amplify aligned content. Disinformation spreads for political and financial gain using bots, fake accounts and doctored content, undermining trust and democracy. Hate speech and cyberbullying on platforms can seriously impact mental health.
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The document discusses several issues relating to the future of information ethics, including privacy, surveillance, algorithmic bias, intellectual property, globalization, and augmentation. Specifically, it addresses the need for individuals to have control over their personal data and consent to its use (privacy), the debate around balancing privacy rights with security interests behind surveillance practices, how algorithms can perpetuate inequality if not designed carefully, balancing copyright with open-source licensing, challenges of cross-border data flows, and both current and future ethical challenges relating to new technologies.
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There are many sorting algorithms that can sort a list of numbers in ascending or descending order. Some common sorting algorithms include bubble sort, merge sort, and quicksort. Bubble sort has a computational complexity of O(n2) while merge sort and quicksort have better complexities of O(n log n). Stack and queue are abstract data types - stack follows LIFO (Last In First Out) while queue follows FIFO (First In First Out). Stack adds and removes elements from the top of the data structure, while queue adds to the tail and removes from the head.
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Computer graphics refers to images and figures created using a computer. There are two main types: 2D computer graphics, which are two-dimensional images, and 3D computer graphics, which create three-dimensional virtual spaces and objects. Creating 3D computer graphics involves several steps: modeling virtual objects, adding textures, rigging for movement, lighting, animation, and rendering to convert the 3D scene to a 2D image. A challenge with creating realistic human figures with 3D computer graphics is the "uncanny valley" effect, where figures that are almost but not perfectly realistic can appear creepy. Advances in technology now allow for highly realistic 3D computer graphics that avoid this effect.
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- 1. Data analysis is a process consisting of several phases and aimed at finding useful information. The following is a series of steps, and each step is iterative. Data collection is a phase in which necessary data is obtained from various sources. The collected data is called raw data, but it cannot be used as is. Data processing and cleaning organize and correct data for analysis, for example arranging data in tabular form or removing outliers. Once the datasets are cleaned, we can move on to the analysis. Analysists use a variety of methods to understand data characteristics. At this point, graphs are often used to visualize and help us better understand the data. Scientific computing: Data analysis Why Lawson continues to sell the 31st largest product in sales. Although one of Lawson’s product, “Horoniga Chocolate Blanc”, ranks 31st in the sweet bread sales ranking, analysis of consumer data shows that 10% of heavy users account for 60% of sales. Based on results of analysis, Lawson continues to sell this product. Exploratory data analysis (EDA) EDA is a method used to actually analyze processed data. There are different ways to understand the characteristics of the data. One example of this is taking a statistical value such as mean, median, mode, variance and correlation coefficient. By taking these values, we can understand broad trends in the overall data. In Lawson’s example, if they had only looked at the sales rankings, they would have ended sales of that product. However, Lawson’s marketing department placed the highest priority on repeat rate, not simple rankings. The results of analysis based on this showed that some woman frequently repeat, and they continued to sell in order not to miss out on important customers. As this example, it is important in analysis to look at data from different perspective, not just one. In addition, visualization of data using graphs is useful tool. Commonly used graphs include the following. ・Box plot ・Histogram ・Scatter plot ・Run chart ・Heat map ・Bar chart
- 2. Mathematical Foundation -Quantum Mechanics- Hook Everything around us is made of atoms. Atoms consist of elementary particles. Key details 1.Elementary particles have mysterious property. 2. Application of Quantum Mechanics More details 1.Quantum Mechanics is the study of observing the physical phenomena of its elementary particles. At first, we thought light was a wave, but when scientist shone light on metal, electrons popped out. Scientist think that this phenomena is light is wave and particle. This was the birth of the quantum mechanics. In short, elementary particles have two properties. It is very mysterious. 2.In fact, Quantum Mechanic of thinking is used in modern society. Using computer system from potential of elementary particles. So called “quantum computer”. Other ways, Used in laser technology applications.
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