Learning at par Industry-standards with Industry experts made my Career Transition possible
Education: BE in Electrical
Previous Profile Company: CGI Profile: Software Engineer (Mainframe) Project: John Hancock (Manulife) Insurance Services Domain: Insurance Location: India
Current Profile Company: CGI Profile: Lead Business Analyst Project: ERP and BI Techno-functional SME Domain: Finance and HRM Location: India
My journey into Data Science
Why Data Science?
Early on in my career, I learnt that the IT industry is constantly upgrading. One of my projects was shut down because the client wanted to upgrade to newer tech, in another company the entire project-team was asked to shift or upgrade with the latest tech. Since then, upgrading with the industry has been my core mantra to stay ahead with the times. After 3 years in IT as a Mainframe developer, I switched to Data Warehousing. After that, progression to Data Science was the natural course.
I checked a lot of courses on other portals like Coursera and Udemy etc. But I knew that what I needed was a more personalized and interactive course that would give me practical knowledge. My colleague did a course through Dimensionless Techademy. He described the course and frankly, I found it hard to trust him since other institutes with similar features were priced comparatively high. After a lot of research and contemplation, I decided to take a Demo class. Looking at the teaching methodology and course content, by the end of the demo I was convinced to join them.
Experience with Dimensionless?
Teachers are highly qualified and industry-experienced
These were all met at Dimensionless, so I am completely satisfied. All these features are hard to find in any other online courses. The teachers then became my mentors and still help me even after the completion of my course.
I am planning to do the AI specialization course with them next.
Career Transition to Data Science
From a Mainframe Developer in Insurance domain to a Lead Business Analyst in ERP and BI domain, and now entering into the Data Science and Advanced Analytics field, my career has taken a complete 360-degree turn. I am applying whatever I learnt to my work in real-time. Apart from theory and practical hands-on, industry case-studies and domain-related use-cases helped me a lot. I’ve finally cleared an interview for an internal-shift (At the time of this interview.)
Imagine every living and non-living entity hooked to the internet, generating bits of information as long as connectivity is maintained. This generation of small bits is a vital sign to deduce if the entity is active or inactive in the wide global network called the Internet. All these vitals are recorded and stored which makes the Internet an easily accessible hub hosting an overwhelming volume of data, generated with immense speed with every passing moment. This data can be extracted to study recurring patterns and trends in order to help in the deduction of advanced insights and useful predictions in any given domain as long as the data is relevant.
This is where the concept of Data Scraping or Web scraping crawls in and demands well deserved attention.
What is Data Scraping?
Data Scraping is the act of automating the process of extracting information from a source of unstructured data like websites, tables, visual and even audio sources in order to restructure them and make them ingestible for machine learning systems. These systems then absorb the structured data, analyze them, and provide intelligent insights on the same.
Previously, data scraping was not a very popular skill since the internet was in its adolescent phase and there were seldom any innovations or undergoing research which suggested ways to utilize such unstructured data. However, with the evolution of technology and especially machine learning and data science over the last two decades, the internet has become almost equivalent to oil fields lying around in the Arabian subcontinent! Literally.
The volume of data which is generated by the global network of the internet is extremely overwhelming and concerns almost all major and minor sectors which run our modern world. If we leave this data lying around in its dormant state just for the eyes of people and deprive machines of the same, it will not only be an unfair use of vast expanses of storage units but also drain humans of highly promising opportunities in the near future. Major industries seem to have grasped this fact and are putting out job openings for people who happen to have an experience with data scraping and keep these coveted skills at hand.
Why is Data Scraping a Desirable Add On to a Data Scientist?
When a Data Scientist is armed with web scrapping skills, he/she can easily evade data blockers. For instance, if the data provided by a client is insufficient, the first step a competent data scientist can take is to look for relevant websites under the same domain and check if it is possible to retrieve valuable data from these websites. If the required data is not found, only then the client could be approached for further data. The latter process will extend the timelines unnecessarily and also prevent the client from having a smooth experience. Also, if the client again provides insufficient data, another similar loop will be generated and time will be extended further. On the other hand, the former process promises higher value addition both in terms of data (since the internet is usually loaded with rich data) and client experience.
Furthermore, one can assume that a Data Scientist possesses decent programming skills. With such skills, she/he can easily make use of the following:
Scraper is written from scratch
Web scraping libraries
When data scraper code is written from scratch, there is the flexibility of extreme customization. When web scraping libraries are used, which are available in abundance, a decent programmer can appropriately tweak the library code based on the domain data in order to optimize the results.
With good programming knowledge, even the following vital points can be taken care of:
Optimum Network Configuration
If data scraping skills are missing in hired individuals, ambitious firms, who plan on handling large scale client data, will have to take the aid of Data Service Companies which provide services in the field of Data Handling and Machine Learning. However, if these firms hire a handful of Data Scientists/Engineers who are skilled in designing web scraping code or know how to tweak inbuilt data scraping libraries for optimum results, it will cost the firm much less in terms of investment on data gathering. With Data Scraping, it is extremely easy to impute missing data with the latest information without declaring that the data in use is irrelevant altogether. For instance, if there are a hundred records concerning the population of different countries and every feature is available other than the historical population data, one can easily scrap the web for year wise population of a given country and fill in the relevant details with one piece of code.
Acquiring data scraping skills will, no doubt, increase an applicant’s overall value on a relative scale.
How Relevant is Data Scraping in the Present World?
When data is extracted through web scraping techniques, real-time data is added to your existing database. This helps to track current trends and also provides real-life service-based data for research purpose. When a firm chooses to enable their product, the system will have to process and analyze real-world data in every instance. Scraped data provides the environment to the machine for learning from realistic information and helps it to be on par with real-time trends and patterns.
This also comes to great use when firms need to monitor their implemented products and take up audience review and feedback from multiple sources. Scraping information directly can provide the firm with a generic idea of the product’s performance and can also help in suggesting ways of improvement.
So, What are the Most Useful Coding Languages for Data Scraping?
While choosing a coding language it is important to keep in mind the features of the language under use. It must satisfy important criteria like flexibility, scaling, maintainability, database integration and ease of use. Even though the speed/efficiency of data scraping is more dependent on the speed of your network and less on your code optimization, it is still advisable to prefer optimizations anytime. Here are a few coding languages which provide efficient data scraping libraries and are easy to implement:
Python is an excellent language to implement data scraping and is in fact, the most recommended. It provides a score of libraries like Beautiful Soup and Scrapy for easy data extraction and takes care of format and scaling issues. People with minimum knowledge on programming can also implement these on a fundamental scale.
C and C++
Both these languages are high-performance object-oriented languages. This means that it is possible to optimize code heavily using these languages. However, the cost to develop such code is extremely high compared to other languages as it requires extreme code specialization.
Node.js is good for small scale projects and is especially recommended for crawling dynamic websites. However, the communications in node.js face instability issues and it is recommended to not use it for large scale projects.
Even though it is possible to implement data scraping using PHP, it is the least recommended language to do so. This is because PHP lacks support for multi-threading which can, in turn, lead to complicated issues during code runs.
All this being said, it is important to understand that coding languages are just the tools to reach a desired goal. If you are comfortable with a certain language, it will be advisable to learn data scraping techniques in that very language as it provides an upper hand to you because of your existing command over the language.
What is the Intensity of Demand for Data Scraping as a Skill?
According to research conducted by KDnuggets.com on the professional network of LinkedIn, it was found that 54 industries require Web Scraping Specialists! The top five sectors included the industries: Computer Software, Information Technology and Services, the Financial sector, Internet domain and finally the Marketing and Advertising industry. It was even found that non-technical jobs also had a high demand for data scraping specialists. This must not come as a shock to anybody since the relevance of data has upgraded to such a level over the last decade that the industries are trying to brace themselves from future impacts with as much data as possible. Data has indeed become the golden key for all modern industries to a secure and profitable future. One needs to master the right skills to master the age of data we live in today.
As is certain from the above discussion, we can say without much doubt that Data Scraping skills have definitely become one of the most sought after and coveted skills of the 21st century. It is recommended to not only aspiring data scientists but also technical professionals to have such skills handy since it only leads to value addition for both the employing firm and the employed individual.
If you are interested in learning core Data Science and subsidiary skills which come along with it, these links can help you with the same: