Exploratory data analysis data visualization:
Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques (mostly graphical) to
Maximize insight into a data set.
Uncover underlying structure.
Extract important variables.
Detect outliers and anomalies.
Test underlying assumptions.
Develop parsimonious models.
Determine optimal factor settings
The document discusses principles and techniques for exploratory data analysis including:
1) Showing comparisons, causality, and systematic structure through data visualization principles.
2) Creating one dimensional and two dimensional plots like scatter plots to understand data properties and find patterns.
3) Using base plotting systems, lattice systems, and ggplot2 systems which offer different levels of customization for creating plots.
4) Addressing issues like scaling, cost, and clustering when analyzing exploratory data.
The document discusses exploratory data analysis and provides examples of how it can be used. It summarizes two case studies: one where an energy utility detected billing fraud by analyzing meter reading patterns, and another where month of birth was found to correlate with exam scores for students in Tamil Nadu. The document then outlines the exploratory data analysis process and provides a high-level overview of U.S. and Indian birth date patterns identified through analysis of large datasets.
This document provides an overview of exploratory data analysis (EDA). It discusses how EDA is used to generate and refine questions from data by visualizing, transforming, and modeling the data. Questions can come from hypotheses, problems, or the data itself. EDA plays a role in developing, testing, and refining theories, solving problems, and asking interesting questions about the data. The document emphasizes being skeptical of assumptions and open to multiple interpretations during EDA to maximize learning from the data. It introduces the dplyr and ggplot2 packages for selecting, filtering, summarizing, and visualizing data during the EDA process.
Data Visualization in Exploratory Data AnalysisEva Durall
This document outlines activities for exploring equity in science education outside the classroom using data visualization. It introduces exploratory data analysis and how data visualization can help generate hypotheses from data. The activities include analyzing an interactive map of science education organizations, and creating visualizations to explore equity indicators like access, diversity, and inclusion. Effective visualization requires defining goals, finding relevant data, and experimenting with different chart types to answer questions arising from the data.
This presentation gives the idea about Data Preprocessing in the field of Data Mining. Images, examples and other things are adopted from "Data Mining Concepts and Techniques by Jiawei Han, Micheline Kamber and Jian Pei "
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.
Exploratory data analysis data visualization:
Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques (mostly graphical) to
Maximize insight into a data set.
Uncover underlying structure.
Extract important variables.
Detect outliers and anomalies.
Test underlying assumptions.
Develop parsimonious models.
Determine optimal factor settings
The document discusses principles and techniques for exploratory data analysis including:
1) Showing comparisons, causality, and systematic structure through data visualization principles.
2) Creating one dimensional and two dimensional plots like scatter plots to understand data properties and find patterns.
3) Using base plotting systems, lattice systems, and ggplot2 systems which offer different levels of customization for creating plots.
4) Addressing issues like scaling, cost, and clustering when analyzing exploratory data.
The document discusses exploratory data analysis and provides examples of how it can be used. It summarizes two case studies: one where an energy utility detected billing fraud by analyzing meter reading patterns, and another where month of birth was found to correlate with exam scores for students in Tamil Nadu. The document then outlines the exploratory data analysis process and provides a high-level overview of U.S. and Indian birth date patterns identified through analysis of large datasets.
This document provides an overview of exploratory data analysis (EDA). It discusses how EDA is used to generate and refine questions from data by visualizing, transforming, and modeling the data. Questions can come from hypotheses, problems, or the data itself. EDA plays a role in developing, testing, and refining theories, solving problems, and asking interesting questions about the data. The document emphasizes being skeptical of assumptions and open to multiple interpretations during EDA to maximize learning from the data. It introduces the dplyr and ggplot2 packages for selecting, filtering, summarizing, and visualizing data during the EDA process.
Data Visualization in Exploratory Data AnalysisEva Durall
This document outlines activities for exploring equity in science education outside the classroom using data visualization. It introduces exploratory data analysis and how data visualization can help generate hypotheses from data. The activities include analyzing an interactive map of science education organizations, and creating visualizations to explore equity indicators like access, diversity, and inclusion. Effective visualization requires defining goals, finding relevant data, and experimenting with different chart types to answer questions arising from the data.
This presentation gives the idea about Data Preprocessing in the field of Data Mining. Images, examples and other things are adopted from "Data Mining Concepts and Techniques by Jiawei Han, Micheline Kamber and Jian Pei "
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.
The document introduces data preprocessing techniques for data mining. It discusses why data preprocessing is important due to real-world data often being dirty, incomplete, noisy, inconsistent or duplicate. It then describes common data types and quality issues like missing values, noise, outliers and duplicates. The major tasks of data preprocessing are outlined as data cleaning, integration, transformation and reduction. Specific techniques for handling missing values, noise, outliers and duplicates are also summarized.
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
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.
The document discusses data preprocessing techniques. It explains that data preprocessing is important because real-world data is often noisy, incomplete, and inconsistent. The key techniques covered are data cleaning, integration, reduction, and transformation. Data cleaning handles missing values, noise, and outliers. Data integration merges data from multiple sources. Data reduction reduces data size through techniques like dimensionality reduction. Data transformation normalizes and aggregates data to make it suitable for mining.
This document discusses data cleansing and provides steps in the data cleansing process. It defines data cleansing as detecting and correcting inaccurate or corrupt records in a database. The key steps described are parsing, correcting, standardizing, matching, and consolidating data. The goal of data cleansing is to clean data within and between databases to make information consistent and suitable for effective decision making. Metadata should document rules and data quality should be built into new systems through regular cleansing schedules.
In this presentation, I have talked about Big Data and its importance in brief. I have included the very basics of Data Science and its importance in the present day, through a case study. You can also get an idea about who a data scientist is and what all tasks he performs. A few applications of data science have been illustrated in the end.
It is an introduction to Data Analytics, its applications in different domains, the stages of Analytics project and the different phases of Data Analytics life cycle.
I deeply acknowledge the sources from which I could consolidate the material.
This document discusses dimensionality reduction techniques for data mining. It begins with an introduction to dimensionality reduction and reasons for using it. These include dealing with high-dimensional data issues like the curse of dimensionality. It then covers major dimensionality reduction techniques of feature selection and feature extraction. Feature selection techniques discussed include search strategies, feature ranking, and evaluation measures. Feature extraction maps data to a lower-dimensional space. The document outlines applications of dimensionality reduction like text mining and gene expression analysis. It concludes with trends in the field.
This document discusses techniques for data reduction to reduce the size of large datasets for analysis. It describes five main strategies for data reduction: data cube aggregation, dimensionality reduction, data compression, numerosity reduction, and discretization. Data cube aggregation involves aggregating data at higher conceptual levels, such as aggregating quarterly sales data to annual totals. Dimensionality reduction removes redundant attributes. The document then focuses on attribute subset selection techniques, including stepwise forward selection, stepwise backward elimination, and combinations of the two, to select a minimal set of relevant attributes. Decision trees can also be used for attribute selection by removing attributes not used in the tree.
This is a presentation I gave on Data Visualization at a General Assembly event in Singapore, on January 22, 2016. The presso provides a brief history of dataviz as well as examples of common chart and visualization formatting mistakes that you should never make.
Data preprocessing techniques
See my Paris applied psychology conference paper here
https://www.slideshare.net/jasonrodrigues/paris-conference-on-applied-psychology
or
https://prezi.com/view/KBP8JnekVH9LkLOiKY3w/
Lecture 4 Decision Trees (2): Entropy, Information Gain, Gain RatioMarina Santini
attribute selection, constructing decision trees, decision trees, divide and conquer, entropy, gain ratio, information gain, machine leaning, pruning, rules, suprisal
This presentation deals with the formal presentation of anomaly detection and outlier analysis and types of anomalies and outliers. Different approaches to tackel anomaly detection problems.
Data Mining: Concepts and Techniques (3rd ed.)- Chapter 3 preprocessingSalah Amean
the chapter contains :
Data Preprocessing: An Overview,
Data Quality,
Major Tasks in Data Preprocessing,
Data Cleaning,
Data Integration,
Data Reduction,
Data Transformation and Data Discretization,
Summary.
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.
This document discusses privacy, security, and ethics in data science. It covers topics such as anonymizing data and computations, seeking security for personal data, and the unethical surprises that can occur in data science work. It also discusses how to respect privacy by securely storing data, adding layers of protection like encryption, and using techniques like distributed computing and differential privacy to better protect sensitive information. The document cautions that biases in data can propagate biases in models, and highlights the importance of addressing issues like social bias, redaction of sensitive info, and debiasing models to help ensure ethical practices in this field.
Logistic regression is a machine learning classification algorithm that predicts the probability of a categorical dependent variable. It models the probability of the dependent variable being in one of two possible categories, as a function of the independent variables. The model transforms the linear combination of the independent variables using the logistic sigmoid function to output a probability between 0 and 1. Logistic regression is optimized using maximum likelihood estimation to find the coefficients that maximize the probability of the observed outcomes in the training data. Like linear regression, it makes assumptions about the data being binary classified with no noise or highly correlated independent variables.
Exploratory Data Analysis - A Comprehensive Guide to EDA.pdfJamieDornan2
EDA or Exploratory Data Analysis is a method of examining and understanding data using multiple techniques like visualization, summary statistics and data transformation to abstract its core characteristics. EDA is done to get a sense of data and discover any potential problems or issues which need to be addressed and is generally performed before formal modeling or hypothesis testing.
Exploratory Data Analysis - A Comprehensive Guide to EDA.pdfJamieDornan2
Exploratory Data Analysis is a method of examining and understanding data using multiple techniques like visualization, summary statistics and data transformation to abstract its core characteristics. EDA is done to get a sense of data and discover any potential problems or issues which need to be addressed and is generally performed before formal modeling or hypothesis testing.
The document introduces data preprocessing techniques for data mining. It discusses why data preprocessing is important due to real-world data often being dirty, incomplete, noisy, inconsistent or duplicate. It then describes common data types and quality issues like missing values, noise, outliers and duplicates. The major tasks of data preprocessing are outlined as data cleaning, integration, transformation and reduction. Specific techniques for handling missing values, noise, outliers and duplicates are also summarized.
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
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.
The document discusses data preprocessing techniques. It explains that data preprocessing is important because real-world data is often noisy, incomplete, and inconsistent. The key techniques covered are data cleaning, integration, reduction, and transformation. Data cleaning handles missing values, noise, and outliers. Data integration merges data from multiple sources. Data reduction reduces data size through techniques like dimensionality reduction. Data transformation normalizes and aggregates data to make it suitable for mining.
This document discusses data cleansing and provides steps in the data cleansing process. It defines data cleansing as detecting and correcting inaccurate or corrupt records in a database. The key steps described are parsing, correcting, standardizing, matching, and consolidating data. The goal of data cleansing is to clean data within and between databases to make information consistent and suitable for effective decision making. Metadata should document rules and data quality should be built into new systems through regular cleansing schedules.
In this presentation, I have talked about Big Data and its importance in brief. I have included the very basics of Data Science and its importance in the present day, through a case study. You can also get an idea about who a data scientist is and what all tasks he performs. A few applications of data science have been illustrated in the end.
It is an introduction to Data Analytics, its applications in different domains, the stages of Analytics project and the different phases of Data Analytics life cycle.
I deeply acknowledge the sources from which I could consolidate the material.
This document discusses dimensionality reduction techniques for data mining. It begins with an introduction to dimensionality reduction and reasons for using it. These include dealing with high-dimensional data issues like the curse of dimensionality. It then covers major dimensionality reduction techniques of feature selection and feature extraction. Feature selection techniques discussed include search strategies, feature ranking, and evaluation measures. Feature extraction maps data to a lower-dimensional space. The document outlines applications of dimensionality reduction like text mining and gene expression analysis. It concludes with trends in the field.
This document discusses techniques for data reduction to reduce the size of large datasets for analysis. It describes five main strategies for data reduction: data cube aggregation, dimensionality reduction, data compression, numerosity reduction, and discretization. Data cube aggregation involves aggregating data at higher conceptual levels, such as aggregating quarterly sales data to annual totals. Dimensionality reduction removes redundant attributes. The document then focuses on attribute subset selection techniques, including stepwise forward selection, stepwise backward elimination, and combinations of the two, to select a minimal set of relevant attributes. Decision trees can also be used for attribute selection by removing attributes not used in the tree.
This is a presentation I gave on Data Visualization at a General Assembly event in Singapore, on January 22, 2016. The presso provides a brief history of dataviz as well as examples of common chart and visualization formatting mistakes that you should never make.
Data preprocessing techniques
See my Paris applied psychology conference paper here
https://www.slideshare.net/jasonrodrigues/paris-conference-on-applied-psychology
or
https://prezi.com/view/KBP8JnekVH9LkLOiKY3w/
Lecture 4 Decision Trees (2): Entropy, Information Gain, Gain RatioMarina Santini
attribute selection, constructing decision trees, decision trees, divide and conquer, entropy, gain ratio, information gain, machine leaning, pruning, rules, suprisal
This presentation deals with the formal presentation of anomaly detection and outlier analysis and types of anomalies and outliers. Different approaches to tackel anomaly detection problems.
Data Mining: Concepts and Techniques (3rd ed.)- Chapter 3 preprocessingSalah Amean
the chapter contains :
Data Preprocessing: An Overview,
Data Quality,
Major Tasks in Data Preprocessing,
Data Cleaning,
Data Integration,
Data Reduction,
Data Transformation and Data Discretization,
Summary.
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.
This document discusses privacy, security, and ethics in data science. It covers topics such as anonymizing data and computations, seeking security for personal data, and the unethical surprises that can occur in data science work. It also discusses how to respect privacy by securely storing data, adding layers of protection like encryption, and using techniques like distributed computing and differential privacy to better protect sensitive information. The document cautions that biases in data can propagate biases in models, and highlights the importance of addressing issues like social bias, redaction of sensitive info, and debiasing models to help ensure ethical practices in this field.
Logistic regression is a machine learning classification algorithm that predicts the probability of a categorical dependent variable. It models the probability of the dependent variable being in one of two possible categories, as a function of the independent variables. The model transforms the linear combination of the independent variables using the logistic sigmoid function to output a probability between 0 and 1. Logistic regression is optimized using maximum likelihood estimation to find the coefficients that maximize the probability of the observed outcomes in the training data. Like linear regression, it makes assumptions about the data being binary classified with no noise or highly correlated independent variables.
Exploratory Data Analysis - A Comprehensive Guide to EDA.pdfJamieDornan2
EDA or Exploratory Data Analysis is a method of examining and understanding data using multiple techniques like visualization, summary statistics and data transformation to abstract its core characteristics. EDA is done to get a sense of data and discover any potential problems or issues which need to be addressed and is generally performed before formal modeling or hypothesis testing.
Exploratory Data Analysis - A Comprehensive Guide to EDA.pdfJamieDornan2
Exploratory Data Analysis is a method of examining and understanding data using multiple techniques like visualization, summary statistics and data transformation to abstract its core characteristics. EDA is done to get a sense of data and discover any potential problems or issues which need to be addressed and is generally performed before formal modeling or hypothesis testing.
Exploratory Data Analysis - A Comprehensive Guide to EDA.pdfStephenAmell4
Exploratory Data Analysis is a method of examining and understanding data using multiple techniques like visualization, summary statistics and data transformation to abstract its core characteristics. EDA is done to get a sense of data and discover any potential problems or issues which need to be addressed and is generally performed before formal modeling or hypothesis testing.
This document provides an overview of a 5-part data science course covering topics like data preparation, exploratory data analysis, regression, classification, unsupervised learning, and natural language processing. The course uses Python and Jupyter Notebook. Part 1 focuses on data preparation and exploratory data analysis. It introduces the data science workflow and covers gathering, cleaning, exploring, and preparing data. Later parts will cover specific modeling techniques. The course also outlines a project where students will apply the skills learned to analyze customer churn for a music streaming company.
This document provides an introduction to data science, including the main roles in data science, the data pipeline process (OSEMN), and steps within the process. It discusses obtaining data from various sources, cleaning data by examining for errors and missing values, exploring data through visualizations and statistics to find patterns, modeling data to create predictive algorithms, and interpreting results through data storytelling. The roles covered include data scientists, data analysts, data engineers, and business intelligence specialists.
This document discusses data quality and data profiling. It begins by describing problems with data like duplication, inconsistency, and incompleteness. Good data is a valuable asset while bad data can harm a business. Data quality is assessed based on dimensions like accuracy, consistency, completeness, and timeliness. Data profiling statistically examines data to understand issues before development begins. It helps assess data quality and catch problems early. Common analyses include analyzing null values, keys, formats, and more. Data profiling is conducted using SQL or profiling tools during requirements, modeling, and ETL design.
Exploratory Data Analysis_ Uncovering Patterns in Data.pdfarchijain931
Exploratory Data Analysis (EDA) serves as the compass that guides data scientists and analysts as they navigate the vast seas of data. It plays a pivotal role in fostering a deep understanding of data, unveiling hidden patterns, and shaping meaningful questions for further analysis. Importantly, EDA is not merely a preliminary step; it is an ongoing and iterative process that continually informs and steers data-driven decision-making.
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.
Master Data Analyst Course in Bangalore with ProITBridge's Expert Course.pdfproitbridgePvtLtd
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This document provides an overview of business intelligence and its key components. It defines business intelligence as processes, technologies, and tools that help transform data into knowledge and plans to guide business decisions. The key components discussed include data mining, data warehousing, and data analysis. Data mining involves extracting patterns from large databases, data warehousing focuses on data storage, and data analysis is the process of inspecting, cleaning, transforming, and modeling data to support decision making.
The document discusses data wrangling, which is the process of cleaning, organizing, and transforming raw data into a usable format for analysis. It defines data wrangling and describes the importance, benefits, common tools, and examples of data wrangling. It also outlines the typical iterative steps in data wrangling software and provides examples of data exploration, cleaning, and filtering in Python.
Technical Documentation 101 for Data Engineers.pdfShristi Shrestha
This document discusses metadata and data documentation best practices. It begins by defining metadata as data that describes other data, such as author, file size, and date for text files. It recommends documenting the table or database last documented, documenter, business case, tools used, and data quality. Good documentation practices include knowing your audience and purpose, keeping documentation minimal but effective, and building user documentation. Common data documentation templates include CRISP-DM, which outlines phases for documentation like business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Thorough data documentation is important for project understanding, reuse, and governance.
Closing the data source discovery gap and accelerating data discovery comprises three steps: profile, identify, and unify. This white paper discusses how the Attivio
platform executes those steps, the pain points each one addresses, and the value Attivio provides to advanced analytics and business intelligence (BI) initiatives.
This document provides an overview of a data analytics course for beginners. It covers understanding data analytics and analyzing different data types. Tools for data analytics include Excel, R, Python, SQL, Tableau and Power BI. The course covers data preparation, analysis using descriptive, predictive and prescriptive techniques, visualization, machine learning, and ethics. The goal is to help beginners understand key concepts and get started in extracting insights from data.
This document discusses the importance of data documentation and metadata. It explains that data documentation is part of the data management process and is essential for reproducible research. Metadata provides information about data, such as descriptive information, technical specifications, and administrative details. The document recommends documenting the context, methodology, structure, validation, manipulations, access conditions, variable names, codes, file format, and algorithms related to any research data. Adopting standards for data documentation and metadata can help enable effective data management and reproducible research.
Introduction to Business and Data Analysis Undergraduate.pdfAbdulrahimShaibuIssa
The document provides an introduction to business and data analytics. It discusses how businesses are recognizing the value of data analytics and are hiring and upskilling people to expand their data analytics capabilities. It also notes the significant demand for skilled data analysts. The document outlines the modern data ecosystem, including different data sources, key players in turning data into insights, and emerging technologies shaping the ecosystem. It defines data analysis and provides an overview of the data analyst ecosystem.
Data science vs. Data scientist by Jothi PeriasamyPeter Kua
This document discusses data science vs data scientists and outlines key competencies for data scientists. It defines data science as modernizing existing analytics and data solutions using new data sources, formats, architectures, and techniques. The document compares traditional and modern approaches to data and analytics. It also discusses the skills required of entry-level vs senior data scientists, noting that enterprise data scientists require strong industry and business process skills while focusing on data, analytics, communication and technical abilities. The document provides an overview of the roles, responsibilities and deliverables of data scientists on enterprise projects.
This document provides an overview of fundamentals of database design. It discusses what a database is, the difference between data and information, why databases are needed, how to select a database system, basic database definitions and building blocks, quality control considerations, and data entry methods. The overall purpose of a database management system is to transform data into information, information into knowledge, and knowledge into action.
This document provides an overview of fundamentals of database design. It discusses what a database is, the difference between data and information, why databases are needed, how to select a database system, basic database definitions and building blocks, quality control considerations, and data entry methods. The overall purpose of a database management system is to transform data into information, information into knowledge, and knowledge into action.
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Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
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Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
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2. CONTENT:
1. Introduction to EDA
2. Importance of EDA
3. Data Types
4. Python Packages for EDA
5. List of Graphs
6. Practical EDA
3. 1.INTRODUCTION TO EDA
Exploratory Data Analysis refers to the critical process of performing
initial investigations on data so as to discover patterns, to spot
anomalies, to test hypothesis and to check assumptions with the help
of summary statistics and graphical representations.
It is a good practice to understand the data first and try to gather as
many insights from it.
4. 2. IMPORTANCE OF EDA
Identifying the most important variables/features in your dataset.
Testing a hypothesis or checking assumptions related to the dataset.
To check the quality of data for further processing and cleaning.
Deliver data-driven insights to business stakeholders.
Verify expected relationships actually exist in the data.
To find unexpected structure or insights in the data.
5.
6.
7.
8. Two Categories of Data
Structured Data types
Example: csv file, excel file, database file
Unstructured Data types
Examples: Images, videos, audio,
10. Structured Data Types
Categorical - This is any data that isn’t a number.
Ordinal - have a set of order e.g. rating happiness on a scale of 1-10.
Binary - have only two values .e.g. Male or Female
Nominal - no set of order e.g. Countries
Numerical – Data inform of numbers
Continuous - numbers that don’t have a logical end to them e.g heights
Discrete - have a logical end to them e.g. days in the month