What Exactly Is Data?
The million-dollar question. In fact, a million dollars is represented as data ($1,000,000). Data is unprocessed information that, if properly identified, can be converted to useful output. Data has several different representations and sources such as:
- Observations: Things we identify from day-to-day activities.
- Questionnaires: This is a process of requesting information from a subject with the anticipation of reaching a meaningful conclusion.
- Interviews: Think of recruiters or journalists that are attempting to learn more about someone.
- Documents: Documentation stands the test of time because it is usually written for future reference.
- Tests: Tests are the result of trial and error and can be a valuable way to represent proof of concepts.
Data is everywhere! Anything that serves the purpose of informing people (and machines) is outputting data. Checking the time, reading a text message, viewing a traffic sign, or monitoring heart rate on a smartwatch. We are at a point in time where professionals in the industry of Data Science are in peak demand, and this corresponds to the increase in technology usage.
When working with data or planning a data management process, it is important to consider the following questions.
- What types of data are most appropriate to answer the questions? This question explores the various forms of data and if planning a database, suggestions about whether a database should be relational or non-relational may arise.
- How much data is necessary? This involves considering whether to set a limit or quota on the total amount.
- Who will do the collection? A question that addresses what type of device to utilize to acquire data (web forms, humans, or physical data collectors such as sensors).
- When and where will the data be collected? A strategic decision as to the best place to maximize the quantity of collected data.
- How will the data be compiled and later stored? The truth is collecting data can be messy considering the inconsistencies of data. Data cleaning, a process of organizing data, may become necessary in the event that such a problem occurs.
Data is classified in two different ways considering the questions above. Qualitative and Quantitative. Qualitative data is represented either in a verbal or narrative format. These types of data are collected through interviews, open-ended questionnaires, or other less structured scenarios but usually in human-interpreted words. On the other hand, quantitative data is data that is expressed in numerical terms. The numeric values may correspond to a specific category or label.
An example of qualitative data: The quick big brown fox jumped over the lazy fox.
An example of quantitative data: Products In Stock: 6,988.
I think you can agree that data can be collected, processed, and stored. One other action we can perform on our data is analysis. Data analysis is the process of scrutinizing a collection of information in order to provide useful insights. Below I have listed some data analysis tools that I have used as well as the advantages of each.
Microsoft Excel
Firstly, I will talk about Excel. The age-old tabular data representation that is still relevant nowadays. Microsoft Excel has several tools within itself to analyze data sets. The first one is its default output which is a table that can have a limitless number of rows and columns. Excel has pivot tables as well as several types of charts for displaying data in a relevant format. If you are like me and you like to convert data sets to comma-separated values (CSV), then most likely your program of choice for opening them is Excel.
Power BI
Another data analysis tool from Microsoft. Power BI is a great tool that has many features specifically for visualizing data. Analysts can import data from over 20 different sources and create dashboards to their liking. It has a plethora of chart types to choose from as well as the capability to transform imported data sets manually or using its in-built programming language DAX. DAX stands for Data Analysis Expressions. If Power BI is installed on your computer, you can access Power Pivot inside Excel as well. Power Pivot is a data modeling feature.
Power BI is accessible on a desktop or online through the Power BI Service. When logged in with a verified account, users can sync their workspace between the desktop version and the web service. Some sources that Power BI can pull data from are:
- SQL Server (Works quite well from my experience especially as a data warehouse).
- JSON.
- Web (Which scrapes HTML web pages).
- Microsoft Access.
- Excel.
- Azure SQL database.
- ActiveDirectory.
- MariaDB.
- Oracle.
- Amazon Redshift.
Google Analytics
Google Analytics is an app that is a part of the Google Workspace suite. It is usable inside the browser only. I personally use this tool to monitor traffic on my website, I find this is a fun way to analyze user activity and this serves as feedback to me as to a course of action. For example, less users in month 2 compared to month 1 indicates that my content is not engaging enough.
Google Analytics can also be used to monitor mobile app performance. The user interface is displayed as an admin dashboard with controls to add new apps to monitor and even a cool feature that suggests analysis questions based on the data you have.
D3.js
D3.js is a JavaScript library for producing dynamic, interactive data visualizations in web browsers. It is an abbreviation for Data-Driven Documents and is executed the same way the JavaScript language is interpreted. I discovered this tool while pursuing freeCodeCamps Data Visualization certification. I have not yet fully explored the capabilities of D3, but with more experience, I would like to use it to represent data in web apps. The degree of freedom that it provides allows one to design charts and visuals extensively. It makes use of HTML5, Cascading Stylesheets, and Scalable Vector Graphics.
That is an exhaustive list of data analysis tools I've used thus far and I encourage anyone to try them if you're looking for a platform to do so. If you haven't already, read my article about HTML. If you are more of a backend nerd, here's an article I wrote about SQL Database Administration. I hope you've enjoyed reading this one and understand that this is completely subjective.
Let me know what you think via this link.
Other freeCodeCamp.org courses worth exploring:
