9.3 Data analysis techniques and tools
3 min read•august 9, 2024
Data analysis is the backbone of data journalism. From spreadsheets to databases, journalists use various tools to clean, extract, and make sense of information. These techniques help uncover stories hidden in complex datasets.
Visualization brings data to life. Interactive tools and mapping software transform raw numbers into compelling visuals. This allows journalists to present findings in ways that engage readers and make complex information accessible to a wider audience.
Data Management and Cleaning
Spreadsheet Software and Functionality
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- and serve as popular spreadsheet tools for organizing and analyzing data
- Spreadsheets organize information into rows and columns, allowing for easy data entry and manipulation
- Functions in spreadsheets automate calculations and data processing (, , )
- summarize large datasets by aggregating and categorizing information
- Conditional formatting highlights data based on specified criteria, improving visual analysis
Database Systems and SQL
- () store and organize large volumes of structured data
- use tables with defined relationships between data elements
- () allows users to interact with and manipulate databases
- SQL commands include for retrieving data, for adding new records, and for combining tables
- in databases improves query performance by creating data structures for faster searches
Data Cleaning Techniques
- involves identifying and correcting errors, inconsistencies, and inaccuracies in datasets
- Handling missing values through or deletion improves data completeness
- ensures consistency (date formats, units of measurement)
- Removing prevents skewed analysis results
- and treatment addresses extreme values that may distort statistical analyses
Data Extraction and Analysis
Data Mining and Machine Learning
- extracts patterns and insights from large datasets using statistical and techniques
- categorize data into predefined groups (, )
- group similar data points without predefined categories (, )
- identifies relationships between variables in large databases
- applies data mining techniques to unstructured text data, extracting meaningful information
Programming Languages for Data Analysis
- specializes in statistical computing and graphics
- (, ) extend functionality for data manipulation and visualization
- offers versatility for data analysis, machine learning, and
- Python libraries (, , ) provide powerful tools for data manipulation and analysis
- Both R and Python support reproducible research through markdown documents and Jupyter notebooks
Web Scraping and API Integration
- Data scraping extracts information from websites, converting unstructured web data into structured formats
- (, ) facilitate web scraping in Python
- API (Application Programming Interface) integration allows direct access to data from external sources
- use HTTP requests to interact with web services and retrieve data
- and ethical considerations play crucial roles in responsible web scraping practices
Data Visualization and Mapping
Interactive Data Visualization Tools
- creates interactive and shareable visualizations without extensive coding
- Tableau's drag-and-drop interface allows for quick creation of charts, graphs, and dashboards
- Data connections in Tableau support various file formats and database systems
- in Tableau enable custom metrics and data transformations
- in Tableau create guided narratives through a series of visualizations
Geographic Information Systems and Spatial Analysis
- () analyze and visualize spatial or geographic data
- in GIS represents discrete features (points, lines, polygons)
- in GIS uses a grid of cells to represent continuous data (elevation, temperature)
- Spatial analysis techniques include , , and
- converts addresses into geographic coordinates for mapping and analysis
- Web-based mapping tools (, ) create interactive online maps and visualizations
Key Terms to Review (59)
API Integration: API integration refers to the process of connecting different software applications through their application programming interfaces (APIs) to enable data exchange and functionality sharing. This connection allows systems to communicate, share data, and enhance overall efficiency by automating workflows. By integrating various tools and technologies, organizations can streamline their operations and leverage data from multiple sources to improve decision-making.
Association rule mining: Association rule mining is a data analysis technique used to discover interesting relationships or patterns between variables in large datasets. It focuses on identifying rules that predict the occurrence of an item based on the presence of other items, which can be extremely useful in various fields like market basket analysis, recommendation systems, and customer behavior analysis.
Average: The average is a statistical concept that represents the central value of a set of numbers, calculated by summing the values and dividing by the count of those values. This measure helps summarize large amounts of data, making it easier to understand and analyze trends or patterns within the data. Averages can be used in various contexts, such as comparing performance metrics or understanding population demographics.
Beautifulsoup: Beautiful Soup is a Python library designed for web scraping, making it easier to extract data from HTML and XML documents. It allows users to navigate the parse tree, search for specific elements, and manipulate the data extracted, which is particularly useful for data analysis and gathering information from various web sources.
Buffer analysis: Buffer analysis is a spatial analysis technique used to determine the area surrounding a geographic feature within a specified distance. This method is essential for understanding the influence of certain locations, such as services or hazards, on their surrounding environment and can help in decision-making processes in various fields like urban planning, environmental science, and public health.
Calculated Fields: Calculated fields are custom fields created in data analysis tools that derive their values from other existing data fields through mathematical expressions or functions. These fields allow analysts to perform complex calculations on the data set without altering the original data, enabling deeper insights and more tailored reporting.
Classification algorithms: Classification algorithms are a type of machine learning model used to categorize data into predefined classes or groups based on input features. These algorithms analyze historical data to identify patterns and make predictions about which category new data points belong to, making them essential tools in data analysis and decision-making processes.
Clustering algorithms: Clustering algorithms are a type of unsupervised learning technique used in data analysis to group a set of objects into clusters based on their similarities. These algorithms help to identify patterns and structures within data sets by organizing similar data points together while separating dissimilar ones. They are essential in various applications, including market segmentation, social network analysis, and image recognition, as they facilitate understanding large amounts of data without pre-existing labels.
Data cleaning: Data cleaning is the process of correcting or removing inaccurate, incomplete, or irrelevant data from a dataset to improve its quality and ensure reliable analysis. This practice is essential in data journalism and data analysis, as it directly impacts the accuracy of insights derived from data. By refining datasets, journalists can effectively communicate stories and support their findings with trustworthy evidence.
Data mining: Data mining is the process of discovering patterns and extracting valuable information from large sets of data using various analytical techniques. It plays a crucial role in journalism by helping researchers and journalists uncover trends, insights, and stories hidden within vast amounts of data, thus enhancing the overall quality of reporting and analysis.
Database management systems: Database management systems (DBMS) are software applications that interact with end-users, other applications, and the database itself to capture and analyze data. They provide a systematic way to create, retrieve, update, and manage data, ensuring data integrity and security. DBMS are crucial for efficiently organizing large amounts of information, making them essential tools for data analysis and tracking financial flows.
Dbms: A Database Management System (DBMS) is software that enables users to create, manage, and manipulate databases. It provides the necessary tools for data storage, retrieval, and organization, making it easier to analyze and manage large volumes of information. A DBMS plays a crucial role in ensuring data integrity, security, and consistency, which are essential for effective data analysis.
Decision trees: Decision trees are a data analysis technique used for classification and regression that visually represent decisions and their possible consequences. They break down complex decision-making processes into a series of simple, branching pathways that lead to a final outcome or prediction. By using a tree-like structure, decision trees make it easier to understand the relationships between different variables and outcomes.
Dplyr: dplyr is a powerful R package designed for data manipulation and analysis, providing a consistent set of functions to work with data frames. It simplifies common data manipulation tasks like filtering, summarizing, and rearranging data, making it essential for data analysis in R. This package is part of the tidyverse, a collection of R packages that share an underlying design philosophy and grammar.
Duplicate records: Duplicate records refer to instances in a database where identical or nearly identical entries exist for the same entity, such as a person, organization, or event. These duplicates can arise from various factors, including data entry errors, system integration issues, or merging of datasets. Managing and eliminating duplicate records is crucial for maintaining data integrity, improving analysis accuracy, and ensuring efficient data retrieval.
Excel: Excel is a powerful spreadsheet application developed by Microsoft that allows users to organize, analyze, and visualize data through tables, formulas, and various analytical tools. It is widely used in data journalism for handling large datasets, performing calculations, and generating charts that help in the interpretation of data. The ability to manipulate data effectively makes Excel an essential tool for journalists looking to uncover insights and present findings in a clear and compelling way.
Geocoding: Geocoding is the process of converting addresses or place names into geographic coordinates, which can be used to map locations on a digital map. This technique enables the integration of location data with other datasets, enhancing analysis and decision-making. By transforming textual information into numerical coordinates, geocoding supports various applications in fields like urban planning, transportation, and marketing.
Geographic information systems: Geographic Information Systems (GIS) are powerful tools used for capturing, storing, analyzing, and managing spatial or geographic data. They allow users to visualize data in a map format, making it easier to identify patterns, relationships, and trends related to location. GIS integrates various data types, including maps, satellite images, and databases, to support decision-making in fields like urban planning, environmental management, and transportation.
Ggplot2: ggplot2 is a powerful R package for data visualization that implements the grammar of graphics, allowing users to create complex and aesthetically pleasing plots using a layered approach. It enables analysts and researchers to explore and present data insights effectively, emphasizing the relationship between different data variables through customizable visual representations.
Gis: GIS, or Geographic Information System, is a technology used for capturing, storing, analyzing, and managing spatial and geographic data. It enables users to visualize data in the form of maps, making it easier to interpret complex information related to locations and relationships between various geographical elements. By integrating various data sources and providing tools for spatial analysis, GIS plays a crucial role in decision-making across different sectors.
Google Sheets: Google Sheets is a web-based spreadsheet application that allows users to create, edit, and collaborate on spreadsheets in real-time. This tool is part of Google's suite of productivity applications, offering a range of features for data analysis, including formulas, charts, and data visualization tools that make it an essential resource for organizing and interpreting information effectively.
Hierarchical Clustering: Hierarchical clustering is a method of cluster analysis that seeks to build a hierarchy of clusters. It creates a tree-like structure known as a dendrogram that illustrates the arrangement of clusters based on their similarities or distances. This technique is widely used in various data analysis tasks, allowing researchers to visualize the relationships among data points, making it easier to identify patterns and structures within complex datasets.
Html parsing tools: HTML parsing tools are software applications or libraries designed to read and interpret HTML documents, enabling users to extract and manipulate data from web pages. These tools play a critical role in web scraping, data extraction, and analysis by transforming unstructured HTML content into structured formats that can be easily understood and processed. They allow users to navigate the Document Object Model (DOM) of a webpage, making it possible to identify and retrieve specific elements, attributes, or text for further use in research and data analysis.
Imputation: Imputation is a statistical technique used to fill in missing data points in a dataset, allowing for more accurate analysis and interpretation. By estimating and replacing the missing values based on the available information, this method helps maintain the integrity of the dataset, ensuring that analyses are not biased or skewed due to gaps in data. Imputation is particularly important when working with large datasets, where missing values can significantly impact results and conclusions drawn from the analysis.
Indexing: Indexing refers to the process of organizing data in a way that allows for efficient retrieval and analysis. This technique is crucial in data analysis as it enables researchers to quickly locate relevant information within large datasets, making the analysis process faster and more accurate. Indexing can involve creating indices or keys that link data points, allowing for easier access and manipulation of the data during analysis.
Insert: In the context of data analysis, 'insert' refers to the action of adding new data or records into a database or dataset. This process is crucial for maintaining up-to-date information and can involve various methods, such as importing data from external sources or manually entering it. The ability to insert data effectively allows researchers to enrich their datasets, leading to more comprehensive analyses and conclusions.
Interactive data visualization tools: Interactive data visualization tools are software applications that enable users to explore, manipulate, and interact with data through graphical representations. These tools allow users to visualize complex datasets in a user-friendly manner, making it easier to identify patterns, trends, and insights. By facilitating real-time interactions, these tools enhance the analytical process and support informed decision-making.
Join: In data analysis, a 'join' is a technique used to combine data from two or more tables based on a related column between them. This process allows for a more comprehensive view of data by integrating various datasets, facilitating deeper insights and enabling more complex queries.
K-means: k-means is a popular clustering algorithm used in data analysis that partitions data into k distinct groups based on their features. Each group, or cluster, is represented by its centroid, which is the average of all points in that cluster. This technique helps in identifying patterns and organizing data into meaningful structures, making it valuable for exploratory data analysis.
Leaflet: A leaflet is a printed piece of paper that provides information or promotes an event, product, or service, typically designed to be easily distributed and read. Leaflets are often used for marketing and educational purposes, allowing organizations to reach a wide audience in a concise format while utilizing various design elements to attract attention.
Machine learning: Machine learning is a subset of artificial intelligence that involves the use of algorithms and statistical models to enable computers to perform tasks without explicit instructions, relying instead on patterns and inference. It plays a crucial role in data analysis by automating the interpretation of large datasets, which can enhance research methods, inform news reporting, and adapt to new media platforms. As technology advances, machine learning continues to reshape how journalists gather, analyze, and present information.
Mapbox: Mapbox is a powerful mapping platform that provides tools for developers to create customizable and interactive maps. It offers a range of services, including geolocation, map visualization, and data-driven mapping solutions that are essential for visualizing spatial data effectively. Its flexibility and accessibility make it a go-to tool for data analysis and presenting research findings in a compelling way.
Network analysis: Network analysis is a research method used to examine relationships and interactions within a network, often involving social networks, information networks, or communication networks. It focuses on understanding how different entities are connected, the flow of information among them, and the patterns that emerge from these connections. This approach is essential in analyzing data across various platforms, revealing insights that can inform strategic decisions in journalism and media.
Numpy: NumPy is a powerful library in Python that is used for numerical computing and data analysis. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays. NumPy is essential for data manipulation and serves as the foundation for many other libraries in the data science ecosystem, making it a critical tool in the realm of data analysis techniques and tools.
Outlier detection: Outlier detection is the process of identifying data points that differ significantly from the rest of the dataset. These anomalous values can indicate errors in data collection, variability in the measurement process, or novel phenomena that warrant further investigation. Detecting outliers is crucial as they can skew statistical analyses and affect the accuracy of predictive models.
Overlay Operations: Overlay operations are techniques used in geographic information systems (GIS) to analyze spatial data by combining multiple layers of information. These operations allow users to visualize, compare, and understand the relationships between different data sets, which can include mapping features like land use, environmental data, and demographic information.
Pandas: Pandas is a powerful open-source data analysis and manipulation library for the Python programming language, designed to make data handling and analysis more straightforward. It provides data structures like Series and DataFrames, which are optimized for performance and ease of use, enabling users to work with structured data seamlessly. This library is essential for various data analysis tasks, from cleaning and transforming data to performing complex statistical analyses.
Pivot Tables: A pivot table is a powerful data analysis tool used in spreadsheet applications that allows users to summarize and reorganize selected columns and rows of data to obtain a desired report. It enables users to quickly analyze large sets of data by transforming it into a more understandable format, providing insights through various calculations and aggregations.
Python: Python is a high-level, interpreted programming language known for its readability and versatility. Widely used in data analysis, Python supports various libraries and frameworks that make it an essential tool for managing, analyzing, and visualizing data efficiently.
R packages: R packages are collections of functions, data, and documentation bundled together for use in the R programming language, designed to extend its capabilities for data analysis, statistical computing, and visualization. They simplify complex tasks by providing pre-written code, making it easier for users to perform various analyses without having to start from scratch. R packages are essential for utilizing specific techniques and tools effectively in the realm of data analysis.
R programming language: R programming language is a powerful and versatile language used primarily for statistical computing and data analysis. It provides a variety of tools and libraries that enable users to manipulate, visualize, and model data effectively, making it essential for data scientists and analysts. R's open-source nature allows for constant updates and contributions from the community, ensuring that it remains relevant in the ever-evolving field of data analysis.
Raster data: Raster data is a type of digital image represented by reducible and non-reducible grids of pixels or cells, each containing a value that represents information such as color, temperature, or elevation. This format is widely used in geographic information systems (GIS), remote sensing, and image processing for various data analysis techniques and tools, enabling detailed visualization and analysis of spatial information.
Rate limiting: Rate limiting refers to a phenomenon in which the speed or flow of a process is restricted by one or more factors, preventing it from proceeding at its maximum potential. In the context of data analysis techniques and tools, rate limiting often affects data processing speed, analysis outcomes, and overall system performance, especially when dealing with large datasets or APIs.
Relational databases: Relational databases are a type of database management system that organizes data into tables, which can be linked—or related—based on data common to each. This structure allows for efficient data retrieval, manipulation, and management, making it easier to analyze large volumes of data and draw meaningful insights. Relational databases are foundational in various data analysis techniques and tools, enabling users to perform complex queries and maintain data integrity through relationships.
RESTful APIs: RESTful APIs (Representational State Transfer APIs) are a set of rules and conventions for building and interacting with web services. They allow different software applications to communicate with each other over the internet using standard HTTP methods like GET, POST, PUT, and DELETE. This architecture is designed to be stateless, meaning each request from a client to the server must contain all the information needed to understand and process that request, making them highly scalable and efficient for data analysis techniques.
Scikit-learn: scikit-learn is a popular open-source machine learning library for Python that provides simple and efficient tools for data analysis and modeling. It supports various supervised and unsupervised learning algorithms, making it a go-to library for data scientists and analysts looking to implement machine learning techniques easily and quickly.
Scrapy: Scrapy is an open-source web crawling framework written in Python, designed for web scraping and extracting data from websites. It enables developers to write spiders that navigate through web pages, extract structured data, and store it in various formats. This tool is essential for automating the process of gathering large amounts of data efficiently and effectively.
Select: To select means to choose or pick out from a group based on specific criteria or preferences. This concept is fundamental in data analysis, as it involves determining which data points are relevant to the research question, which helps in filtering out noise and focusing on significant information.
SQL: SQL, or Structured Query Language, is a standardized programming language used for managing and manipulating relational databases. It allows users to create, read, update, and delete data efficiently within a database system. SQL's versatility makes it a fundamental tool for data analysis techniques and tools, enabling analysts to extract insights and make data-driven decisions.
Standardizing data formats: Standardizing data formats refers to the process of converting data into a common structure or set of rules, which facilitates consistent data collection, analysis, and interpretation. This practice ensures that information can be easily shared, compared, and integrated across different systems or platforms, enhancing the reliability and usability of data during analysis. By adopting standardized formats, researchers and analysts can minimize errors and improve the overall quality of their findings.
Story points: Story points are a unit of measure used in agile project management to estimate the relative effort required to complete a particular task or user story. They help teams assess the complexity, risks, and time involved in development work, allowing for better planning and prioritization of tasks. By using story points, teams can create a shared understanding of the workload involved in each user story, which aids in improving overall productivity and efficiency.
Structured query language: Structured Query Language, or SQL, is a standardized programming language specifically designed for managing and manipulating relational databases. SQL enables users to perform various operations like querying data, updating records, and creating or modifying database structures. It's essential for data analysis and retrieval, making it a vital tool in handling large datasets effectively.
Sum: In data analysis, the sum refers to the total obtained by adding together a series of numbers or values. It is a fundamental operation that helps in understanding datasets by providing insights into overall trends and patterns, allowing researchers to quantify data points and derive meaningful conclusions.
Support Vector Machines: Support Vector Machines (SVM) are supervised learning models used for classification and regression tasks that work by finding the optimal hyperplane that best separates different classes in a dataset. They are powerful tools in data analysis, particularly useful for high-dimensional spaces, and are popular due to their effectiveness in handling both linear and non-linear classification problems.
Tableau: A tableau is a powerful data visualization tool that allows users to create interactive and shareable dashboards. It helps in representing data visually, making complex information more understandable and accessible to audiences. By turning raw data into engaging graphics, a tableau plays a crucial role in data journalism, enhancing the presentation and analysis of information.
Text mining: Text mining is the process of deriving meaningful information and patterns from unstructured text data using various computational and analytical techniques. It helps researchers and journalists uncover insights from vast amounts of textual information, allowing for more informed decisions and narratives. By applying natural language processing (NLP), machine learning, and statistical methods, text mining can extract valuable insights, trends, and relationships hidden within large datasets.
Vector data: Vector data is a type of data used in geographic information systems (GIS) that represents spatial features through geometric shapes like points, lines, and polygons. It allows for precise representation of real-world objects and can be easily manipulated, analyzed, and displayed, making it essential for various data analysis techniques and tools.
Vlookup: VLOOKUP is a powerful Excel function used to search for a specific value in the first column of a table and return a corresponding value from another column in the same row. This function is essential for data analysis as it allows users to quickly retrieve and analyze data from large datasets, making it easier to draw insights and make informed decisions.
Web scraping: Web scraping is the automated process of extracting data from websites, allowing users to collect large amounts of information efficiently. This technique is often used in various fields, including journalism, where data-driven stories rely on information gathered from online sources. As the demand for data journalism grows, understanding web scraping's ethical implications and the tools available for analysis becomes crucial.