The first step in the data science process and analytics is the data collection that determines the availability of data and what you need to collect.
In the Data Science Process data analytics, the first step is mostly the data collection or scraping from multiple web sources.
The data gathering process restrains the multiple ways like Identify the data sources and databases, Retrieving the data and Query data for analysis, etc.
It includes other similar ways also like web scraping, data gathering using API, manual collection, purchasing data, or automation on web sources for data collection.
The first step in data analysis is always be acquiring data that decides what data is available? and what is your business requirement?
The data collection process needs the right understanding of what you want and what you’re really collecting from the data source.
Following Important key point we need to keep in mind while doing the data collection:
- Data should be Correct
- Use of Multiple Technologies
- Scripting Languages for Collection
- Websites are the big Source of Data
- Web Services for Data Scraping
- NoSQL for Data Storage and Retrieval
- API for Data Collection
Top 7 Ways for Right Data Collection
1. Data Collection Need to be Correct
Always you need to be careful while finding the correct data sources for data collection because what you going to collect should be accurate for analysis.
In the data gathering process, you need to recognize the suitable data related to your business problem, if it is then extract the complete data.
Utilization of the whole collected data which is appropriate to your problem for the current analysis can help to know the data accuracy.
Sometimes, leaving out even a little quantity of relevant data can drive to wrong conclusions.
The collected data mostly comes from several places that can be local, remote, many varieties, structured and unstructured, and so on.
The correctness of data after gathering can depend on the different velocities of data gathering, which can mention the streaming velocity of the data.
2. Data Collection Using Multiple Technologies
The data on several platforms need specific techniques and technologies to obtain certain diverse forms of data.
A large amount of data comes from the organization that usually in conventional, relational databases, or structured form that needs specific technology to process.
To execute the data of relational database management systems needs SQL (Structure query language) that use for structured data.
Whereas the huge volume of data is available on the internet which is in the unstructured form and processing those types of data want certain kinds of technologies and tools.
Similarly, data collection highly do in the modern world using automated tools, bot, and scripting languages that process called web scraping.
3. Scripting Languages for Data Collection
The scripting programming languages are largely utilized for web scraping and automated data processing operations or collection.
The unstructured data highly exist in multimedia forms like images, videos, audios, text files, documents, excel spreadsheets, etc. that needs specific logic for gathering from the internet.
The Scripting languages commonly helpful for writing the exact logic for data scraping, storing, and formatting for analysis.
However, the other common scripting languages that support data gathering and processing tasks like PHP, R, MATLAB, Perl, and Octa, etc.
4. Websites are the Big Source of Data
Internet is the big source of data where websites are the small sections of it, and to get data is from websites for analysis is increasing extensively.
Websites are the rich sources of data because the webpages do write using a set of senders approved by a World Wide Web (www) consortium or W3C.
Websites carry a variety of formats and services, the common format for websites can be XML or JSON.
XML and JSON use markup symbols and tabs to describe the contents on a web page.
Websites and hosting platform services provide programmatic and API access of complete data which is available on webpages.
5. Web Services for Data Scraping or Collection
Several types of web services and securities possible on the internet, which makes data extraction very easy and The REST API is one of them.
A service like REST (Representational State Transfer) is an approach for performing the web services operation with performance scalability and maintainability.
Web services like WebSocket services are growing fast and becoming familiar on the web that enables Real-time notifications from the websites.
The service on the web is getting more necessary for automating big data extraction from complicated sources.
6. NoSQL for Storage and Retrieval of Data
The storage systems commonly use to manage a mixture of data types and unstructured data that exactly handle using a NoSQL database.
The NoSQL unable to represent data in a table format like rows and columns this is opposite to conventional relational databases.
The NoSQL systems present data access using a web interface, these interfaces further supports data extraction and automated collection.
It supports multiple databases for unstructured data forms like HBase, Cassandra, MongoDB for big data storage.
7. Data Collection using API
Websites and data sources provide APIs (Application programming interface) to enable users to obtain the data.
NoSQL databases system can support the API execution that provides an automated way for the data extraction directly to the cloud system.
APIs can apply directly or manually to the data scraping application for accessing the automated data to the local system.
On the other hand, python script can make automated scraping easy with help of APIs and standard scraping libraries.
Rest API is also a very significant way to get the data from different sources that carry efficient ways for web scraping or data acquisition process to NoSQL databases.
Data usually comes from numerous places and it can be different depending on the source and structure of data.
As per the above-detailed point, you got that the data collection process needs a deep understanding of several factors and using certain ways you can access the data.
Obtaining and evaluating all the associated data throughout the data collection process is significant for accurate analysis.
Analytics Teams working on creating useful content related to Data Science, analytics, and AI. It is a team of skilled data Scientists and Analysts, some works full time and some are part-time.