Data Science




Data is the most valuable asset of any organization, be it a business or the government. It can be collected from customers to suppliers to help us create better products and services, or it can be used as fuel for innovation in various fields like technology and health care. But if we use it wisely the best outcome will come out of it. The key thing is getting it right.


Natural language processing (NLP) — If you are learning how to code and want to work on machine learning, then NLP might be your first point of entry into this domain. Natural Language Processing (NLP) can also be applied to marketing. You don’t have to know or at least understand natural language; you can find ways around building chatbots using artificial intelligence (AI). Some examples of that would be Google Assistant, Siri, and Cortana. Also, there are plenty of open-source libraries already available that make things easier and faster for anyone with an interest in learning about computers, especially for people who aren't tech-savvy but are interested in building their own tools. For example, Microsoft Open Source Neural Networks has a ton of tutorials available online to learn what they are about. Deep Learning Studio allows you to train any neural network program, which can then be installed on your macOS.


Data analysis — Since most companies now use big data for decision-making, all sorts of different research questions can be analyzed using statistical methods so that they can give real feedback to those in charge. One example could be product design or customer acquisition. Another area where researchers can use predictive analytics is in healthcare to predict diseases or sicknesses in patients before it becomes too late and offer them early treatment options. A good place to start studying the world of healthcare would be PubMed and EMBASE. Another area where quantitative techniques can be applied is finance or economics. Many different kinds of questions can be tried. For example, there's a lot of data regarding stock prices. How do financial analysts make predictions with the help of AI and computer vision? How does the price change itself as a result of changes in market conditions? We can even apply some concepts within insurance to predict if policyholders will claim a loss. To improve the overall understanding of what data science is, I highly recommend reading the book “Exploring Machine Learning With Python.” It’s an amazing resource that you can really jumpstart on for free!


I personally prefer coding the algorithms myself because it makes it much simpler to debug rather than just having someone else write it. This approach helps me develop my ideas quickly and allows me to see the results if needed. However, there is always room to collaborate and share knowledge with others. When working with machines, one of the biggest differences between humans and machines is their ability to think and reason. Working with code and looking for patterns within data or working out why something went wrong can be quite confusing, though it can help to become smarter along with this.


In my experience working with data, I’ve seen several interesting relationships emerge between datasets, models, and data scientists. My favorite example would be when I was working on analyzing traffic flows at our office. While driving, I noticed that some parts of the city looked exactly the same at any given minute, while others looked completely different. Due to this, we were able to analyze and identify the patterns and predict what the weather would be like for a particular day. That process helped save time and resources spent trying to solve this issue. Data can be extremely powerful and sometimes scary. Overcoming fears and fears that you may have about the unknown can lead to significant advancements in medicine and financial services. Taking certain steps and being proactive can help avoid dangerous situations or even bring out positive surprises.