(Professional Development Project. Week 1: first update.)

Over the past year, as I finished up my AS in Computer Science, as I’ve been a participant in the Praxis program, I spent a good deal of time gaining entry-level skills in a variety of technological areas: SQL, systems analysis, web development skills including HTML, CSS, and JavaScript, etc. After all that, I wanted to pick something to focus on for continuing professional development.

As I considered what I should do next, I realized that while I have some decent skills in web development, I have a stronger aptitude in analytical areas. Further, I really enjoy solving problems and doing analyses, so I decided that I would go ahead and start doing that.

Ultimately, I decided on a data science course from Udemy, which I’ve now been working on for a week.

So far, I’ve completed about 130 out of 470 total segments (each includes a lecture and an accompanying quiz). This was basically the first two major sections: an overview including definitions of industry jargon, and an in-depth section on descriptive and inferential statistics. Given that I just finished a class in statistics as one of the final classes for my degree, I was able to skim through the second section.

Besides the refresher on statistics, what I basically learned this week was a lot of jargon and technical terms. I learned the distinctions between analysis and analytics; between business analysis, systems analysis, and data analysis; between neural networks and deep learning.

From here on out, you can expect weekly updates every Sunday, detailing what I’ve done that week. When it becomes applicable, I’ll be doing some coding projects as demonstrations of what I’ve learned by that point. I’ll post those here too, giving each project its own write-up, and I’ll then link back to those project posts at the end of each week in the wrap-up post.

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