data science vs machine learning reddit

In popular discourse, it has taken on a wide swath of meanings and implications well beyond its scope to practitioners. of the ML MOOC courses I've taken have been uniformly awesome and did such an amazing job of making what could have been abstruse, dense topics accessible and very interesting to non-Math/Stats majors. For example, time series statistics are almost all about prediction. From my actual university courses, I have taken some calculus based-probability and stats courses and I did well in a linear algebra course (I didn't particularly enjoy it though) but those were all mainly focused on application and computation; an actual math major who can actually prove all the theorems that I merely used would easily destroy me. Machine learnists tend to get to work in situations where there is an established data pipeline: there's lots of data and it's very dirty and the scientific question is often much more vague. I mean, I DID enjoy my data structures and algorithms class and Sedgewick's Coursera Algorithms course. Put simply, they are not one in the same – not exactly, anyway: But so do statisticians, but I guess we use high level languages. The difference between data science, ML, and AI is that data science produces insights, machine learning produces predictions, and AI produces actions. Quick start guide for data science: (in no particular order) Introduction to Computer Science with Python from A subreddit for those with questions about working in the tech industry or in a computer-science-related job. Part of the confusion comes from the fact that machine learning is a part of data science. It also involves the application of database knowledge, hadoop etc. MOOCs are great for breadth and exposure, but are no where near the level of a graduate level course for the most part (places like Stanford put all the lectures and materials online for free though). Are you thinking to build a machine learning project and stuck between choosing the right programming language for your project? You can't look at your cohort members as competition, or grad school will eat you alive. Data science involves the application of machine learning. You'll need more math although it seems like you have decent amounts to start (calc 1-3, linear algebra, and probability theory would be the core ones you use day to day/what comes up in papers + convex optimization would be good too for a grad math class). In a typical cohort of 20 - 30, and given that it's grad school, it wouldn't be disproportionate. Statisticians conversely tend to have more applied knowledge, work in groups, and have stronger mathematical rather than computational skills. I'd be very careful with mixing up machine learners and data scientists. I've recently been doing research on the state of the data science/ML hiring market, trying to answer the question of how in-demand different roles really are. In conclusion MOOCs are good to know what is out there at a superficial level, but a real graduate education will go a lot further and get you that desired T shaped knowledge. Also, we're on the verge of the next major economic revolution with DL (self driving vehicles, universal real time translators, good robots, rapid drug discovery, etc.). My only "side projects" have been Kaggle, basically (a few bronzes and a silver). Press question mark to learn the rest of the keyboard shortcuts. Machine learning is a field of study that gives computers the ability to learn without being explicitly programmed. Some of this might suck to read, but hopefully it'll help. Your CS program will give you a great footing, and real-world experience in and an interest in data, mathematics, statistics, and business intelligence will do the rest. When I first started learning data science and machine learning, I began (as a lot do) by trying to predict stocks. Now that literally every method is somehow described as machine learning, we've all had to move on to calling what we do 'AI' or some version of a 'deep' method. Will you snag a 6 figure SV job teaching neural nets to identify weakpoints in GIS infrastructure? The top people in regular software engineering earn over $1 million as well. Data scientists aren't proper scientists, while Statisticians aren't proper mathematicians. Data science involves the application of machine learning. These companies are so bent on getting people with experience that they've turned down people with relevant advanced degrees. Data Science versus Machine Learning. Building machine learning pipelines is no easy feat – and amateur data scientists are not exposed to this side of the lifecycle. It needs mathematical expertise, technological knowledge / technical skills and business strategy/acumen with a … As the demand for data scientists and machine learning engineers grows, you can also expect these numbers to rise. It is this buzz word that many have tried to define with varying success. I couldn't get any internships for data science/engineering or ML, however, because I have no experience with big and messy data sets (the Kaggle ones I used happened to all have sanitized ones that could fit in memory). There isn't any shortage for ML jobs (you just need the skills/credentials). After looking through the job postings for every data-focused YC company since 2012 (~1400 companies), I learned that today there's a much higher need for data roles with an engineering focus rather than pure science roles. You'd all be going so you could take your Masters degrees and skip the 5 year line of working your way up the ladder. Before going into the details, you might be interested in my previous article, which is also closely related to data science –

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