I can assure you I have no relationship with this company. I have been wanting to learn Python for a while and I just saw the number of testimonials and figured worst thing that can happen is I lose 10 dollars so I assigned up.
My statistics class at Cal requires the use of R for statistical analysis. Professor wasn't the best, and the GSI was even worse. Took the "Intro to Machine Learning in R." Super helpful and made working with R in class much simpler. Thanks for helping my grade jump a letter.
I've been an avid user of Leada for a few months now, and must say that the one-on-one time I receive with the founders is very helpful. It's a crisp and effective digital classroom at my fingertips. As a beginner, it has been a very manageable and powerful tool to an introduction of a whole new way of thinking.
In this data-driven world, I would love to learn more about this. Especially we are about to launch a new product, A/B test will definitely help us to understand users more. Thanks for making effort on this subject!
Not quite. I used to be a 'data scientist' in finance. Their jargon for this position is "quantitative analyst".
The two definitely have intersections. Here is what's in DataScience \cap Statistics
1. A solid understanding of basic inferential statistics.
2. A healthy dose of skepticism about drawing conclusions from data.
3. The ability to model the problem and communicate how data fits into the model.
Here are the skills that most data scientists I know do NOT have but statisticians do.
1. A solid mathematical foundation of statistics: I am guessing because this stuff is only taught in graduate-level statistics classes, which most data scientists have never taken.
2. The research frontier of statistics, or the "what's new" of the statistics academia.
3. Coming up with new statistical methodologies (or discovering anything new and contributing to the foundation of statistics). This is not entirely true, but most data scientists I know are users of existing techniques/ideas.
Here are the skills that most statisticians I know do NOT have but data scientists do.
1. "Vocational" programming skills: most statisticians know R/Matlab, but many of them lack the experience of acquiring and cleaning data before doing analysis. I don't think this is a matter of ability but experience and lack of exposure.
2. Communicating statistical conclusions to non-statisticians: some of my statistician friends have a knack for this, but it is a skill that's hugely important for data scientists. People who want to be data-driven and ask for statistical conclusions far outnumber people who can actually understand statistical subtleties, and almost by survivors' bias, all data scientists I know are excellent at getting their conclusions across the organization even if others do not fully understand the full ramificaiton.
Data Science can mean machine learning, statistics, "data engineering" (the IT side of Big Data), SQL Monkey, data cleaning, data mining, data analytics, data visualization, etc.
So its like statistics, except there may not be math or statistics. Depends heavily on the role.
One of the founders of Leada here, any and all feedback would be appreciated but especially UX and if any part of the on-boarding process is confusing would be great!
