As the world becomes more and more connected, people have accumulated more and more data. Interpreting, storing and handling these information has become so complex that experts decided we need a new field to study and apply strategies on managing a really large data storage and analyzing unstructured (words, image, sound, etc.) information. This was initially the objective of data science but seeing as understanding this complex information provided the basics of “self-optimizing” machines, data science is now usually related to the terms machine learning and artificial intelligence.
I started out learning about data science by taking online courses. When I decided to further my career in this field, I quit my previous job to focus on learning and embracing my newfound calling. Luckily, I discovered a local classroom training dedicated to data science and there I really understood the importance of actually doing work on the field (rather than focusing on theories). Then I started immersing myself with all things data science. I listened to podcasts about it, I followed prominent data scientists online, read books, etc… I still do this every day since there’s just too much to learn about the field and I found this beyond exciting.
Now we hear a lot of development happening in different industries especially in medical imaging (automated skin cancer detection, breast cancer early detection) and the roots of those innovations come from the fundamentals of organizing, cleaning, and processing huge amount of data.
The role of a data scientist is similar to being a web developer. When the internet came to being, there are a lot of tasks and technologies encompassed in a single job title. A data scientist is expected to have a deep understanding of the domain/industry he/she is working on, must be comfortable with handling data, knows how to communicate the data to specific audiences, understands core mathematics (statistics, linear algebra, etc.), must be comfortable with reading and writing code, and a lot more skills relating to the end-to-end process of getting the data until delivering meaningful, actionable output.
It’s a lot of work and expectation to give a single role and I’m thrilled to see how the community progresses to spread all this work around. Similar to how we now have frontend developers, backend developers, DevOps, etc. for the web, we will see new job titles to cover niche tasks in the future.
As with any other role, it takes deep interest in these tasks for a company to accept you as their data scientist. If the tasks I mentioned above sparked joy in you, then there are a lot of online resources on how to go about doing some work in data science.
Part of the Data Scientist’s role is to deliver the results in a manner that each targeted audience understands natively. This process is now popularly known as “Data Storytelling” wherein we try to make the visuals compel the audience to do something or understand immediately what happened. This is achieved through the use of different visualization techniques and most importantly understanding beforehand what you want the audience to learn.
This varies largely on the project and the industry. But since most of our outputs are useable products instead of research findings/inferences, we mostly use Python as the primary language to simplify the deployment process. As for the data sources, each of our client has their own preference and needs depending on how sensitive their information is and how often and timely do they need to access them.
I am delighted to work at Appcentric for we have the right set of technology and client base to transform local companies especially those non-IT companies, be equipped with the right set of tools to harness useable information from their sleeping data, thrive, and operate on a global scale.
Appcentric Solutions Inc. is SAP’s Most Innovative Partner for two consecutive years. This recognition was given due to our constant innovation, dedication, and knowledge in creating systems and applications that run on top of SAP. We have enabled market leaders in different industries to run intelligent operations. Want to learn more about how data science can help your organization? Talk to us today! Our hotline is +63 2 759 1510 or email email@example.com.