Various Methods of Data Collection

Professionals in all the business industries widely use research, whether it is education, medical, or manufacturing, etc. In order to perform a thorough research, you need to follow few suitable steps regarding data collection. Data collection services play an important role in performing research. Here data is gathered with appropriate medium.

Types of Data

Research could be divided in two basic techniques of collecting data, namely: Qualitative collection of data and quantitative collection. Qualitative data is descriptive in nature and it does not include statistics or numbers. Quantitative data is numerical and includes a lot of figures and numbers. They are classified depending on the methods of its collection and its characteristics. Data collected primarily by the researcher without depending on pre-researched data is called primary data. Interviews as well as questionnaires are generally found primary data/information collection techniques. Data collected from other means, other than by the researcher is secondary data. Company surveys and government census are examples of secondary collection of information.

Let us understand in detail the methods of qualitative data collection techniques in research.

Internet Data: Here there is a huge collection of data where one gets a huge amount of information for research. Researchers remember that they depend on reliable sources on the web for precise information.
Books and Guides: This traditional technique is authentically used in today's research.

Observational data: Data is gathered using observational skills. Here the data is collected by visiting the place and noting down details of all that the researcher observes which is needed for essential for his research.

Personal Interviews: Increases authenticity of data as it helps to collect first hand information. It does not serve fruitful when a big number of people are to be interviewed.

Questionnaires: Serves best when questioning a particular class. A questionnaire is prepared by the researcher as per the need of data-collection and forwarded to responders.

Group Discussions: A technique of collecting data where the researcher notes down details of what people in a group has to think. He comes to a conclusion depending on the group discussion that involves debate on topics of research.

Use of experiments: To obtain the complete understanding researchers conduct real experiments in the field used mainly in manufacturing and science. It is used to obtain an in-depth understanding of the researching subject.

Data collection services use many techniques including the above mentioned for collection. These techniques are helpful to the researcher in drawing conceptual and statistical conclusions. In order to obtain precise data researchers combine two or more of the data collection techniques.


Article Source: http://EzineArticles.com/5906957

How Easily Can You Extract Data From Web

With tech advancements taking the entire world by a storm, every sector is undergoing massive transformations. As far as the business arena is concerned, the rise of big data and data analytic is playing a crucial part in operations. Big data and data analysis is the best way to identify customer interests. Businesses can gain crystal clear insights into consumers’ preferences, choices, and purchase behaviours, and that’s what leads to unmatched business success. So, it’s here that we come across a crucial question. How do enterprises and organizations leverage data to gain crucial insights into consumer preferences? Well, data extraction and mining are the two significant processes in this context. Let’s take a look at what data extraction means as a process.

Decoding data extraction

Businesses across the globe are trying their best to retrieve crucial data. But, what is it that’s helping them do that? It’s here that the concept of data extraction comes into the picture. Let’s begin with a functional definition of this concept. According to formal definitions, ‘data extraction’ refers to the retrieval of crucial information through crawling and indexing. The sources of this extraction are mostly poorly-structured or unstructured data sets. Data extraction can prove to be highly beneficial if done in the right way. With the increasing shift towards online operations, extracting data from the web has become highly important.

The emergence of ‘scraping’

The act of information or data retrieval gets a unique name, and that’s what we call ‘data scraping.’ You might have already decided to pull data from 3rd party websites. If that’s what it is, then it’s high time to embark on the project. Most of the extractors will begin by checking the presence of APIs. However, they might be unaware of a crucial and unique option in this context.

Automatic data support

Every website lends virtual support to a structured data source, and that too by default. You can pull out or retrieve highly relevant data directly from the HTML. The process is termed as ‘web scraping’ and can ensure numerous benefits for you. Let’s check out how web scraping is useful and awesome.

Any content you view is ready for scraping

All of us download various stuff throughout the day. Whether it is music, important documents or images, downloads seem to be regular affairs. When you are successful in downloading any particular content of a page, it means the website offers unrestricted access to your browser. It won’t take long for you to understand that the content is programmatically accessible too. On that note, it’s high time to work out effective reasons that define the importance of web scraping. Before opting for RSS feeds, APIs, or other conventional data extraction methods, you should assess the benefits of web scraping. Here’s what you need to know in this context.

Website vs. APIs: Who’s the winner?

Site owners are more concerned about their public-facing or official websites than the structured data feeds. APIs can change, and feeds can shift without prior notifications. The breakdown of Twitter’s developer ecosystem is a crucial example for this.

So, what are the reasons for this downfall?

At times, these errors are deliberate. However, the crucial reasons are something else. Most of the enterprises are completely unaware of their structured data and information. Even if the data gets damaged, altered, or mangled, there’s no one to care about it.

However, that isn’t what happens with the website. When an official website stops functioning or delivers poor performance, the consequences are direct and in-your-face. Quite naturally, developers and site owners decide to fix it almost instantaneously.

Zero-rate limiting

Rate-limiting doesn’t exist for public websites. Although it’s imperative to build defences against access automation, most of the enterprises don’t care to do that. It’s only done if there are captchas on signups. If you aren’t making repeated requests, there are no possibilities of you being considered as a DDOS attack.

In-your-face data

Web scraping is perhaps the best way to gain access to crucial data. The desired data sets are already there, and you won’t have to rely on APIs or other data sources for gaining access. All you need to do is browse the site and find out the most appropriate data. Identifying and figuring out the basic data patterns will help you to a great extent.

Unknown and Anonymous access

You might want to gather information or collect data secretly. Simply put, you might wish to keep the entire process highly confidential. APIs will demand registrations and give you a key, which is the most important part of sending requests. With HTTP requests, you can stay secure and keep the process confidential, as the only aspects exposed are your site cookies and IP address. These are some of the reasons explaining the benefits of web scraping. Once you are through with these points, it’s high time to master the art of scraping.

Getting started with data extraction

If you are already eager to grab data, it’s high time you work on the blueprints for the project. Surprised? Well, data scraping or rather web data scraping requires in-depth analysis along with a bit of upfront work. While documentations are available with APIs, that’s not the case with HTTP requests. Be patient and innovative, as that will help you throughout the project.

2. Data fetching

Begin the process by looking for the URL and knowing the endpoints. Here are some of the pointers worth considering:

- Organized information: You must have an idea of the kind of information you want. If you wish to have it in an organized manner, rely on the navigation offered by the site. Track the changes in the site URL while you click through sections and sub-sections.
- Search functionality: Websites with search functionality will make your job easier than ever. You can keep on typing some of the useful terms or keywords based on your search. While doing so, keep track of URL changes.
- Removing unnecessary parameters: When it comes to looking for crucial information, the GET parameter plays a vital role. Try looking for unnecessary and undesired GET parameters in the URL, and removing them from the URL. Keep the ones that’ll help you load the data.

2. Pagination comes next

While looking for data, you might have to scroll down and move to subsequent pages. Once you click to Page 2, ‘offset=parameter’ gets added to the selected URL. Now, what is this function all about? The ‘offset=parameter’ function can represent either the number of features on the page or the page-numbering itself. The function will help you perform multiple iterations until you attain the “end of data” status.

Trying out AJAX

Most of the people nurture certain misconceptions about data scraping. While they think that AJAX makes their job tougher than ever, it’s actually the opposite. Sites utilising AJAX for data-loading ensures smooth data scraping. The time isn’t far away when AJAX will return along with JavaScript. Pulling up the ‘Network’ tab in Firebug or Web Inspector will be the best thing to do in this context. With these tips in mind, you will have the opportunity to get crucial data or information from the server. You need to extract the information and get it out of the page markup, which is the most difficult or tricky part of the process.

Unstructured data issues

When it comes to dealing with unstructured data, you will need to keep certain crucial aspects in mind. As stated earlier, pulling out the data from page markups is a highly critical task. Here’s how you can do it:

1. Utilising the CSS hooks

According to numerous web designers, the CSS hooks happen to be the best resources for puling data. Since it doesn’t involve numerous classes, CSS hooks offer straightforward data scraping.

2. Good HTML Parsing

Having a good HTML library will help you in ways more than one. With the help of a functional and dynamic HTML parsing library, you can create several iterations as and when you wish to.
Knowing the loopholes

Web scraping won’t be an easy affair. However, it won’t be a hard nut to crack either. While knowing the crucial web scraping tips is necessary, it’s also imperative to get an idea of the traps. If you have been thinking about it, we have something for you!

- Login contents: Contents that require you to login might prove to be potential traps. It reveals your identity and wreaks havoc on your project’s confidentiality.

- Rate limiting: Rate limiting can affect your scraping needs both positively and negatively, and that entirely depends on the application you are working on.

Source:-https://www.promptcloud.com/blog/how-easy-is-data-extraction

Data Collection Techniques for a Successful Thesis

Irrespective of the grade of the topic and the subject of research you have chosen, basic requirement and process of all remains same i.e. "research". Re-search in itself means searching on a searched content and this involves some proven fact along with some practical figures reflecting the authenticity and reliability of the study. These facts and figures which are required to prove the fundamentals of study are known as "data's".

These data's are collected according to the demand of research topic and its study undertaken. Also their collection techniques vary along with the topic in detail for example if the topic is like "Changing era of HR policies", the demanded data would be subjective and its technique thus depends on the same. Whereas if the topic is like "Causes of performance appraisal", then the demanded data would be objective and in the terms of figures which shows different parameters, reasons and factors affecting performance appraisal of different number of employees. So, let's have a broader look on the different data collection techniques which gives a reliable ground to your research -

• Primary Technique - Here, the data is collected by the first hand source directly are known as primary data's. Self-analysis is a sub classification of primary data collection - As understood; here you get self-response for a set of questions or a study. For example - personal in-depth interviews and questionnaires are self-analyzed data collection techniques, but its limitation lies in the fact that self-response can be sometimes biased or even confused. On the other, hand the advantage is in the court of most updated data as it is directly collected from the source.

• Secondary Technique - In this technique the data is collected from the pre-collected resources they are called as secondary data's. Data's are collected from articles, bulletins, annual reports, journals, published papers, government and non-government documents and case studies. Limitation of these is that they may not be the updated one or may be manipulated as it is not collected by the researcher itself.

Secondary data is easy to collect as they are pre-collected and are preferred when there is lack of time whereas primary data's are tough to amass. Thus, if researcher wants to bring up to date, reliable and factual data's they should prefer primary source of collection. But, these data collection techniques vary according to problem generated in the thesis. Hence, go through the demands of your thesis first before indulging yourself into data collection.

Source: http://ezinearticles.com/?Data-Collection-Techniques-for-a-Successful-Thesis&id=9178754

How Hedge Funds Can Use Web Scraping

How Hedge Funds Can Use Web Scraping

Web scraping or data extraction is the need of the hour to make sense of the huge and varied data being generated across multiple sources on the web. Irrespective of the sector you are working in, data extraction and mining is a crucial necessity to glean insights into consumer behavior, market forces, competitive intelligence, and price movements, and assist in management decision making.

There’s no denying the fact that numerous brands and enterprises are leveraging data extraction for further development and growth. Of late, hedge fund owners too are showing a huge affinity to utilizing the prowess of web scraping for unlocking new investment opportunities.

What we need to know is how web scraping is helping out hedge fund owners. What is it that makes web scraping essential for them and how can they use the technology to their advantage?
Fund management with web scraping

For a majority of discretionary fund managers, web scraping is a relatively new term. Although data scientists are aware of the concept, they might not have the right skills that lead to effective use of web scraping and data extraction. So, how does hedge fund management take place now? Let’s take a look at the current processes.

Most of the hedge funds have dedicated and centralized teams looking after the data extraction process. They have a group which is continuously looking for crucial data thus extracting it for more information. Once they find what they are looking for, they seek assistance from skilled data scientists who prepare comprehensive reports on the key findings. Based on these reports, managers have to take significant steps and implement crucial business strategies.

It’s here that the major problem arises. Most of these managers aren’t aware of the technicalities involved in data extraction. They don’t know what to do with these reports when it comes to devising business strategies.
The need for effective techniques

What you need is a comprehensive and integrated approach towards the entire process. Data scientists and business managers should have crystal clear understanding of web scraping thus working in tandem for better results. Here’s how they can work together:

1. Portfolio managers: PMs will need to develop a comprehensive understanding of trading strategies along with the power to explain his understandings. He should have the power to identify alpha opportunities.

2. Data scientists: Data scientists should know the art of data mining thus ingesting the findings into a database.

Simultaneous operations should take place where PMs, data scientists, and web scraping experts will take active parts. In a nutshell, business owners need highly efficient quant teams capable of extracting quant data sets.
The steps around web scraping for hedge funds

If you are managing hedge funds, data extraction and web scraping will be essential for you. Before knowing how to use this particular technique, make sure you gain information about the crucial steps that lead to web scraping.

•   Gaining access to data sets: Without the right data sets, it is impossible to perform web scraping. Data scientists and PMs must put their best efforts to find the correct information. It can come from internal divisions, external publications, or even from social media.

•   Understanding the financial drivers: You should know about the financial drivers involved in the process. Web scraping will depend on these key drivers to a great extent.

•   Quant vs. fundamental: There’s always a debate between data quants and fundamental knowledge. The prime emphasis should always be on identifying the insights, working on them, and turning them into effective actions.

With these steps in mind, you can plan the fund management process in detail thus taking the venture towards unsurpassed growth. Hedge fund owners have been relying on fundamental knowledge since a long time; it is high time they made a move and embraced web scraping.

Current positions and prospects

If market reports are anything to go by, you will come across nearly 70 hedge funds who claim to leverage big data. Once you take a closer look, the entire situation will get revealed. Only 20 amongst these 70 hedge funds work with Big Data and rely on web scraping techniques. Market reports also suggest that only a few of them are good at performing the process.

Web scraping is going to be the future! Just after a few years, hedge fund owners will have to rely on web scraping for effective fund management. Therefore, it’s high time to upgrade performances, processes, and operations. Those getting introduced to the concept for the first time should learn the art of performing web scraping and data extraction.

Building strong and effective financial models

Do you feel the existing infrastructure is enough to leverage web scraping? That’s not true, as there are numerous other aspects involved in the process. The presence of a strong and reliable financial model is of paramount significance. Financial models play a highly significant part in the utilization of technologies. If you are thinking of implementing web scraping, check the financial infrastructure and support your venture offers to you.

The third wave

Before the emergence of web scraping and data extraction, hedge fund owners relied on traditional data mining techniques. Those weren’t effective to a great extent, as they failed to offer targeted insights into the extraction process.

It’s here that the need for a third wave came up, and web scraping was what we all waited for. With this new and innovative technology, hedge fund managers will be able to utilize insights to stay ahead of the growth curve!

Final thoughts

Hedge fund management involves quite a few significant processes in order to yield the benefits expected by senior management of the company. However, if you are planning to use web scraping, it is important to know the right tips to do so. Most of the data scientists want to bridge the gap between fundamental fund management and web scraping. It is quite obvious that the latter is beneficial in the long run. With these tips and web scraping techniques in mind, you can ensure targeted hedge fund management and handling.

Source:https://www.promptcloud.com/blog/how-hedge-funds-can-use-web-scraping

What is the future of Data Scraping and the Structured Web?

Big Data has become a hot topic over the past year. What do you think the reason for this is?

I think this is obvious. It’s difficult to imagine today’s world without data. When I got involved in IT, a 10 MB hard drive seemed gigantic, and today, hard drives capable of storing terabytes of data are a standard! Besides, the largest “drive” today is the Internet that contains an immeasurable amount of data and expands at a mind-blowing speed. We just need to learn to separate seeds from the chaff, and that’s what big data technologies are all about.


Do you have any tips & tricks for people who want to turn unstructured data  structured data from the Web?

The thing is, this is still a fairly complex task. Products vary from “low-level”, where you need to be familiar with things like regex, xpath, css, http and such, to “high-level”, where all you need to do is to make clicks on the data you want to extract. The first type is usually more universal, but requires some technical skills. The second one works even for inexperienced users, but is often not efficient enough for solving more complex tasks. That’s why I truly appreciate the efforts made by import.io and similar services to find the golden mean.

What do you think the future is for the Structured Web, and web data.
There is no doubt that connections between data on the Internet will grow (remember, it once started with the good old hypertext), and the speed of this process depends on how commercially profitable it will be. However, I don’t think that the problem of data scraping will ever go away. Even if all websites eventually become structurally interconnected, there will always be a need to untangle this huge knot 🙂

Source url :-https://www.import.io/post/what-is-the-future-scraping-and-the-structured-web/

Six Tools to Make Data Scraping More Approachable

What is data scraping?

Data scraping is a technique in which a computer program/software extracts data from a website, so it can be used for other purposes.Scraping may sound a little intimidating, but with the help of scraping tools, the process can be a lot more approachable. The tools are used to capture data you need from specific web pages quicker and easier.

Let your computer do all the work

It takes only a few minutes for systems to recognize each others codes even in huge databases. Computers have their own language and that is why some of these tools make it easier to pull and format information in a way that is simpler for people to reuse.

Here is a list of some data scraping tools:

1.Diffbot

What makes this tool so likable is the business-friendly approach. Tools like Diffbot are perfect for searching through competitors work and the performance of your own webpage. Get product data from images, articles, discussions, web crawling tools and process websites. If you like how this sounds, see for yourself and sign up for their 14-day free trial.


2.Import.io

Import.io can help you easily get the information from the any source on the web. This tool can get your data in less than 30 seconds, depending on how complicated the data is and its structure in the website.  It can also be used for multiple URL scraping at once.

Here is one example: Which city of California based organizations try to hire the most through Linkedin? Check this list of jobs available in linkedin, download a csv file, sort from A to Z the cities and voila – San Francisco it is. Did you know that it’s for free?

3.Kimono

Kimono gives you easy access to APIs created for various web pages. No need to write any code or install any software to extract data. Simply paste the URL into the website or use a bookmark. Select how often you want the data to be collected and it saves it for you.

4.ScraperWiki

ScraperWiki gives you two choices – extract data from PDFs or build your own scraping tool in PHP, Ruby and Python language. It is meant for more experienced users and offers consulting (a paid service) if you need to learn some coding to get what you need. The first two PDF files are analyzed and reorganized for free, afterwards it’s a paid solution.

5.Grabz.it

Yes, Grabz.it does grab something. It takes information that is meaningful to you. The tool extracts data from the web, then converts videos into animated GIF that you can use on your website or application. This tool was made for those who code in ASP.NET, Java, JavaScript, Node.js, Perl, PHP, Python and Ruby languages.

6.Python

If programming is the language you love the most, then use Python to build your own scraping tool and get the data from a page you want to explore. It is particularly useful if the other tools don’t recognize the data you need.

If you haven’t used this tool before, follow this playlist of videos to learn how to use Python for web scraping:

If you want more tools, look into the Common Crawl organization. It is made for those who are interested in the data crawling world. Need a more specific tool? DMOZ and KDnuggets have lists of other tools for web data mining.

All of these tools extract information in spreadsheet formats and that is why this webinar about how to work with data in Excel can help you understand more about what to do if you desire  to supply the world with unique and beautifully data visualizations.



Source Url:-https://infogr.am/blog/six-tools-to-make-data-scraping-more-approachable/

Things to Consider when Evaluating Options for Web Data Extraction

Things to Consider when Evaluating Options for Web Data Extraction

Web data extraction possess tremendous applications in the business world. There are businesses that function solely based on data, others use it for business intelligence, competitor analysis and market research among other countless use cases. While everything is good with data, extracting massive data from the web is still a major roadblock for many companies, more so because they are not going through the optimal route. We decided to give you a detailed overview of different ways by which you can extract data from the web. This could help you make the final call while evaluating different options for web data extraction.

Different routes you can take to web data

Although different solutions exist for web data extraction, you should opt for the one that’s most suited for your requirement. These are the various options you can go with:

1. Build it in-house

2. DIY web scraping tool

3. Vertical-specific solution

4. Data-as-a-Service

1.   Build it in-house

If your company is technically rich, meaning you have a good technical team that can build and maintain a web scraping setup, it makes sense to build a crawler setup in-house. This option is more suitable for medium sized businesses with simpler requirements when it comes to data. However, building an in-house setup is not the biggest challenge- maintaining it is. Since web crawlers are really fragile and are vulnerable to the changes on target websites, you will have to dedicate time and labour into the maintenance of the in-house crawling setup.

Building your own in-house setup will not be easy if the number of websites you need to scrape are high or the websites aren’t using simple and traditional coding practices. If the target websites use complicated dynamic code, building your in-house setup becomes a bigger hurdle. This can hog your resources especially if extracting data from the web is not a competency of your business. Scaling up with your in-house crawling setup could also be a challenge as this would require high end resources, an extensive tech stack and a dedicated internal team. If your data needs are limited and the target websites simple, you can go ahead with an in-house crawling setup to cover your data needs.

Pros:

- Total ownership and control over the process
- Ideal for simpler requirements

2.   DIY scraping tools

If you don’t want to maintain a technical team that can build an in-house crawling setup and infrastructure, don’t worry. DIY scraping tools are exactly what you need. These tools usually require no technical knowledge as such and can be used by anyone who is good with the basics. They usually come with a visual interface where you can configure and deploy your web crawlers. The downside however, is that they are very limited in their capabilities and scale of operation. They are an ideal choice if you are just starting out with no budgets for data acquisition. DIY web scraping tools are usually priced very low and some are even free to use.

Maintenance would still be a challenge that you have to face with the DIY tools. As web crawlers are susceptible to becoming useless with minor changes in the target sites, you still have to maintain and adapt the tool from time to time. The good part is that it doesn’t require technically sound labour to handle them. Since the solution is readymade, you will also save the costs associated with building your own infrastructure for scraping.

With DIY tools, you will also be sacrificing on the data quality as these tools are not known for providing data in a ready to consume format. You will either have to employ an automated tool to check the data quality or do it manually. With these downsides apart, DIY tools can cater to simple and small scale data requirements. 

Pros:

- Full control over the process
- Prebuilt solution
- You can avail support for the tools
- Easier to configure and use

3.   Vertical-specific solution

You might be able find a data provider catering to only a specific industry vertical. If you could find one that has data for the industry that you are targeting, consider yourself lucky. Vertical specific data providers can give you data that is comprehensive in nature which improves the overall quality of the project. These solutions typically give you datasets that are already extracted and is ready to use.

The downside is the lack of customisation options. Since the provider is focusing on a specific industry vertical, their solution is less flexible to be altered depending on your specific requirements. They won’t let you add or remove data points and the data is given as is. It will be hard to find a vertical-specific solution that has data exactly the way you want. Another important thing to consider is that your competitors have access to the same data from these vertical-specific data providers. The data you get is hence less exclusive, but this may or may not be a deal breaker depending upon your requirement.

Pros:

- Comprehensive data from the industry
- Faster access to data
- No need to handle the complicated aspects of extraction

4.   Data as a service (DaaS)

Getting the required data from a DaaS provider is by far the best way to extract data from the web. With a data provider, you are completely relieved from the responsibility of crawler setup, maintenance and quality inspection of the data being extracted. Since these are companies specialised in data extraction with a pre-built infrastructure and dedicated team to handle it, they can provide this service to you at a much lower cost than what you’d incur with an in-house crawling setup.

In the case of a DaaS solution, all you have to do is provide them with your requirements like the data points, source websites, frequency of crawl, data format and the delivery methods. DaaS providers have high end infrastructure, resources and expert team to extract data from the web efficiently.

They will also have far superior knowledge in extracting data efficiently and at scale. With DaaS, you also have the comfort of getting data that’s free from noise and is formatted properly for compatibility. Since the data goes through quality inspections at their end, you can focus only on  applying data to your business. This can greatly reduce the workload on your data team and improve the efficiency.

Customisation and flexibility are other great advantages that come with a DaaS solution. Since these solutions are meant for the large enterprises, their offering is completely customisable for your exact requirements. If your requirement is large scale and recurring, it’s always best to go with a DaaS solution.

Pros:

- Completely customisable for your requirement
- Takes complete ownership of the process
- Quality checks to ensure high quality data
- Can handle dynamic and complicated websites
- More time to focus on your core business

Source:https://www.promptcloud.com/blog/choosing-a-data-extraction-service-provider