Customer segmentation, text classification, sentiment, time series, and recommender systems.
The goal of this article is to outline projects that a professional Data Scientist will eventually perform or should perform. I have taken a lot of bootcamps and educational courses in Data Science. While they have all been useful in some way, I find that some forget to highlight real-world applications of Data Science. It is beneficial to know what to expect as you transition from educational to professional Data Scientist. Customer segmentation, text classification, sentiment analysis, time series forecasting, and recommender systems can all help your company that you are employed at tremendously. I will perform a deep dive an explain why these specific five projects come to mind, and we will hopefully motivate you to employ these where you work.
Customer segmentation is a form of Data Science where an unsupervised and clustering modeling technique is employed to develop groups or segments of a human population or observations in data. The goal is to create groups that are separate, but the groups themselves have closely related features. The technical term for this separation and togetherness is called:
Between-groups sum of squares (BGSS)
Within-group sum of squares (WGSS)
how closely related the unique group features are
K-means clustering. Image by Author .
As you can see in the image above, these groups are well separated — BGSS and are closely centered — WGSS. This example is ideal. Think of each of the clusters as those groups that you will target with a specific marketing advertisement: ‘_we want to appeal to recent college graduates by marketing our company product as young-professional centered_’. Some useful clustering algorithms are:
DBSCAN K-means Agglomerative Hierarchical Clustering
What happens with customer segmentation results?
— finding insights about specific groups
— marketing towards specific groups
— defining groups in the first place
— tracking metrics about certain groups
This type of Data Science project is broadly used, but most useful in the marketing industry.
Learning is a new fun in the field of Machine Learning and Data Science. In this article, we’ll be discussing 15 machine learning and data science projects.
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
What is Machine Learning? Machine Learning is the field of study that gives computers the ability to learn without being explicitly programmed. [Arthur Samuel, 1959 ]
Included in each course section in this article will be what is included, some useful facts about the course, and what is unique about the course.
“How’d you get started with machine learning and data science?”: I trained my first model in 2017 on my friend's lounge room floor.