Ea F1 Analysis Tool

[Tony Phan]

Dataanalysis tools help researchers make sense of the data collected. It enables them to report results and make interpretations. How the data is analyzed depends on the goals of the project and the type of data collected. Some studies focus on qualitative data, others on quantitative data, and many on both (mixed-methods studies); examples of these can be found in a NAGT-GER Division hosted collection of presentations on Methods for Conducting GER. The Analytical Tool collection includes examples in these areas, as well as special types of analytical tool used for data specific applications and data visualizations. Quantitative and Qualitative methods both use deductive, inductive, and adductive processes to understand a process or phenomenon, just in different ways using different data.

Quantitative analysis uses numerical data to identify statistical relationships between variables. Quantitative data are numerical, ordinal, nominal. For example, surveys, questionnaires, and evaluations that include multiple choice items and ratings (e.g., Likert scale) provide quantitative data for analysis.

Qualitative analysis uses descriptive data to understand processes (e.g., how students learn in a group), develop insights into the form of sensitizing concepts, and present the view of the world from the point of view of the participants (e.g., the teachers, students and others related to the classroom). Qualitative data are descriptive. For example, field notes, interviews, video, audio, open-ended survey questions all provide qualitative data for analysis.

Some types of special analyses in geoscience education research depend on data analysis tools original developed for other purposes in the sciences or social sciences. In this section you can find descriptions of some of those tools, including eye tracking analysis software and data visualization tools (e.g., Generic Mapping Tools, MatLab, ArGIS).

Each community has unique circumstances impacting homeless populations. The CoC Analysis Tool: Race and Ethnicity draws on Point-In-Time Count (PIT) and American Community Survey (ACS) data to facilitate analysis of racial disparities among people experiencing homelessness. Such an analysis is a critical first step in identifying and changing racial and ethnic disparities in our systems and services.

The number of people experiencing homelessness represented in version 4.0 of this tool is drawn from the 2022 PIT Count data. PIT Counts are unduplicated 1-night estimates of sheltered and unsheltered homeless populations conducted by CoCs nationwide during the last week of January each year.

Select the link below to download the tool. To use the tool, select a CoC from the dropdown at the top of the Dashboard tab. The charts and tables will automatically populate with local and state data. Instruction and further details are provided in the accompanying PDF document and in the "How to Use this Tool" and "Methodology" tabs.

Data analysis is a core practice of modern businesses. Choosing the right data analytics tool is challenging, as no tool fits every need. To help you determine which data analysis tool best fits your organization, let's examine the important factors for choosing between them and then look at some of the most popular options on the market today.

There are a few things to take care of before evaluating the available tools. You should first understand the types of data your enterprise wants to analyze, and, by extension, your data integration requirements. In addition, before you can begin analyzing data, you'll need to select data sources and the tables and columns within them, and replicate them to a data warehouse to create a single source of truth for analytics. You'll want to assess data security and data governance as well. If data is shared between departments, for example, there should be access control and permission systems to protect sensitive information.

Consider a tool's data modeling capabilities. Some support a semantic layer or can perform data modeling themselves. If you want to use one that doesn't, you'll have to use SQL or a tool like dbt to model your data prior to analysis.

Finally, consider price and licensing. Some offerings are free, while others charge licensing or subscription fees. The most expensive tools are not necessarily the most feature-complete, and users should not ignore the many robust free solutions available.

Now that you know what factors to look for in a data analysis tool, let's jump into the list. We'll start with discussing the eight platforms in the Visionaries band of Gartner's Magic Quadrant for Analytics and Business Intelligence Platforms before covering other popular options.

Microsoft Power BI is a top business intelligence platform with support for dozens of data sources. It allows users to create and share reports, visualizations, and dashboards. Users can combine a group of dashboards and reports into a Power BI app for simple distribution. Power BI also allows users to build automated machine learning models and integrates with Azure Machine Learning.

SAP BusinessObjects provides a suite of business intelligence applications for data discovery, analysis, and reporting. The tools are aimed at less technical business users, but they're also capable of performing complex analysis. BusinessObjects integrates with Microsoft Office products, allowing business analysts to quickly go back and forth between applications such as Excel and BusinessObjects reports. It also allows for self-service predictive analytics.

TIBCO Spotfire is a data analytics platform that provides natural language search and AI-powered data insights. It's a comprehensive visualization tool that can publish reports to both mobile and desktop applications. Spotfire also provides point-and-click tools for building predictive analytics models.

Thoughtspot is an analytics platform that allows users to explore data from various types of sources through reports and natural language searches. Its AI system, SpotIQ, finds insights automatically to help users uncover patterns they didn't know to look for. The platform also allows users to automatically join tables from different data sources to help break down data silos.

Qlik provides a self-service data analytics and business intelligence platform that supports both cloud and on-premises deployment. The tool boasts strong support for data exploration and discovery by technical and nontechnical users alike. Qlik supports many types of charts that users can customize with both embedded SQL and drag-and-drop modules.

SAS Business Intelligence provides a suite of applications for self-service analytics. It has many built-in collaboration features, such as the ability to push reports to mobile applications. While SAS Business Intelligence is a comprehensive and flexible platform, it can be more expensive than some of its competitors. Larger enterprises may find it worth the price due to its versatility.

Tableau is a data visualization and analytics platform that allows users to create reports and share them across desktop and mobile platforms, within a browser, or embedded in an application. It can run on the cloud or on-premises. Much of the Tableau platform runs on top of its core query language, VizQL. This translates drag-and-drop dashboard and visualization components into efficient back-end queries and minimizes the need for end-user performance optimizations. However, Tableau lacks support for advanced SQL queries.

Google Data Studio is a free dashboarding and data visualization tool that automatically integrates with most other Google applications, such as Google Analytics, Google Ads, and Google BigQuery. Thanks to its integration with other Google services, Data Studio is great for those who need to analyze their Google data. For instance, marketers can build dashboards for their Google Ads and Analytics data to better understand customer conversion and retention. Data Studio can work with data from a variety of other sources as well, provided that the data is first replicated to BigQuery using a data pipeline like Stitch.

Redash is a lightweight and cost-effective tool for querying data sources and building visualizations. The code is open source, and an affordable hosted version is available for organizations that want to get started fast. The core of Redash is the query editor, which provides a simple interface for writing queries, exploring schemas, and managing integrations. Query results are cached within Redash and users can schedule updates to run automatically.

Metabase is a free, open source analytics and business intelligence tool. Metabase allows users to "ask questions" about data, which is a way for nontechnical users to use a point-and-click interface for query construction. This works well for simple filtering and aggregations; more technical users can go straight to raw SQL for more complex analysis. Metabase also has the ability to push analytics results to external systems like Slack.

IBM Cognos is a business intelligence platform that features built-in AI tools to reveal insights hidden in data and explain them in plain English. Cognos also has automated data preparation tools to automatically cleanse and aggregate data sources, which allows for quickly integrating and experimenting with data sources for analysis.

Chartio is a self-service business intelligence system that integrates with various data warehouses and allows for easy import of files such as spreadsheets. Chartio has a unique visual representation of SQL that allows for point-and-click construction of queries, which lets business analysts who aren't familiar with SQL syntax modify and experiment with queries without having to dig into the language.

Mode is an analytics platform focused on giving data scientists an easy and iterative environment. It provides an interactive SQL editor and notebook environment for analysis, along with visualization and collaboration tools for less technical users. Mode has a unique data engine called Helix that streams data from external databases and stores it in memory to allow for fast and interactive analysis. It supports in-memory analysis of up to 10GB of data.

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