What is Stochastic Gradient Descent? Stochastic gradient descent is an optimisation technique, and not a machine learning model. It is a method that allow us to efficiently train a machine learning model on large amounts of data. The word ‘descent’ gives the purpose of SGD away – to minimise a cost (or loss) function. For […]
Since data science is such a new profession, there is a lot of confusion and uncertainty around job requirements and qualifications. A common question that gets asked is: what kind of qualifications are necessary to get a job in data science and is a Masters degree or PhD really worth it? I wanted to share […]
I have a definite love-hate relationship with productivity. To me, being productive means that I am actively working towards my goals and dreams. However, productivity is not something I can objectively obtain every day. Instead, it ebbs and flows based on so many factors such as diet, sleep, sickness, weather, connection, conflict, my own internal […]
Reading a textbook for self-study is very different to school or university. Back when I was in school, I read the textbook for the sole purpose of doing well on a test and it didn’t really matter if I couldn’t apply the concepts I was learning about. However, I have come to realise that this […]
Welcome to the next 6 months of my data science learning journey! After being heavily inspired by Daniel Bourke’s AI masters curriculum, I have created my own learning curriculum that I will be following for the next 6 months. Will this be a challenge for me? Yes. Will the content be difficult and hard to […]
Business Intelligence forms the back-bone of decision making in an ever-growing number of organisations in the world today. More than ever, organisations need to be able to make fast decisions to changes in the market. Unfortunately, many businesses do not have a system that allows them to obtain the insights they need to do this. […]
In this week’s data science project, I apply principal component analysis to a dataset based around hospital mortality using python. The dataset is extensive, containing over 50 variables on demographic characteristics, vital signs, and other measurements taken in the lab. This should be a perfect use-case for dimensionality reduction with PCA. What is Principal Component […]
In this data science project, I tackle the problem of data segmentation or clustering, specifically applied to customer data. There are a couple of different algorithms to choose from when clustering your data depending on your requirements and inputs. However, K-Means clustering is one of the most popular methods in machine learning and is the […]
Task automation has become like a secret weapon as a data scientist. My only regret is not getting started with automation sooner. In the past, I have felt intimidated by the process of automation because I always thought it was an intensive, tedious process. I have come to learn that automation has done the exact […]
Those companies that view data as a strategic asset and develop robust data and analytics strategies are the ones that will succeed in this new data-driven world. Bernard Marr It is very easy to get caught up in the latest buzzword, jumping on the big data bandwagon without any clear direction on where you would […]