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In today’s data-driven world, it is crucial to have access to reliable and relevant data for informed decision-making. Often, data from external sources is obtained through processes like pulling or pushing from data providers and subsequently stored in a data lake. This marks the beginning of a data preparation journey where various techniques are applied to clean, transform, and apply business rules to the data. Ultimately, this prepared data serves as the foundation for Business Intelligence (BI) or AI applications, tailored to meet individual business requirements. Join me as we dive into the world of data scraping with Octoparse and discover its potential in enhancing data-driven insights.

This article was published as a part of the Data Science Blogathon.

Web Scrapping and Analytics

Yes! In some cases, we have e to grab the data from an external source using Web Scraping techniques and do all data torturing on top of the data to find the insight of the data with techniques.

Same time we do not forget to use to find the relationship and correlation between features and expand the other opportunities to explore further by applying mathematics, statistics, and visualisation techniques, on top of selecting and using machine learning algorithms and finding the prediction/classification/clustering to improve the business opportunities and prospects, this is a tremendous journey.

Focusing on excellent data collection from the right resource is the critical success of a data platform project. I hope you know that. In this article, let’s try to understand the process of gaining data using scraping techniques – zero code.

Before getting into this, I will try to understand a few things better.

Data Providers

As I mentioned earlier, the Data Sources for DS/DA could be in from any data source. Here, our focus is on Web-Scraping processes.

What is Web-Scraping and Why?

Web-Scraping is the process of extracting data in diverse volumes in a specific format from a website(s) in the form of slice and dice for Data Analytics and Data Science standpoint and file formats depending on the business requirements. It would .csv, JSON, .xlsx,.xml, etc.. Sometimes we can store the data directly into the database.

Why Web-Scraping?

Web-Scraping is critical to the process; it allows quick and economical extraction of data from different sources, followed by diverse data processing techniques to gather the insights directed to understand the business better and keep track of the brand and reputation of a company to align with legal limits.

Web Scraping Process RequestVsResponse

The first step is to request the target website(s) for the specific contents of a particular URL, which returns the data in a specific format mentioned in the programming language (or) script.

Parsing&Extraction

As we know, Parsing is usually applied to programming languages (Java..Net, Python, etc.). It is the structured process of taking the code in the form of text and producing a structured output in understandable ways.

Data-Downloading

The last part of scrapping is where you can download and save the data in CSV, JSON format or a database. We can use this file as input for Data Analytics and Data Science perspective.

There are multiple Web Scraping tools/software available in the market, and let’s look at a few of them.

In the market, many Web-Scraping tools are available, and let’s review a few of them.

ProWebScraper Features

Completely effortless exercise

It can be used by anyone can who knows how to browse

It can scrape Texts, Table data, Links, Images, Numbers and Key-Value Pairs.

It can scrape multiple pages.

It can be scheduled based on the demand (Hourly, Daily, Weekly, etc.)

Highly scalable, it can run multiple scrapers simultaneously and thousands of pages.

Let’s focus on Octoparse,

The web-Data Extraction tool, Octoparse, stands out from other devices in the market. You can extract the required data without coding, scrape data with modern visual design, and automatically scrapes the data from the website(s) along with the SaaS Web-Data platform feature.

Octoparse provides ready-to-use Scraping templates for different purposes, including Amazon, eBay, Twitter, Instagram, Facebook, BestBuy and many more. It lets us tailor the scraper according to our requirements specific.

Compared with other tools available in the market, it is beneficial at the organisational level with massive Web- Scraping demands. We can use this for multiple industries like e-commerce, travel, investment, social, crypto-currency, marketing, real estate etc.

Features

Both categories could find it easy to use this to extract information from websites.

ZERO code experience is fantastic.

Indeed, it makes life easier and faster to get data from websites without code and with simple configurations.

It can scrape the data from Text, Table, Web-Links, Listing-pages and images.

It can download the data in CSV and Excel formats from multiple pages.

It can be scheduled based on the demand (Hourly, Daily, Weekly, etc.)

Excellent API integration feature, which delivers the data automatically to our systems.

Now time to Scrape eBay product information using Octoparse.

Getting product information from eBay, Let’s open the eBay and select/search for a product, and copy the URL

In a few steps, we were able to complete the entire process.

Open the target webpage

Creating a workflow

Scrapping the content from the specified web pages.

Customizing and validating the data using review future

Extract the data using workflow

Scheduling

Open Target Webpage

Let’s login Octoparse, paste the URL and hit the start button; Octoparse starts auto-detect and pulls the details for you in a separate window.

Creating Workflow and New-Task

Wait until the search reaches 100% so that you will get data for your needs.

During the detection, Octoparse will select the critical elements for your convenience and save our time.

Note: To remove the cookies, please turn off the browser tag.

Scrapping the Content from the Identified Web-page

Once we confirm the detection, the Workflow template is ready for configurations and data preview at the bottom. There you can configure the column as convenient (Copy, Delete, Customize the column, etc.,)

Customizing and Validating the Data using Review Future

You can add your custom field(s) in the Data preview window, import and export the data, and remove duplicates.

Extract the Data using Workflow

On the Workflow window, based on your hit on each navigation, we could move around the web browser. – Go to the web page, Scroll Page, Loop Item, Extract Data, and you can add new steps.

We can configure time out, file format in JSON or NOT, Before and After the action is performed, and how often the action should perform. After the required configurations have been done, we could act and extract the data.

Save Configuration, and Run the Workflow

Schedule-task

You can run it on your device or in the cloud.

Data Extraction – Process starts

Data ready to Export

Chose the Data Format for Further Usage

Saving the Extracted Data

Extracted Data is Ready in the Specified-format

Data is ready for further usage either in Data Analytics and Data Science

What is Next! Yes, no doubt about that, have to load in Jupiter notebook and start using the EDA process extensively.

Conclusion

Importance of Data Source

Data Science Lifecycle

What is Web Scrapping and Why

The process involved in Web Scrapping

Top Web Scraping tools and their overview

Octoparse Use case – Data Extraction from eBay

Data Extraction using Octoparse – detailed steps (Zero Code)

I have enjoyed this web-scraping tool and am impressed with its features; you can try and want it to extract free data for your Data Science & Analytics practice projects perspective.

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Using Data For Successful Multi

How to use data to optimise your multi-channel marketing

Multi-channel marketing provides businesses with the opportunity to engage with consumers across a variety of different fronts, tailoring messages for specific groups while maintaining a consistent message and brand. But it’s not simply a matter of sending your message blindly out into the ether – to achieve true success over multiple channels you need to make effective use of the data at your disposal.

This article demystifies this process, helping you understand what this data is, where you need to find it and what you need to do with it. As with all marketing, a little bit of considered thought at the start of the campaign makes a real difference in the result.

Tracking your data

This may seem obvious, but the actual obtaining of your data is the most important place to start and tracking a campaign is really the only way to determine whether your marketing efforts are having a positive effect on your business’s bottom line.

In the digital world, track conversion metrics broken down by target audience and geography. When offline, data should be tracked at the lowest level possible to ensure clarity and simplicity. Something else to consider is the manner in which this data will be stored. Marketing produces a high volume of data, and it’s important to ensure you will have an intuitive system for tracking and managing this information. It may even be an idea to outsource this aspect of the process, for simplicities sake.

Analyse your data

Now that you have your data, it’s important to understand what it’s telling you. Consider a consumer’s interaction with your brand as a path, from discovering the initial message to ultimately making a purchase/interacting with your service.

Discovering and acknowledging the channels different groups are using to interact with your brand is a good way to understand improvements you can make on a broader level, as well as some quick victories that can streamline processes.

A thorough data analysis is also a good way to gain a thorough understanding of the consumers who are interacting with your brand. Find out who the high value consumers are and determine ways in which you can enhance engagement. Also, consider the devices they are using in their interactions. You’re as good as your data in marketing, and a thorough analysis will ensure you get more bang for your buck.

Develop a strategy

Now that you’ve analysed your data, it’s time to decide how you’re going to respond to it. You’ve discovered the channels your consumers are responding to and the groups of consumers are of highest value, so now it’s time to maximise this and develop a message that will achieve results for your business.

There are a few things to consider in this process. For instance, it’s important to make sure the message that passes through to your customers across multiple channels is a consistent, effective one. It’s also important to make sure the consumer’s journey through different channels is as seamless as possible. An online clothes retailer may have people browsing on mobile devices during the day but only making the purchase when they get home, so keep this in mind at all times.

Respond through preferred channels

Now you’ve analysed your data and formulated an effective strategy, it’s time to bring it all together. Multi-channel marketing allows you to engage different groups of consumers with tailor-made messages, but as mentioned before, it’s important to ensure these messages are consistent with the overall identity of your brand.

Test, test, test

The most important part here is tracking your results, responding and testing. Look for different aspects of your campaign to test and make sure you integrate them into your planning. Think about different conversation metric variables and see how you can tinker with them to achieve different results. As with any marketing, it’s not likely you’re going to find the thing that works best with your first effort, so be flexible and willing to incorporate new ideas into your campaign. The world moves at a fast pace these days and if you’re not willing to keep up, it will be to the detriment of your multi-channel marketing campaign. Testing and a degree of flexibility in your approach allows you to keep track of what is and isn’t working and stay ahead of the curve.

Multi-channel marketing is one of the most effective ways to engage with consumers in 2024. But it’s important to do it correctly. Track your data, analyse your data and develop a strategy that allows you to respond effectively in the appropriate channels. And once you’ve done this test, test and test some more! A proactive approach can achieve serious results for your business, allowing you to maintain a consistent message across multiple platforms and maximise the yield from your consumers.

At the end of the day, multi-channel marketing is about getting as much bang for your buck as possible. An acute awareness of what your data is telling you and how to respond will help your business grow and separate you from the rest of the pack.

Web Scraping Vs Data Mining: Why The Confusion?

Web scraping is the process of scanning the text or multimedia content from targeted websites and turning this content into data table that can be analyzed. So essentially, web scraping is a form of data extraction. It does not generate any business insights before the collected data is cleaned, formatted and analyzed.

Just like the example we mentioned, a very common use case web scraping enables data mining is commercial data on e-commerce business owners or brands that provide an online shop. Web scraping tools can collect the product definition, reviews, price, features, stock status, colors, ratings, and many other information that can generate insight for businesses. Apart from good and products, web scraping can also collect service information such as flight fares, ticket prices and freelancer fees across all the websites you target.

Natural language processing as a data mining method has transformed text data into a valuable asset. Web scraping is a fast and efficient way to collect written data on web. It can scrape entire articles, tables and images on the articles as well as links that are embedded in these articles. It can target exact websites or top search engine results that appear for a certain keyword.

In one second, there are more than 9000 tweets on Twitter and 1000 Instagram posts on Instagram on average. Depending on what your industry you are in, a significant amount of this big and increasing content can be relevant to your business. Web scraping can target certain keywords and hashtags that are important to your business into the data of what people said online. This data can reveal whether there is more activity on social media for your competitors, whether your consumers mention negative or positive words about your product, and many other insights about new trends emerging.

If you already have existing data mining processes supporting your business decisions or plan to use new methods, you can access free data sources scraped from the web to see whether any of the use cases we mentioned above can be beneficial for your business. Keep in mind that if you decide using web scraping on a continuous basis, you need to consider all the benefits and challenges of collecting data from the web before making a decision on whether you’d like to build such a capability in-house or leverage an external provider.

Sponsored:

One way to find free data sets that may be more suitable for commercial use cases can by getting a sample from Bright Data. They already have readily available datasets that are up-to-date and collected for specific use cases which may help you run proof of concept analysis and decide whether web scraping is a useful tool for your business.

Recommended Reading:

To explore web scraping use cases for different industries, its benefits, and challenges read our articles:

If you want to have more in-depth knowledge about web scraping, download our whitepaper:

If you believe that your business may benefit from a web scraping solution, check our list of web crawlers to find the best vendor for you.

For guidance to choose the right tool, reach out to us:

This article was drafted by former AIMultiple industry analyst Bengüsu Özcan.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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Goodbye Data Teams, Welcome Data Product Teams To The Commercial Circle

Building more data product teams than data teams to improve collaborative teamwork

It was suggested by Emilie Schario and Taylor Murphy last year to “manage your data team like a product team.” The article’s thesis was that data teams would benefit from adopting many of the excellent techniques now used by product teams. The product team is led by a product manager.

Somewhere along the line, we lost sight of this and cheerfully replaced it with strawmen, like building data teams, maintaining production-grade systems for our data assets, or rigorously defining what production means in the service of hardening data contracts. While each of these is undoubtedly important, they are more focused on the correct management of data and data assets than on the data product teams who generate the impact.  The main goal of this article was not to debate the parameters and definition of a “Data Product” or to impose SLAs on data producers, but rather to force them.

Let’s talk about the specifics of running your data team like a product team. User-centricity and proactivity are the two main concepts that product teams uphold. Each will be discussed in turn.

User-Centricity

User-centric product teams are the finest. They regularly communicate with their customers and allow direct user feedback to immediately affect their roadmap. Any successful product relies on this flywheel to ensure that it is solving problems as well as providing features.

The same methods must be used by data teams. We’ve become too infatuated with how technically fascinating our work can be, and we’ve forgotten that we are a business unit hired to deliver economic value, not a solitary haven for scientific or engineering interests. Additionally, our metaphorical “data product”—all of our data work—fails if we, like product teams, do not use data to solve business challenges.

This does not include acting impulsively in response to requests. This does not entail avoiding all scientific activities, either. It just entails remaining aware of the company’s requirements and seeking out possibilities to further those goals. Taylor and Emilie argue that your coworkers are your customers; nevertheless, we believe that this is not sufficient and that your true customer is the company. You must be aware of it, comprehend it, and base all you do on it.

Proactivity

Second, the top product teams have proactive procedures in place to aid in the creation of the products. They intentionally give themselves room to form the vision, conceive ideas, and work on passion projects that fall beyond the purview of taking on direct client demands.

On the other hand, analytics teams rarely work this way. We should at the very least spend some time investigating the data independently of incoming queries. We should also be on the lookout for trends at the team level so that we can deliberately design our roadmap and complete high-value tasks.

Despite this, reactive work is still important because analysts are the company’s main tool for data exploration, therefore we frequently find ourselves playing a supporting role. However, the secret is to always strive to comprehend the context of this work and to allow this context to inspire smart, high-impact projects.

What is the Structure of the Product Team?

Because it enables the business to distribute duties and responsibilities as effectively as possible, an effective team structure is essential. Teams can be created in a variety of ways that have proven successful for other businesses.

Product teams require a strong framework and well-defined roles. But it all begins with a crystal-clear product vision and a transparent product strategy that lays out shared objectives. All team members will be aware of what they are working on and what the group will produce as a result of the project.

How can a Product Team be Formed?

When assembling a product team, be aware that its composition will evolve to project requirements.

And be ready to respond to the following inquiries from time to time:

How many items should be controlled?

Which (s) have a higher priority in terms of needs for development and revenue generation?

How complicated are these products?

Which stage of a product’s lifetime is it in?

Analyzing Data Made Effortless Using Chatgpt

Introduction

To learn more about the development of generative models with hands-on experience, join us at the ‘Natural Language Processing using Generative Models’ Workshop at the DataHack Summit 2023. Attending DataHack Summit 2023 will be a game-changer for you. The workshops are designed to deliver immense value, empowering you with practical skills and real-world knowledge. With hands-on experience, you’ll gain the confidence to tackle data challenges head-on. Don’t miss out on this invaluable opportunity to enhance your expertise, connect with industry leaders, and unlock new career opportunities.

Why Prompts are Critical in ChatGPT?

I realized that prompts are very critical in order to make use of ChatGPT to its full potential. Even Though ChatGPT is capable of performing any task, in order to make use of it to its full extent, we need to provide the right and detailed prompts. Without the exact prompts, you will not be able to get the desired results.

I am running the experiment to see if ChatGPT can really make sense out of the dataset. I know that ChatGPT can provide me with the code snippets of certain tasks.

For example, given a prompt “help me with the code snippet to check for outliers”. ChatGPT provided me with a code snippet to check and identify the outliers. But can a ChatGPT help me answer the questions such as determining the columns that contain outliers in the dataset? or what is the correlation coefficient between the target variable and features?

In order to answer these questions, ChatGPT has to analyze the specific columns in the dataset and do the math to come up with the answer.

Fingers crossed!

But it’s really interesting to see if ChatGPT can do the math and provide me with the exact answers to the questions. Let’s see!

Exploratory Data Analysis (EDA) Using ChatGPT

Let’s try some of the prompts, EDA using ChatGPT:

Prompt 1:

I want you to act as a data scientist and analyze the dataset. Provide me with the exact and definitive answer for each question. Do not provide me with the code snippets for the questions. The dataset is provided below. Consider the given dataset for analysis. The first row of the dataset contains the header.

Prompt 2:

6,0,3,”Moran, Mr. James”,male,,0,0,330877,8.4583,,Q

Prompt 3:

How many rows and columns are present in the dataset?

Prompt 4:

List down the numerical and categorical columns

Prompt 5:

Check for NANs present in the dataset? If yes, print no. of nans in each column.

Prompt 6:

Are there any outliers in the dataset?

Prompt 7:

Name the columns that contain the outliers. Provide me with the exact answer.

Prompt 8:

What are the significant factors that affect the survival rate?

Prompt 9:

Determine the columns that follow the skewed distribution and name them.

Prompt 10:

Generate meaningful insights about the dataset.

Such cool stuff 🙂 As you can see here, ChatGPT provided me with a summary of valuable insights and also the important factors that might have affected the survival rate.

Conclusion

Impressive! ChatGPT is able to generate meaningful insights in no time. My experiment is successful. And ChatGPT lived up to my expectations.

To learn more about the development of generative models with hands-on experience, join us at the ‘Natural Language Processing using Generative Models’ Workshop at the DataHack Summit 2023. Attending DataHack Summit 2023 will be a game-changer for you. The workshops are designed to deliver immense value, empowering you with practical skills and real-world knowledge. With hands-on experience, you’ll gain the confidence to tackle data challenges head-on. Don’t miss out on this invaluable opportunity to enhance your expertise, connect with industry leaders, and unlock new career opportunities. Register now for the DataHack Summit 2023!

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Safeguarding Against Data Breaches Using Pam Solutions

Data breaches have unfortunately become a regular occurrence. High-profile instances of data theft involving companies like T-Mobile, the US Transportation Security Administration (TSA), Twitter, and others have been recently documented.

Often, breached companies are hesitant to disclose the specifics of such events, commonly attributing them to hacker attacks. However, evidence suggests that a significant number of these data breaches actually stem from privileged accounts within the victimized enterprises’ information systems. These accounts are highly sought after by cybercriminals.

Accounts with extensive system rights can access thousands of users’ confidential data, business information, and IT system configurations. Once criminals breach the security perimeter, they can remain undetected for months, waiting for the best moment to execute their attack.

The Security Risks Associated with Privileged Users

In the first scenario, cyber attackers manage to gain access to a privileged accounts. Essentially, this grants them a “master key” to the organization, making a targeted attack a matter of when not if. Whether used directly by hackers or sold on the dark web, the “username-password” pair becomes a serious corporate security breach once compromised. The only hope for preventing an attack is detecting the data leak early.

In the second scenario, the privileged users themselves – intentionally or unwittingly – become the perpetrator. There are many well-documented instances of this. The results of an everyday employee morphing into a cybercriminal can range from deliberate disruption of corporate systems to outright data theft. This is reminiscent of the numerous data leaks various services have experienced in the past.

Privilege Access Management Systems

To overcome the problems of controlling privileged accounts, there are specialized tools called PAM systems (Privilege Access Management). They allow you to prevent massive leaks of data, as well as control the use of passwords in an organization, allowing you to save on reputational costs. PAM systems solve four important tasks:

User privilege management. Extended rights are granted only to those users who have good reasons for this. In addition, access is not given to all resources but only to those that users really need to fulfill their work duties, and the validity period of privileges is strictly limited in time.

Monitoring the actions of privileged users. The system records user sessions and stores the data for further review. Advanced PAM solutions keep a log of work sessions and can recognize text (OCR function).

Password management. The system stores passwords in encrypted form, updates them, and does not give users access to secret information. Sometimes third-party password management solutions are used for this.

Support for pass-through authentication. It allows users not to enter a password to access each corporate service but “log in” only once with the help of Single Sign-On (SSO).

Who Needs RAM Systems?

PAM systems are applicable to organizations of all types, as every organization has privileged users. Not only individual users hold accounts with special rights, but also entities like business partners, contractors, companies that manage information systems, and third-party systems that interact with corporate systems without human intervention. Nearly every business stores data needing special protection, such as employees’ personal data and customers’ personal records. The applicability of PAM systems is virtually unlimited, regardless of the company’s size.

Implementing a PAM system

Installation and configuration of PAM systems are generally straightforward. They do not require intricate integrations and ensure compatibility with various systems. Typically, PAM is installed “over” other enterprise information systems and becomes a sort of “gateway” for all user access.

The implementation method varies depending on an organization’s competencies and its approach to IT infrastructure development. Some organizations utilize contractors’ services, maintaining an in-house team of administrators to oversee system operations. In contrast, others possess sufficient competence to implement a PAM system independently. This typically includes deploying server components, organizing log and data storage, and installing agents on protected infrastructure nodes.

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