The challenge of implementing machine learning models without a good understanding of the underlying math and programming skills may lead to a blackbox approach in data science training.
Data Science, Machine Learning, and Analytics are considered to be among the hottest career paths. The demand for skilled data science practitioners in industry, academia, and the government is rapidly growing. This has given rise to a proliferation of massive open online courses (MOOCs) covering different areas of data science and machine learning. The most popular providers of MOOCs include the following:
a) edx: https://www.edx.org/
b) Coursera: https://www.coursera.org/
c) DataCamp: https://www.datacamp.com/
Most beginners interested in getting into the field of data science are always concerned about the math requirements. Data science is a very quantitative field that requires advanced mathematics. But to get started, you only need to master a few math topics. In this series of articles, we will dive deep and discuss the essential math topics that must be reviewed before embarking on a data science journey. The topics to be covered in the series are:
Other articles in the series:
Regression models are the most popular…
Other articles in the series:
Understanding the most important features to use is crucial for developing a model that performs well. Knowing which features to consider requires experimentation, and proper visualization of your data can help clarify your initial selections. The scatter pairplot is a great place to start.
The scatter pairplot is a visualization of pairwise relationships in a dataset and is the first step for effective feature selection. It provides a qualitative analysis of the pairwise correlation between features and is a powerful tool for feature selection and dimensionality reduction. …
Most beginners interested in getting into the field of data science are always concerned about the math requirements. Data science is a very quantitative field that requires advanced mathematics. But to get started, you only need to master a few math topics. In this series of articles, we will dive deep and discuss the essential math topics that must be reviewed before embarking on a data science journey. We will begin with functions. The topics to be covered in the series are:
Other published articles in the series:
When I wrote my first article on medium back in June 2018, I hardly would have imagine that I was launching the beginning of a writing career. Before summer 2018, I didn’t even know that a blogging website like medium existed. I was working with a group of friends on a data science project, and one of my team members shared a tutorial which he found on medium to help guide us while we tackled the project at hand. As I navigated through medium, I just fell in love with the platform. …
Disclaimer: This article is meant to share some basic knowledge about personal finance and wealth building, and in no way should be considered as investment advice.
Investing is a subject that a lot of people are uncomfortable talking about. Some view investing as as type of gambling activity with only a small chance of success. However, investing is inevitable for financial planning and wealth building. The best saving accounts, even money market accounts have interest rates (< 1%) that can’t even keep up with inflation (~ 3% annually). Also, as we get old, cost of living will increase as our…
I received the April and May $500 bonus from medium. The message from medium read like this:
Benjamin: This past month, you were in the top 1,000 writers in the Partner Program. Congratulations! To celebrate your achievement, we’re rewarding you with a $500 bonus.
Unlike in April (2021) where the top 1,000 writers each received a $500 bonus, in May (2021), the bonus was distributed into 3 bonus categories or tiers as shown below:
I’ve seen numerous posts, books, or ads about learning data science in 4 weeks or in one year. While it is possible to learn the basics (black-box knowledge) of data science within a short period of time, it takes a lot more to really master the theoretical and practical aspects of data science. That being said, the journey to become a data scientist is analogous to a marathon, not a sprint. Here are 5 reasons why:
The proliferation of data science programs, whether from well-known colleges or from massive open online platforms means there are lots of options to choose…
I recently wrote a data science blog, entitled: Essential Linear Algebra for Data Science and Machine Learning, which can be found using this link:
The basic idea of the article is to illustrate the application of linear algebra in data science and machine learning.
Writing of code: A Jupyter notebook code was developed for analyzing the data and for producing all data visualizations and tables.
Preparation of article draft: The output from the Jupyter notebook was used…
Physicist, Data Science Educator, Writer. Interests: Data Science, Machine Learning, AI, Python & R, Personal Finance Analytics, Materials Sciences, Biophysics