Data wrangling is the process of removing errors and combining complex data sets to make them more accessible and easier to analyze. Due to the rapid expansion of the amount of data and data sources available today, storing and organizing large quantities of data for analysis is becoming increasingly necessary.Data wrangling is the process of removing errors and combining complex data sets to make them more accessible and easier to analyze. Due to the rapid expansion of the amount of data and data sources available today, storing and organizing large quantities of data for analysis is becoming increasingly necessary.Data wrangling is the process of removing errors and combining complex data sets to make them more accessible and easier to analyze. Due to the rapid expansion of the amount of data and data sources available today, storing and organizing large quantities of data for analysis is becoming increasingly necessary.
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.
Data warehouse implementation design for a Retail businessArsalan Qadri
The document contains an end to end data warehouse design - from SKU procurement to SKU Sale. Additionally, a BI dashboard has been created in Tableau, to mine the warehouse, with SKU as the grain. The data can be aggregated at levels of Supplier/Store/Location/Inventory/Sale Date/Time in Warehouse etc.
This document discusses objectives and techniques for data exploration, including understanding data, preparation for data mining, and interpreting results. It outlines univariate and multivariate descriptive statistics, various data visualization techniques like histograms and scatter plots, and provides a roadmap for exploring a data set through organizing, finding central points, understanding attribute spreads, visualizing distributions, pivoting data, identifying outliers, understanding relationships between attributes, visualizing those relationships, and visualizing high-dimensional data sets.
Query processing and Query OptimizationNiraj Gandha
This presentation on query processing and query optimization is made with many efforts. According to me, I have used the most basic/ fundamental examples and topics for the explanation.
Methods for Sentiment Analysis: A Literature Studyvivatechijri
Sentiment analysis is a trending topic, as everyone has an opinion on everything. The systematic
study of these opinions can lead to information which can prove to be valuable for many companies and
industries in future. A huge number of users are online, and they share their opinions and comments regularly,
this information can be mined and used efficiently. Various companies can review their own product using
sentiment analysis and make the necessary changes in future. The data is huge and thus it requires efficient
processing to collect this data and analyze it to produce required result.
In this paper, we will discuss the various methods used for sentiment analysis. It also covers various techniques
used for sentiment analysis such as lexicon based approach, SVM [10], Convolution neural network,
morphological sentence pattern model [1] and IML algorithm. This paper shows studies on various data sets
such as Twitter API, Weibo, movie review, IMDb, Chinese micro-blog database [9] and more. The paper shows
various accuracy results obtained by all the systems.
This document discusses data warehousing and online analytical processing (OLAP). It defines a data warehouse as a subject-oriented, integrated, time-variant, and nonvolatile collection of data used for analysis and decision making. The key aspects of a data warehouse covered are its multidimensional data model using cubes and dimensions, extraction of data from multiple sources, and usage for querying, reporting, analytical processing, and data mining. Common data warehouse architectures and operations like star schemas, snowflake schemas, and OLAP functions such as roll-up and drill-down are also summarized.
This document discusses different types of data models, including hierarchical, network, relational, and object-oriented models. It focuses on explaining the relational model. The relational model organizes data into tables with rows and columns and handles relationships using keys. It allows for simple and symmetric data retrieval and integrity through mechanisms like normalization. The relational model is well-suited for the database assignment scenario because it supports linking data across multiple tables using primary and foreign keys, and provides query capabilities through SQL.
Data preprocessing techniques are applied before mining. These can improve the overall quality of the patterns mined and the time required for the actual mining.
Some important data preprocessing that must be needed before applying the data mining algorithm to any data sets are completely described in these slides.
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.
Data warehouse implementation design for a Retail businessArsalan Qadri
The document contains an end to end data warehouse design - from SKU procurement to SKU Sale. Additionally, a BI dashboard has been created in Tableau, to mine the warehouse, with SKU as the grain. The data can be aggregated at levels of Supplier/Store/Location/Inventory/Sale Date/Time in Warehouse etc.
This document discusses objectives and techniques for data exploration, including understanding data, preparation for data mining, and interpreting results. It outlines univariate and multivariate descriptive statistics, various data visualization techniques like histograms and scatter plots, and provides a roadmap for exploring a data set through organizing, finding central points, understanding attribute spreads, visualizing distributions, pivoting data, identifying outliers, understanding relationships between attributes, visualizing those relationships, and visualizing high-dimensional data sets.
Query processing and Query OptimizationNiraj Gandha
This presentation on query processing and query optimization is made with many efforts. According to me, I have used the most basic/ fundamental examples and topics for the explanation.
Methods for Sentiment Analysis: A Literature Studyvivatechijri
Sentiment analysis is a trending topic, as everyone has an opinion on everything. The systematic
study of these opinions can lead to information which can prove to be valuable for many companies and
industries in future. A huge number of users are online, and they share their opinions and comments regularly,
this information can be mined and used efficiently. Various companies can review their own product using
sentiment analysis and make the necessary changes in future. The data is huge and thus it requires efficient
processing to collect this data and analyze it to produce required result.
In this paper, we will discuss the various methods used for sentiment analysis. It also covers various techniques
used for sentiment analysis such as lexicon based approach, SVM [10], Convolution neural network,
morphological sentence pattern model [1] and IML algorithm. This paper shows studies on various data sets
such as Twitter API, Weibo, movie review, IMDb, Chinese micro-blog database [9] and more. The paper shows
various accuracy results obtained by all the systems.
This document discusses data warehousing and online analytical processing (OLAP). It defines a data warehouse as a subject-oriented, integrated, time-variant, and nonvolatile collection of data used for analysis and decision making. The key aspects of a data warehouse covered are its multidimensional data model using cubes and dimensions, extraction of data from multiple sources, and usage for querying, reporting, analytical processing, and data mining. Common data warehouse architectures and operations like star schemas, snowflake schemas, and OLAP functions such as roll-up and drill-down are also summarized.
This document discusses different types of data models, including hierarchical, network, relational, and object-oriented models. It focuses on explaining the relational model. The relational model organizes data into tables with rows and columns and handles relationships using keys. It allows for simple and symmetric data retrieval and integrity through mechanisms like normalization. The relational model is well-suited for the database assignment scenario because it supports linking data across multiple tables using primary and foreign keys, and provides query capabilities through SQL.
Data preprocessing techniques are applied before mining. These can improve the overall quality of the patterns mined and the time required for the actual mining.
Some important data preprocessing that must be needed before applying the data mining algorithm to any data sets are completely described in these slides.
An overview of data warehousing and OLAP technology Nikhatfatima16
This document provides an overview of data warehousing and OLAP (online analytical processing) technology. It defines data warehousing as integrating data from multiple sources to support analysis and decision making. OLAP allows insights through fast, consistent access to multidimensional data models. It describes the three tiers of a data warehouse architecture including front-end tools, a middle OLAP server tier using ROLAP or MOLAP, and a bottom data warehouse database tier. Multidimensional databases are optimized for data warehouses and OLAP, representing data through cubes, stars, and snowflakes.
Data preprocessing involves transforming raw data into an understandable and consistent format. It includes data cleaning, integration, transformation, and reduction. Data cleaning aims to fill missing values, smooth noise, and resolve inconsistencies. Data integration combines data from multiple sources. Data transformation handles tasks like normalization and aggregation to prepare the data for mining. Data reduction techniques obtain a reduced representation of data that maintains analytical results but reduces volume, such as through aggregation, dimensionality reduction, discretization, and sampling.
This presentation briefly discusses the following topics:
Classification of Data
What is Structured Data?
What is Unstructured Data?
What is Semistructured Data?
Structured vs Unstructured Data: 5 Key Differences
Data warehousing combines data from multiple sources into a single database to provide businesses with analytics results from data mining, OLAP, scorecarding and reporting. It extracts, transforms and loads data from operational data stores and data marts into a data warehouse and staging area to integrate and store large amounts of corporate data. Data mining analyzes large databases to extract previously unknown and potentially useful patterns and relationships to improve business processes.
This document provides an overview of data warehousing and related concepts. It defines a data warehouse as a centralized database for analysis and reporting that stores current and historical data from multiple sources. The document describes key elements of data warehousing including Extract-Transform-Load (ETL) processes, multidimensional data models, online analytical processing (OLAP), and data marts. It also outlines advantages such as enhanced access and consistency, and disadvantages like time required for data extraction and loading.
Description of four techniques for Data Cleaning:
1.DWCLEANER Framework
2.Data Mining Techniques include Association Rule and Functional Dependecies
,...
Normalization is a process used to organize data in a database. It involves breaking tables into smaller, more manageable pieces to reduce data redundancy and improve data integrity. There are several normal forms including 1NF, 2NF, 3NF, BCNF, 4NF and 5NF. The document provides examples of tables and how they can be decomposed into different normal forms to eliminate anomalies and redundancy through the creation of additional tables and establishing primary keys.
The document compares NoSQL and SQL databases. It notes that NoSQL databases are non-relational and have dynamic schemas that can accommodate unstructured data, while SQL databases are relational and have strict, predefined schemas. NoSQL databases offer more flexibility in data structure, but SQL databases provide better support for transactions and data integrity. The document also discusses differences in queries, scaling, and consistency between the two database types.
This document introduces databases and database management systems. It discusses the disadvantages of file-based systems, including data duplication, incompatible formats, and fixed queries. A database was created to address these issues by centralizing data storage and control. A database management system provides tools to define, create, maintain and control access to a database. Common examples of databases include those for supermarkets, credit cards, travel agencies, libraries, insurance, and universities.
OLAP provides multidimensional analysis of large datasets to help solve business problems. It uses a multidimensional data model to allow for drilling down and across different dimensions like students, exams, departments, and colleges. OLAP tools are classified as MOLAP, ROLAP, or HOLAP based on how they store and access multidimensional data. MOLAP uses a multidimensional database for fast performance while ROLAP accesses relational databases through metadata. HOLAP provides some analysis directly on relational data or through intermediate MOLAP storage. Web-enabled OLAP allows interactive querying over the internet.
My presentation at The Richmond Data Science Community (Jan 2018). The slides are slightly different than what I had presented last year at The Data Intelligence Conference.
This document discusses various techniques for data preprocessing including data cleaning, integration, transformation, reduction, and discretization.
Data cleaning involves filling in missing values, smoothing noisy data, identifying outliers, and resolving inconsistencies. Data integration combines data from multiple sources by integrating schemas and resolving value conflicts. Data transformation techniques include normalization, aggregation, generalization, and smoothing.
Data reduction aims to reduce the volume of data while maintaining similar analytical results. This includes data cube aggregation, dimensionality reduction by removing unimportant attributes, data compression, and discretization which converts continuous attributes to categorical bins.
Machine Learning - Accuracy and Confusion MatrixAndrew Ferlitsch
Abstract: This PDSG workshop introduces basic concepts on measuring accuracy of your trained model. Concepts covered are loss functions and confusion matrices.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.
There are three common data warehouse architectures: basic, with a staging area, and with a staging area and data marts. The basic architecture extracts data directly from source systems into the data warehouse for users. The staging area architecture uses a staging area to clean and process data before loading it into the warehouse. The third architecture adds data marts, which are subsets of the warehouse organized for specific business units like sales or purchasing.
The document discusses data warehousing, including its history, types, security, applications, components, architecture, benefits and problems. A data warehouse is defined as a subject-oriented, integrated, time-variant collection of data to support management decision making. In the 1990s, organizations needed timely data but traditional systems were too slow. Data warehouses now provide competitive advantages through improved decision making and productivity. They integrate data from multiple sources to support applications like customer analysis, stock control and fraud detection.
The document discusses the disjoint set abstract data type (ADT). It can be used to represent equivalence relations and solve the dynamic equivalence problem. There are three main representations - array, linked list, and tree. The tree representation can be improved using two heuristics: smart union algorithm (e.g. union-by-rank) and path compression. Together these optimizations allow the disjoint set operations to run in near-linear time with respect to the total number of operations.
The document acknowledges and thanks several people who helped with the completion of a seminar report. It expresses gratitude to the seminar guide for being supportive and compassionate during the preparation of the report. It also thanks friends who contributed to the preparation and refinement of the seminar. Finally, it acknowledges profound gratitude to the Almighty for making the completion of the report possible with their blessings.
Companies are finding that data can be a powerful differentiator and are investing heavily in infrastructure, tools and personnel to ingest and curate raw data to be "analyzable". This process of data curation is called "Data Wrangling"
This task can be very cumbersome and requires trained personnel. However with the advances in open source and commercial tooling, this process has gotten a lot easier and the technical expertise required to do this effectively has dropped several notches.
In this tutorial, we will get a feel for what data wranglers do and use R, RStudio, Trifacta Wrangler, Open Refine tools with some hands-on exercises available at http://akuntamukkala.blogspot.com/2016/05/data-wrangling-examples.html
This document discusses different sampling techniques that can be used to analyze large datasets. It defines key sampling concepts like the target population, sampling frame, and sampling unit. Probability sampling techniques described include simple random sampling, systematic sampling, stratified sampling, cluster sampling, and probability proportional to size sampling. Non-probability sampling techniques include convenience sampling and purposive sampling. The document also covers how to calculate sample sizes needed to estimate proportions and means within a desired level of accuracy. Stratified sampling can help reduce variability and improve efficiency by dividing the population into relevant subgroups.
This document provides an overview of key concepts in sampling and descriptive statistics. It defines populations, samples, parameters, and statistics. It explains why samples are used instead of whole populations for research. Common sampling methods like simple random and systematic sampling are also described. The document then covers descriptive statistics, including frequency distributions, measures of central tendency, and measures of dispersion. It discusses the normal distribution and how the central limit theorem applies. Key terms are defined, such as standard deviation, variance, and standardized scores.
An overview of data warehousing and OLAP technology Nikhatfatima16
This document provides an overview of data warehousing and OLAP (online analytical processing) technology. It defines data warehousing as integrating data from multiple sources to support analysis and decision making. OLAP allows insights through fast, consistent access to multidimensional data models. It describes the three tiers of a data warehouse architecture including front-end tools, a middle OLAP server tier using ROLAP or MOLAP, and a bottom data warehouse database tier. Multidimensional databases are optimized for data warehouses and OLAP, representing data through cubes, stars, and snowflakes.
Data preprocessing involves transforming raw data into an understandable and consistent format. It includes data cleaning, integration, transformation, and reduction. Data cleaning aims to fill missing values, smooth noise, and resolve inconsistencies. Data integration combines data from multiple sources. Data transformation handles tasks like normalization and aggregation to prepare the data for mining. Data reduction techniques obtain a reduced representation of data that maintains analytical results but reduces volume, such as through aggregation, dimensionality reduction, discretization, and sampling.
This presentation briefly discusses the following topics:
Classification of Data
What is Structured Data?
What is Unstructured Data?
What is Semistructured Data?
Structured vs Unstructured Data: 5 Key Differences
Data warehousing combines data from multiple sources into a single database to provide businesses with analytics results from data mining, OLAP, scorecarding and reporting. It extracts, transforms and loads data from operational data stores and data marts into a data warehouse and staging area to integrate and store large amounts of corporate data. Data mining analyzes large databases to extract previously unknown and potentially useful patterns and relationships to improve business processes.
This document provides an overview of data warehousing and related concepts. It defines a data warehouse as a centralized database for analysis and reporting that stores current and historical data from multiple sources. The document describes key elements of data warehousing including Extract-Transform-Load (ETL) processes, multidimensional data models, online analytical processing (OLAP), and data marts. It also outlines advantages such as enhanced access and consistency, and disadvantages like time required for data extraction and loading.
Description of four techniques for Data Cleaning:
1.DWCLEANER Framework
2.Data Mining Techniques include Association Rule and Functional Dependecies
,...
Normalization is a process used to organize data in a database. It involves breaking tables into smaller, more manageable pieces to reduce data redundancy and improve data integrity. There are several normal forms including 1NF, 2NF, 3NF, BCNF, 4NF and 5NF. The document provides examples of tables and how they can be decomposed into different normal forms to eliminate anomalies and redundancy through the creation of additional tables and establishing primary keys.
The document compares NoSQL and SQL databases. It notes that NoSQL databases are non-relational and have dynamic schemas that can accommodate unstructured data, while SQL databases are relational and have strict, predefined schemas. NoSQL databases offer more flexibility in data structure, but SQL databases provide better support for transactions and data integrity. The document also discusses differences in queries, scaling, and consistency between the two database types.
This document introduces databases and database management systems. It discusses the disadvantages of file-based systems, including data duplication, incompatible formats, and fixed queries. A database was created to address these issues by centralizing data storage and control. A database management system provides tools to define, create, maintain and control access to a database. Common examples of databases include those for supermarkets, credit cards, travel agencies, libraries, insurance, and universities.
OLAP provides multidimensional analysis of large datasets to help solve business problems. It uses a multidimensional data model to allow for drilling down and across different dimensions like students, exams, departments, and colleges. OLAP tools are classified as MOLAP, ROLAP, or HOLAP based on how they store and access multidimensional data. MOLAP uses a multidimensional database for fast performance while ROLAP accesses relational databases through metadata. HOLAP provides some analysis directly on relational data or through intermediate MOLAP storage. Web-enabled OLAP allows interactive querying over the internet.
My presentation at The Richmond Data Science Community (Jan 2018). The slides are slightly different than what I had presented last year at The Data Intelligence Conference.
This document discusses various techniques for data preprocessing including data cleaning, integration, transformation, reduction, and discretization.
Data cleaning involves filling in missing values, smoothing noisy data, identifying outliers, and resolving inconsistencies. Data integration combines data from multiple sources by integrating schemas and resolving value conflicts. Data transformation techniques include normalization, aggregation, generalization, and smoothing.
Data reduction aims to reduce the volume of data while maintaining similar analytical results. This includes data cube aggregation, dimensionality reduction by removing unimportant attributes, data compression, and discretization which converts continuous attributes to categorical bins.
Machine Learning - Accuracy and Confusion MatrixAndrew Ferlitsch
Abstract: This PDSG workshop introduces basic concepts on measuring accuracy of your trained model. Concepts covered are loss functions and confusion matrices.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.
There are three common data warehouse architectures: basic, with a staging area, and with a staging area and data marts. The basic architecture extracts data directly from source systems into the data warehouse for users. The staging area architecture uses a staging area to clean and process data before loading it into the warehouse. The third architecture adds data marts, which are subsets of the warehouse organized for specific business units like sales or purchasing.
The document discusses data warehousing, including its history, types, security, applications, components, architecture, benefits and problems. A data warehouse is defined as a subject-oriented, integrated, time-variant collection of data to support management decision making. In the 1990s, organizations needed timely data but traditional systems were too slow. Data warehouses now provide competitive advantages through improved decision making and productivity. They integrate data from multiple sources to support applications like customer analysis, stock control and fraud detection.
The document discusses the disjoint set abstract data type (ADT). It can be used to represent equivalence relations and solve the dynamic equivalence problem. There are three main representations - array, linked list, and tree. The tree representation can be improved using two heuristics: smart union algorithm (e.g. union-by-rank) and path compression. Together these optimizations allow the disjoint set operations to run in near-linear time with respect to the total number of operations.
The document acknowledges and thanks several people who helped with the completion of a seminar report. It expresses gratitude to the seminar guide for being supportive and compassionate during the preparation of the report. It also thanks friends who contributed to the preparation and refinement of the seminar. Finally, it acknowledges profound gratitude to the Almighty for making the completion of the report possible with their blessings.
Companies are finding that data can be a powerful differentiator and are investing heavily in infrastructure, tools and personnel to ingest and curate raw data to be "analyzable". This process of data curation is called "Data Wrangling"
This task can be very cumbersome and requires trained personnel. However with the advances in open source and commercial tooling, this process has gotten a lot easier and the technical expertise required to do this effectively has dropped several notches.
In this tutorial, we will get a feel for what data wranglers do and use R, RStudio, Trifacta Wrangler, Open Refine tools with some hands-on exercises available at http://akuntamukkala.blogspot.com/2016/05/data-wrangling-examples.html
This document discusses different sampling techniques that can be used to analyze large datasets. It defines key sampling concepts like the target population, sampling frame, and sampling unit. Probability sampling techniques described include simple random sampling, systematic sampling, stratified sampling, cluster sampling, and probability proportional to size sampling. Non-probability sampling techniques include convenience sampling and purposive sampling. The document also covers how to calculate sample sizes needed to estimate proportions and means within a desired level of accuracy. Stratified sampling can help reduce variability and improve efficiency by dividing the population into relevant subgroups.
This document provides an overview of key concepts in sampling and descriptive statistics. It defines populations, samples, parameters, and statistics. It explains why samples are used instead of whole populations for research. Common sampling methods like simple random and systematic sampling are also described. The document then covers descriptive statistics, including frequency distributions, measures of central tendency, and measures of dispersion. It discusses the normal distribution and how the central limit theorem applies. Key terms are defined, such as standard deviation, variance, and standardized scores.
Lect 3 background mathematics for Data Mininghktripathy
The document discusses various statistical measures used to describe data, including measures of central tendency and dispersion.
It introduces the mean, median, and mode as common measures of central tendency. The mean is the average value, the median is the middle value, and the mode is the most frequent value. It also discusses weighted means.
It then discusses various measures of data dispersion, including range, variance, standard deviation, quartiles, and interquartile range. The standard deviation specifically measures how far data values typically are from the mean and is important for describing the width of a distribution.
The document provides an overview of different sampling techniques, including:
- Probability sampling techniques like simple random sampling, stratified random sampling, systematic sampling, and probability proportional to size sampling.
- Non-probability sampling techniques like judgmental sampling, convenience sampling, and quota sampling.
- It defines key sampling concepts like population, sample, sampling frame, and explains the stages of sampling such as defining the population, selecting a sampling frame, sample selection, data collection, and inference.
- For each sampling technique, it provides examples, methodology, merits and demerits to help understand how to apply them for research sampling.
The document provides an overview of different sampling techniques, including:
- Probability sampling techniques like simple random sampling, stratified random sampling, systematic sampling, and probability proportional to size sampling.
- Non-probability sampling techniques like judgmental sampling, convenience sampling, and quota sampling.
It defines key sampling concepts like population, sample, sampling frame, and explains the stages of sampling such as defining the population, selecting a sampling frame, choosing the sample, and making inferences. For each technique, it provides examples, merits, and demerits. The document is a comprehensive reference on sampling definitions, processes, and different methodologies.
The document discusses basic statistical descriptions of data including measures of central tendency (mean, median, mode), dispersion (range, variance, standard deviation), and position (quartiles, percentiles). It explains how to calculate and interpret these measures. It also covers estimating these values from grouped frequency data and identifying outliers. The key goals are to better understand relationships within a data set and analyze data at multiple levels of precision.
This document discusses key components and concepts of research methods. It covers:
1) Main components of research methods including study design, population, sampling, variables, data collection and analysis.
2) Probability and non-probability sampling techniques such as simple random sampling, stratified sampling, and cluster sampling.
3) Key terms related to sampling such as target population, study population, sampling unit, and sampling frame.
This document discusses various sampling techniques used in research. It defines key terms like population, sample, census, and sampling frame. It describes probability sampling methods like simple random sampling, systematic sampling, stratified random sampling, cluster sampling, and multi-stage sampling. It also discusses non-probability sampling techniques like judgmental, quota, snowball, and convenience sampling. The document emphasizes the importance of selecting the most appropriate sampling technique based on the research question and having a representative sample.
This document provides an overview of fundamental statistical concepts. It defines statistics as a science used to obtain information from data to facilitate decision making. It discusses different types of variables and scales of measurement used in statistics. It also describes key concepts such as population and sample, different sampling methods, descriptive statistics including measures of central tendency, and how statistical inference is used to generalize from samples to populations. Examples are provided to illustrate concepts throughout.
This document provides an introduction to biostatistics and descriptive statistics concepts. It defines key terms like data, variables, populations, samples, and measurement scales. It also discusses measures of central tendency like mean, median and mode. Measures of dispersion such as range, variance, standard deviation and coefficient of variation are introduced. Finally, the document discusses frequency distributions, histograms, percentiles, quartiles, and box plots as ways to summarize and visualize data distributions. Examples are provided throughout to illustrate statistical concepts.
This document provides an introduction to statistics for data science. It discusses why statistics are important for processing and analyzing data to find meaningful trends and insights. Descriptive statistics are used to summarize data through measures like mean, median, and mode for central tendency, and range, variance, and standard deviation for variability. Inferential statistics make inferences about populations based on samples through hypothesis testing and other techniques like t-tests and regression. The document outlines the basic terminology, types, and steps of statistical analysis for data science.
This document discusses sampling techniques and concepts in statistics. It begins by outlining learning objectives related to sampling, errors, and statistical analysis. It then discusses reasons for sampling such as saving money and time compared to a census. The document contrasts random and non-random sampling methods. It provides examples of random sampling techniques including simple random sampling, systematic random sampling, stratified random sampling, and cluster sampling. It also discusses non-random convenience sampling and sources of non-sampling errors. Finally, it introduces the concepts of sampling distributions and the central limit theorem, and provides examples of using normal approximations.
The process of obtaining information from a subset (sample) of
a larger group (population)
The results for the sample are then used to make estimates of
the larger group
Faster and cheaper than asking the entire population
This document discusses key concepts in descriptive statistics including:
- Measures of central tendency like mean, median, and mode.
- Measures of variability such as range, interquartile range, variance, and standard deviation.
- Frequency distributions, percentages, and probability distributions.
- Population and sample distributions as well as the sampling distribution of the mean and the central limit theorem.
This document discusses developing a sample plan, which involves six steps: 1) defining the relevant population, 2) obtaining a population list, 3) designing the sample method and size, 4) drawing the sample, 5) assessing the sample, and 6) resampling if necessary. It also covers basic sampling concepts and different probability and non-probability sampling methods.
This document provides an overview of how to use SPSS to conduct basic statistical analysis and present results. It outlines expectations for the workshop, including learning how to prepare an SPSS file, display and summarize data, and create graphical presentations. The document then covers key SPSS concepts like variables, data types, and examples. It also demonstrates how to perform descriptive statistics, frequency tables, crosstabs, measures of central tendency and dispersion. Finally, it discusses different methods of graphical presentation in SPSS like bar charts, histograms, box plots and more.
The document discusses various techniques for data reduction including data sampling, data cleaning, data transformation, and data segmentation to break down large datasets into more manageable groups that provide better insight, as well as hierarchical and k-means clustering methods for grouping similar objects into clusters to analyze relationships in the data.
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
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Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
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2. Exploring Your Data
• Working with data is both an art and a science. We’ve mostly
been talking about the science part, getting your feet wet with
Python tools for Data Science. Lets look at some of the art
now.
• After you’ve identified the questions you’re trying to answer
and have gotten your hands on some data, you might be
tempted to dive in and immediately start building models and
getting answers. But you should resist this urge. Your first
step should be to explore your data.
4. Data Wrangling
• The process of transforming “raw” data into data that can be
analyzed to generate valid actionable insights
• Data Wrangling : aka
• Data preprocessing
• Data preparation
• Data Cleansing
• Data Scrubbing
• Data Munging
• Data Transformation
• Data Fold, Spindle, Mutilate……
5. Data Wrangling Steps
• Iterative process of
• Obtain
• Understand
• Explore
• Transform
• Augment
• Visualize
8. Exploring Your Data
• The simplest case is when you have a one-dimensional data
set, which is just a collection of numbers. For example,
• daily average number of minutes each user spends on your site,
• the number of times each of a collection of data science tutorial videos was
watched,
• the number of pages of each of the data science books in your data science
library.
• An obvious first step is to compute a few summary statistics.
• You’d like to know how many data points you have, the smallest, the largest,
the mean, and the standard deviation.
• But even these don’t necessarily give you a great understanding.
9. Summary statistics of a single data set
• Information (numbers) that give a quick and simple
description of the data
• Maximum value
• Minimum value
• Range (dispersion): max – min
• Mean
• Median
• Mode
• Quantile
• Standard deviation
• Etc.
0 quartile = 0 quantile = 0 percentile
1 quartile = 0.25 quantile = 25 percentile
2 quartile = .5 quantile = 50 percentile (median)
3 quartile = .75 quantile = 75 percentile
4 quartile = 1 quantile = 100 percentile
10. Mean vs average vs median vs mode
• (Arithmetic) Mean: the “average” value of the data
• Average: can be ambiguous
• The average household income in this community is $60,000
• The average (mean) income for households in this community is $60,000
• The income for an average household in this community is $60,000
• What if most households are earning below $30,000 but one household is
earning $1M
• Median: the “middlest” value, or mean of the two middle values
• Can be obtained by sorting the data first
• Does not depend on all values in the data.
• More robust to outliers
• Mode: the most-common value in the data
def mean(a): return sum(a) / float(len(a))
def mean(a): return reduce(lambda x, y: x+y, a) / float(len(a))
Quantile: a generalization of
median.
E.g. 75 percentile is the value
which 75% of values are less
than or equal to
11. Variance and standard deviation
• Describes the spread of the data from the mean
• Is the mean squared of the deviation
• Standard deviation (square root of the variance):
• Easier to understand than variance
• Has the same unit as the measurement
• Say the data measures height of people in inch, the unit of is also inch. The
unit for 2 is square inch …
12. CDC BRFSS Dataset
• The Behavioral Risk Factor Surveillance System (BRFSS) is
the nation's premier system of health-related telephone
surveys that collect state data about U.S. residents regarding
their health-related risk behaviors, chronic health conditions,
and use of preventive services.
• https://www.cs.odu.edu/~sampath/courses/f19/cs620/files/data/brfss.csv
13. Activity
• Download the brfss.csv file and load it to your python module.
• https://www.cs.odu.edu/~sampath/courses/f19/cs620/files/data/brfss.csv
• Display the content and observe the data
• Create a function cleanBRFSSFrame() to clean the dataset
• Drop the sex from the dataframe
• Drop the rows of NaN values (every single NaN)
• Use describe() method to display the count, mean, std, min, and
quantile data for column weight2.
• Find the median (median()) and mode (mode()) of the age
14. Population vs sample
Sampling is a process used in statistical analysis in which a
predetermined number of observations are taken from a larger
population
15. Population vs sample
• Population: all members of a group in a study
• The average height of men
• The average height of living male ≥ 18yr in USA between 2001 and 2010
• The average height of all male students ≥ 18yr registered in Fall’17
• Sample: a subset of the members in the population
• Most studies choose to sample the population due to cost/time or other factors
• Each sample is only one of many possible subsets of the population
• May or may not be representative of the whole population
• Sample size and sampling procedure is important
df = pd.read_csv('brfss.csv')
print(df.sample(100)) # random sample of 100 values
16. Why do we sample?
• Enables research/ surveys to be done more quickly/ timely
• Less expensive and often more accurate than large CENSUS (
survey of the entire population)
• Given limited research budgets and large population sizes, there
is no alternative to sampling.
• Sampling also allows for minimal damage or lost
• Sample data can also be used to validate census data
• A survey of the entire universe (gives real estimate not sample estimate)
17. Simple Random Sampling
• In Simple Random Sampling, each element of the larger
population is assigned a unique ID number, and a table of
random numbers or a lottery technique is used to select elements,
one at a time, until the desired sample size is reached.
• Simple random sampling is usually reserved for use with
relatively small populations with an easy-to-use sampling frame
( very tedious when drawing large samples).
• Bias is avoided because the person drawing the sample does not
manipulate the lottery or random number table to select certain
individuals.
18. Random Selection
• Selects at random
• With replacement
• From any array
• A specified number of times
np.random.choice
np.random.choice(some_array, sample size)
Example:
import numpy as np
d = np.arange(6) + 1
s = np.random.choice(d, 1000)
print(s)
19. Systematic Sampling
• Systematic sampling is a type of probability sampling method in which
sample members from a larger population are selected according to a random
starting point and a fixed periodic interval.
• In this approach, the estimated number of elements in the larger population is
divided by the desired sample size to yield a SAMPLNG INTERVAL. The
sample is then drawn by listing the population in an arbitrary order and
selecting every nth case, starting with a randomly selected.
• This is less time consuming and easier to implement.
• Systematic sampling is useful when the units in your sampling frame are not
numbered or when the sampling frame consists of very long list.
20. • Populations often consist of strata or groups that are different from each other
and that consist of very different sizes.
• Stratified Sampling ensures that all relevant strata of the population are
represented in the sample.
• Stratification treats each stratum as a separate population- arranging the
sampling frame first in strata before either a simple random technique or a
systematic approach is used to draw the sample.
Stratified Sampling
21. • Convenience sampling is where subjects are selected because of their
convenient accessibility and proximity to the researcher.
• Convenience Sampling involves the selection of samples from whatever
cases/subjects or respondents that happens to be available at a given place or
time.
• Also known as Incidental/Accidental, Opportunity or Grab Sampling.
Snow- ball Sampling is a special type of convenience sampling where
individuals or persons that have agreed or showed up to be interviewed in the
study serially recommend their acquaintances.
Convenience Sampling
22. • In Cluster Sampling, samples are selected in two or more stages
• Non-probability sampling involves a technique where samples
are gathered in a process that does not give all the individuals in
the population equal chances of being selected.
• Nonprobability sampling procedures are not valid for obtaining a sample that is
truly representative of a larger population
Other Sampling
23. Exploring Your Data
• Good next step is to create a histogram, in which you group
your data into discrete buckets and count how many points
fall into each bucket:
df = pd.read_csv('brfss.csv', index_col=0)
df['weight2'].hist(bins=100)
A histogram is a plot that lets
you discover, and show, the
underlying frequency
distribution (shape) of a set
of continuous data. This
allows the inspection of the
data for its underlying
distribution (e.g., normal
distribution), outliers,
skewness, etc.
24. Regression – estimation of the relationship between variables
• Linear regression
• Assessing the assumptions
• Non-linear regression
Correlation
• Correlation coefficient quantifies the association strength
• Sensitivity to the distribution
Regression vs Correlation
Relationship No Relationship
26. Correlation quantifies the degree to which two variables are
related.
• Correlation does not fit a line through the data points. You simply are
computing a correlation coefficient (r) that tells you how much one variable
tends to change when the other one does.
• When r is 0.0, there is no relationship. When r is positive, there is a trend
that one variable goes up as the other one goes up. When r is negative,
there is a trend that one variable goes up as the other one goes down.
Linear regression finds the best line that predicts Y from X.
• Correlation is almost always used when you measure both variables. It
rarely is appropriate when one variable is something you experimentally
manipulate.
• Linear regression is usually used when X is a variable you manipulate
Regression vs Correlation
28. Feature Matrix
• We can review the relationships between attributes by
looking at the distribution of the interactions of each pair of
attributes.
from pandas.tools.plotting import scatter_matrix
scatter_matrix(df[['weight2', 'wtyrago', 'htm3' ]]) This is a powerful plot from
which a lot of inspiration
about the data can be drawn.
For example, we can see a
possible correlation between
weight and weight year ago
29. 3 - 29
There are two basic types of data: numerical and
categorical data.
Numerical data: data to which a number is
assigned as a quantitative value.
age, weight, shoe size….
Categorical data: data defined by the classes or
categories into which an individual member falls.
eye color, gender, blood type, ethnicity
Types of data
30. Continuous or Non-continuous data
• A continuous variable is one in which it can
theoretically assume any value between the lowest and
highest point on the scale on which it is being measured
• (e.g. weight, speed, price, time, height)
• Non-continuous variables, also known as discrete
variables, that can only take on a finite number of values
• Discrete data can be numeric -- like numbers of
apples -- but it can also be categorical -- like red or
blue, or male or female, or good or bad.
31. Qualitative vs. Quantitative Data
• A qualitative data is one in which the “true” or naturally
occurring levels or categories taken by that variable are not
described as numbers but rather by verbal groupings
• Open ended answers
• Quantitative data on the other hand are those in which the
natural levels take on certain quantities (e.g. price, travel time)
• That is, quantitative variables are measurable in some
numerical unit (e.g. pesos, minutes, inches, etc.)
• Likert scales, semantic scales, yes/no, check box
32. Data transformation
• Transform data to obtain a certain distribution
• transform data so different columns became comparable / compatible
• Typical transformation approach:
• Z-score transformation
• Scale to between 0 and 1
• mean normalization
33. Rescaling
• Many techniques are sensitive to the scale of your data. For
example, imagine that you have a data set consisting of the
heights and weights of hundreds of data scientists, and that
you are trying to identify clusters of body sizes.
data = {"height_inch":{'A':63, 'B':67, 'C':70},
"height_cm":{'A':160, 'B':170.2, 'C':177.8},
"weight":{'A':150, 'B':160, 'C':171}}
df2 = DataFrame(data)
print(df2)
34. Why normalization (re-scaling)
height_inch height_cm weight
A 63 160.0 150
B 67 170.2 160
C 70 177.8 171
from scipy.spatial import distance
a = df2.iloc[0, [0,2]]
b = df2.iloc[1, [0,2]]
c = df2.iloc[2, [0,2]]
print("%.2f" % distance.euclidean(a,b)) #10.77
print("%.2f" % distance.euclidean(a,c)) # 22.14
print("%.2f" % distance.euclidean(b,c)) #11.40
35. Boxplot
The box plot (a.k.a. box and whisker diagram) is a standardized way of displaying
the distribution of data based on the five number summary: minimum, first quartile,
median, third quartile, and maximum. In the simplest box plot the central rectangle
spans the first quartile to the third quartile (the interquartile range or IQR). A
segment inside the rectangle shows the median and "whiskers" above and below
the box show the locations of the minimum and maximum.
36. Boxplot example
df=DataFrame({'a': np.random.rand(1000),
'b': np.random.randn(1000),'c': np.random.lognormal(size=(1000))})
print(df.head())
df.boxplot()
a b c
0 0.316825 -1.418293 2.090594
1 0.451174 0.901202 0.735789
2 0.208511 -0.710432 1.409085
3 0.254617 -0.637264 2.398320
4 0.256281 -0.564593 1.821763
38. Activity 9
• Use the brfss.csv file and load it to your python module.
• https://www.cs.odu.edu/~sampath/courses/f19/cs620/files/data/brfss.csv
• Use the min-max algorithm to re-scale the data. Remember to
drop the column ‘sex’ from the dataframe before the rescaling.
(Activity 8)
• (series – series.min())/(series.max() – series.min())
• Create a boxplot (DataFrame.boxplot()) of the dataset.
39. Z-score transformation
• Z scores, or standard scores, indicate how many standard
deviations an observation is above or below the mean. These
scores are a useful way of putting data from different sources
onto the same scale.
• The z-score linearly transforms the data in such a way, that
the mean value of the transformed data equals 0 while their
standard deviation equals 1. The transformed values
themselves do not lie in a particular interval like [0,1] or so.
Z score: Z = (x - sample mean)/sample standard deviation.