Terence Shin

Data Scientist | MSc Analytics & MBA student

a data enthusiast with 3+ years of experience in SQL and 2+ years of experience in Python, and a blogger on Towards Data Science and KDnuggets.

14 Data Science projects to improve your skills

By Terence Shin, Data Scientist | MSc Analytics & MBA student on December 1, 2020 in Data Exploration, Data Science, Data Science Skills, Data Visualization, Prediction, Project
There's a lot of data out there and so many data science techniques to master or review. Check out these great project ideas from easy to advanced difficulty levels to develop new skills and strengthen your portfolio.
How to Get Into Data Science Without a Degree

By Terence Shin, Data Scientist | MSc Analytics & MBA student on November 16, 2020 in Career Advice, Data Science, Online Education
Breaking into any new field or slogging through a career change is always a challenge, and requires focus and even a little grit. While transitioning to becoming a Data Scientist is no different, aspiring to this role is possible, even without a formal post-secondary degree, largely due to the vast amount of quality learning resources available today.
How to Explain Key Machine Learning Algorithms at an Interview

By Terence Shin, Data Scientist | MSc Analytics & MBA student on October 19, 2020 in Algorithms, Decision Trees, Interview Questions, K-nearest neighbors, Machine Learning, Naive Bayes, Regression, SVM
While preparing for interviews in Data Science, it is essential to clearly understand a range of machine learning models -- with a concise explanation for each at the ready. Here, we summarize various machine learning models by highlighting the main points to help you communicate complex models.
How I Consistently Improve My Machine Learning Models From 80% to Over 90% Accuracy

By Terence Shin, Data Scientist | MSc Analytics & MBA student on September 23, 2020 in Accuracy, Ensemble Methods, Feature Engineering, Feature Selection, Hyperparameter, Machine Learning, Missing Values, Tips
Data science work typically requires a big lift near the end to increase the accuracy of any model developed. These five recommendations will help improve your machine learning models and help your projects reach their target goals.

Terence Shin

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