Here is a recent interview I did for CLK Tech. CLK Tech is a newsletter based out of Northeast Ohio, run by a couple of tech recruiters in the area. Topics span general career questions and data science in particular.
In addition, I’m busy with a project that I look forward to announcing soon. It’s shaping up to be a a busy year…
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One of the challenges of data science in general is that it is a multi-disciplinary field. For any given problem, you may need skills in data extraction, data transformation, data cleaning, math, statistics, software engineering, data visualization, and the domain. And that list likely isn’t inclusive.
One of the first questions when it comes to machine learning in specific, is “how much math do I need to know?”
This is where I would recommend you start, to get the most value for your time:
- Matrix Multiplication (Subject: Linear Algebra)
- Probability (Subject: Statistics)
- Normal Distributions (Subject: Statistics)
- Bayes Theorem (Subject: Statistics)
- Linear Regression (Subject: Statistics)
Of course you will run across other math needs, but I think the above list represents the foundation.
If you need places to get started with those topics, check out Kahn Academy, Coursera, or your location library.
For more on machine learning, check out other posts such as ML in R, Linear Algebra in R, and ML w/XGBoost.