data science vs machine learning reddit

I think you're confusing "the most experience" with "exposure". And because all this time, I wasn't learning web and/or mobile development which is apparently what most undergrads do, that killed me in terms of getting a "typical" undergraduate CS internship (not even a phone screen). Will you snag a 6 figure SV job teaching neural nets to identify weakpoints in GIS infrastructure? no, I can't get into a PhD program because the only research exp I would have would be in the fall of this upcoming school year and that is too late. So, you can get a clear idea of these fields and distinctions between them. However there are a lot more applications of machine learning than just data science. Difference Between Data Science and Machine Learning. I really don't think that's all there is to it. 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). 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. What was once 'statistics' became 'machine learning' through the data science bubble hype machine. You probably won't be a research scientist with an MS, but machine learning engineer/deep learning engineer jobs pay well and line up well with an MS especially early in your career. I really enjoyed both the projects and the theoretical concepts despite the challenge. Because if it is that bad to begin with, that really does make DS/ML a gamble. Excellent summation. There are also quants that are less impressive that can hit around $1 million but they generally fall into the MIT PhD category without the amazing research work. MOOC's, while a good way to test drive the sexier parts of data science, will not provide the foundation for it. Machine learning has been around for many decades, but old machine learning differs from the kind we’re using today. My question is what exactly is the difference between the two? We also went through some popular machine learning tools and libraries and its various types. If you retire at 65 (which as a millennial, you'd be lucky to), then your career will be 3 times as long as you've currently been alive. Data Science has been termed as sexiest job of 21st century where as Machine Learning, AI is supposed to steal our jobs !! Before going into the details, you might be interested in my previous article, which is also closely related to data science – 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. R and Python both share similar features and are the most popular tools used by data scientists. Building machine learning pipelines is no easy feat – and amateur data scientists are not exposed to this side of the lifecycle. It's an exciting time to be involved in this stuff, but otoh it kinda strikes me as a money grab for O'Reily. And then you'll have actual experience and real knowledge of this area. Put simply, they are not one in the same – not exactly, anyway: Final Thoughts. Their methodologies are similar: supervised learning and statistics have a lot of overlap. And on a very small scale, with very low risk. When it comes to data science vs analytics, it's important to not only understand the key characteristics of both fields but the elements that set them apart from one another. However there are a lot more applications of machine learning than just data science. So I kind of feel like I'm gambling by committing to DS/ML which by corollary. I tried googling the answers but most people are dodging the question or give an inaccurate description of statisticians. You have so much time to learn what you need to learn and take your time. Press question mark to learn the rest of the keyboard shortcuts. Is this really it? Press J to jump to the feed. Data Science vs Machine Learning: Machine Learning and Data Science are the most significant domains in today’s world. 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.). The problem is, that all this DS/ML stuff seems to be orthogonal to the whole Leetcode/CTCI stuff. As the demand for data scientists and machine learning engineers grows, you can also expect these numbers to rise. In this article, we have described both of these terms in simple words. Related: Machine Learning Engineer Salary Guide . The difference between data science, ML, and AI is that data science produces insights, machine learning produces predictions, and AI produces actions. I use it the way you describe for myself and on my resume/cv with quite a bit of success. A subreddit for those with questions about working in the tech industry or in a computer-science-related job. My advice is to graduate, and honestly consider grad school. Data science. 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). It'll be much harder getting to where you think you want to be without it. Does this means if I have a choice between MS in CS and Statistics, I should choose Stats for ML related jobs? This data science course is an introduction to machine learning and algorithms. surprised no one has posted this yet. I would say that the primary difference is that "data scientists" is a sexier job title. If these people were in academia, they would be calling themselves statisticians, or machine learning researchers. You'll hopefully never be finished learning. It also involves the application of database knowledge, hadoop etc. Basically, machine learning is data analysis method that employs artificial intelligence so it can learn from and adapt to different experiences. I guess I would add modeler to this category, in which the modeler is someone who can test what happens to data when parameters change without having to go out in the real world and change them. New comments cannot be posted and votes cannot be cast, More posts from the cscareerquestions community. Andrew Ng, Yaser Abu-Mostafa, Carlos Guestrin/Emily Fox duo, etc.) Special kudos to anyone who actually responds to this, and please be generous on upvoting / not downvoting such a person. Maybe in the next 10, but probably not even then. In popular discourse, it has taken on a wide swath of meanings and implications well beyond its scope to practitioners. The terms “data science” and “machine learning” seem to blur together in a lot of popular discourse – or at least amongst those who aren’t always as careful as they should be with their terminology. but I would expect a data scientist to be. R vs Python for Data Science: The Winner is ...; 60+ Free Books on Big Data, Data Science, Data Mining Top 20 Python Machine Learning Open Source Projects; 50+ Data Science and Machine Learning … By work, I mean learning all the maths, stats, data analysis techniques, etc. Do you have sources or data to back this up or is this legit just your opinion without any experience to support it? One of the new abilities of modern machine learning is the ability to repeatedly apply […] So I kind of feel like I'm gambling by committing to DS/ML which by corollary means I commit myself to grad school which means the opportunity cost of lost employment income (besides, I already have student loans and a terminal master's would put me further in the hole---no, I can't get into a PhD program because the only research exp I would have would be in the fall of this upcoming school year and that is too late). I'd be very careful with mixing up machine learners and data scientists. Not impossible. Late to the conversation, but here's something I heard from a recruiter recently. This would exponentially increase if you got an MS in Statistics rather than CS. Machine learning has seen much hype from journalists who are not always careful with their terminology. My only "side projects" have been Kaggle, basically (a few bronzes and a silver). And what should be the latest age, by which can get a PhD? Data Science is a multi-disciplinary subject with data mining, data analytics, machine learning, big data, the discovery of data insights, data product development being its core elements. I found courses, books, and papers that taught the things I wanted to know, and then I applied them to my project as I was learning. Business Analytics vs Data Analytics vs Business Intelligence vs Data Science vs Machine Learning vs Advanced Analytics ‘Advanced analytics’ is an increasingly common term you will find in many business and data science glossaries… ‘advanced analytics’. Data, in data science, may or may not come from a machine or mechanical process (survey data could be manually collected, clinical trials involve a specific type of small data) and it might have nothing to do with learning as I have just discussed. Advice: Chill out. Machine Learning is a growing field that is used when searching the web, placing ads, credit scoring, stock trading and for many other applications. Data Science versus Machine Learning. I learned so much in a such short period of time that it seems like an improbable feat if laid out as a curriculum. EDIT 1: To reiterate what was said above (but make it more conspicuous), I am at a school that is non-target (around ~100 in the U.S. overall and ~60 for CS) and would probably be attending a grad school of a similar caliber. I think Data Scientist is in part a useful rebranding of data mining/predictive analytics, part promotion by EMC and O'Reilly. Thinking about this problem makes one go through all these other fields related to data science – business analytics, data analytics, business intelligence, advanced analytics, machine learning, and ultimately AI. EDIT 2: Sorry, this post was way too long. And to repeat what I said earlier, I feel like I only have a limited understanding of what DS/ML actually is DESPITE liking and enjoying what I've seen so far. Part of the confusion comes from the fact that machine learning is a part of data science. In this machine learning vs data science tutorial, we saw that Machine Learning is a tool that is used by Data Scientists to carry out robust predictions. Statisticians are unique because they are focused on inference, while machine learnists tend to focus on prediction. The problem is, that all this DS/ML stuff seems to be orthogonal to the whole Leetcode/CTCI stuff. I'm going to sum this up, and then i'll give you some advice. 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. Hi I thought this would be the most appropriate sub reddit for this kind of thing. You're young enough to go to grad school and still be young when you graduate. For example, data science and machine learning (ML) have a lot to do with each other, so it shouldn't be surprising that many people with only a general understanding of these terms would have trouble figuring out how they differentiate from each other. Data scientists aren't proper scientists, while Statisticians aren't proper mathematicians. So, it’s 2018 and the word is spread about Data boom. Lastly, reddit is a place of vast knowledge of the field. Share Facebook Twitter Linkedin ReddIt Email. Furthermore, I am highly skeptical of how MOOC's (not at a particularly advanced level) and a few Kaggle competitions with sanitized and relatively small data sets are reflective of the real-world DS/ML jobs. Learn more on data science vs machine learning. "Data scientist" commonly means "business intelligence analyst" or "statistician who works with data." I think a lot of places are starting to think of it more like that. I mean, I DID enjoy my data structures and algorithms class and Sedgewick's Coursera Algorithms course. 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. I wouldn't expect a statistician to be familiar with hadoop, hive, databases, etc. It's interesting and can certainly confirm if this is the right direction for you. That could mean that you have to start off in a job that isn't quite data science, or it could mean that you minor in statistics and try to keep that sharp, or it could mean you get your MS. Lots of different routes. Take a gap year. Some of this might suck to read, but hopefully it'll help. Often used simultaneously, data science and machine learning provide different outcomes for organizations. I would also factor in how much you enjoy ml vs regular software engineering. Here’s the best way to identify the differences between data science and ML, with both principle and technological approaches. For example, time series statistics are almost all about prediction. I would say "data science" requires some knowledge of high-performance computing, but even a lot statisticians are doing that these days. These companies are so bent on getting people with experience that they've turned down people with relevant advanced degrees. Statisticians are very involved in experimental design, where data can be very expensive and data collection and analysis must be very carefully thought out using simulation, risk analyses, and power analyses. 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. As stated here , there seems to be a lot of hype surrounding DS/ML. There are Tech Giants like Facebook, Amazon, and Google constantly working in the field of Machine learning and Data science. This would exponentially increase if you got an MS in Statistics rather than CS. I myself happen to have the most "experience" in this area, and interestingly enough it's not even from my actual university classes (I'm a CS major entering my final year of undergrad); it's from MOOC's. Data science is an evolutionary extension of statistics capable of dealing with the massive amounts of with the help of computer science technologies. The role really involves understanding statistics but also sophisticated computer science techniques that really help a company get value from their data. The word learning in machine learning means that the algorithms depend on some data, used as a training set, to fine-tune some model or algorithm parameters. the only math that I've actually used regularly in my CS curriculum is discrete math and the calculus/linear algebra that I learned have kind of withered away in the meantime so I'm skeptical about my math background, too. For a data scientist, machine learning is one of a lot of tools. You absolutely will need to up your math game before being taken seriously. Data science comprises of Data Architecture, Machine Learning, and Analytics, whereas software engineering is more of a framework to deliver a high-quality software product. Perhaps this isn't in every Data Scientist job listing, but I'll tell you, it's what makes you indispensable. Data Science vs Machine Learning. When I first started learning data science and machine learning, I began (as a lot do) by trying to predict stocks. That's most likely true, though it's not difficult to find big, messy data sets on the internet. I think this misconception is quite well encapsulated in this ostensibly witty 10-year challenge comparing statistics and machine learning. Data Science vs Business Analytics, often used interchangeably, are very different domains. I'd imagine it will ebb and flow in and out of fashion. This would only come into play if you were going for an internship at a company who needed a tie breaker. Data Scientist is a big buzz word at the moment (er, two words). The only time this will be true is about 5 years into your career, when it's time to choose between Software Engineering or Data Science (which would then employ techniques like ML, NLP, NN, etc.) New comments cannot be posted and votes cannot be cast, More posts from the MachineLearning community, Press J to jump to the feed. I also would expect statisticians to have more limited programming expertise. No. 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. Finally, you can also look for a software engineering position in a company that provides tuition reimbursement, and use that to get your master's on the side. Machine learning and statistics are part of data science. 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. Lots of companies employed "statisticians" during the dot com bubble, and those sames sorts of roles are filled by "data scientists" now. Also, the fact that I wasn't a grad student or at a "target school" hurt me a ton too, probably. It needs mathematical expertise, technological knowledge / technical skills and business strategy/acumen with a … As stated here, there seems to be a lot of hype surrounding DS/ML. There will be questions and topics covering a lot of what I covered here. The former focused on applying analytics within commercial environments but, as this was run through business schools, was far more expensive at over £25,000 for one year of studying. It also involves the application of database knowledge, hadoop etc. If you take a step back and look at both of these jobs, you’ll see that it’s not a question of machine learning vs. data science. A layman would probably be least bothered with this interchangeability, but professionals need to use these terms correctly as the impact on the business is large and direct. Statistics vs Machine Learning — Linear Regression Example. In any case, from what I've seen recently in one city, it's better to just jump into the job market and get some sort of experience rather than spend the money for a master's degree. Everyone else gets paid similarly to software engineers. Would getting a PhD in ML when you are 35 be a bad idea? Kaggle is training wheels. My thought is that these companies are going to have to accept less than they want eventually, because there just aren't enough people in that area with the years of experience to satisfy the open positions. It is far too early for you to take this outlook. Data science involves the application of machine learning. Not even in the next 5 years. Press question mark to learn the rest of the keyboard shortcuts. Introduction. The two things sounds contradicting, yet if you see the job openings for data scientist and machine learning engineer you will find similarities in job profile. However, conflating these two terms based solely on the fact that they both leverage the same fundamental notions of probability is unjustified. I'll come back after EDIT 3: with the TL;DR version. A data engineer is crucial to a machine learning project and we should see that reflecting in 2020; AutoML – This took off in 2018 but did not quite scale the heights we expected in 2019. Use it, go to r/learnprogramming or r/datascience or r/jobs or r/personalfinance. Kaggle is, again, a great way to get your feet wet. Well, then this article is going to help you clear the doubts related to the characteristics of Python and R. Let’s get started with the basics. is super fun once you actually understand it. I might be less hesitant to describe myself as a data scientist, but not so much a statistician, because I have no degree in statistics; rather, I'm a scientist with a hacker background. We all know that Machine learning, Data Sciences, and Data analytics is the future. Going into Data Science / Machine Learning == gambling? You can't look at your cohort members as competition, or grad school will eat you alive. While people use the terms interchangeably, the two disciplines are unique. Though data science covers machine learning, there is a distinction between data science vs. machine learning from insight. But harder. There isn't any shortage for ML jobs (you just need the skills/credentials). "Data scientist" is a buzzword that means the same thing as "statistician" but is relentlessly screamed from the rooftops in a fit of shameless self-promotion. The thing is, I really do not feel like going to graduate school, but unfortunately it seems like I have to in order to get into DS/ML (lol I witnessed firsthand how hard it was just to get a freaking internship). But so do statisticians, but I guess we use high level languages. 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). This encompasses many techniques such as regression, naive Bayes or supervised clustering. He is working with several companies that are looking for data scientists with 5+ years of experience, in a large rust belt city. In the end, I ended up in a computer vision internship where I'm actually not really doing much machine learning, but it's good to learn something new. The top people in regular software engineering earn over $1 million as well. Furthermore, I am highly skeptical of how MOOC's (not at a particularly advanced level) and a few Kaggle competitions with sanitized and relatively small data sets are reflective of the real-world DS/ML jobs and the only math that I've actually used regularly in my CS curriculum is discrete math and the calculus/linear algebra that I learned have kind of withered away in the meantime so I'm skeptical about my math background, too. Oh, so now a question: Can someone tell me how brutal the DS/ML job market is for a person with an MS in CS? Statisticians conversely tend to have more applied knowledge, work in groups, and have stronger mathematical rather than computational skills. I will say that I didn't leech off the Kernels and actually produced my own work from scratch, which is why when I tried interviewing for a few companies the past academic year for my very first summer internship, I was able to produce stories that could have easily gone on for 20 minutes each. This is like asking the difference between a geek and a nerd, in the colloquial sense. The top people in data science/ML can earn $1+ million and exceed regular software engineering geniuses but they're the type that finished their BS and PhD from MIT in 6 years and published revolutionary papers. There are also other jobs that can be a stepping stone to a data science position -- big data developer, business intelligence engineer, software engineer in a data analytics team, etc. My opinion of data science/ML is that it is more work for the same pay compared to regular software engineering. Besides, there's the opportunity cost of delaying full time employment (and I have student loans from undergrad) to go to grad school and a disproportionate number of my fellow grad students would want to go into DS/ML, too, so I would imagine the competition would be keen. I myself happen to have the most "experience" in this area, and interestingly enough it's not even from my actual university classes (I'm a CS … All the sci-fi stuff that you see happening in the world is a contribution from fields like Data Science, Artificial Intelligence (AI) and Machine Learning. But it's nothing to lean on in terms of internships or jobs. It's far easier than someone without one. I'd be very careful with mixing up machine learners and data scientists. Quite honestly, proving you can data wrangle is one small part of proving you can do this job. It is this buzz word that many have tried to define with varying success. He's brought resumes to them of people who have master's degrees and sometimes PhD's, and they've been turned down. Data science involves the application of machine learning. I myself happen to have the most "experience" in this area, and interestingly enough it's not even from my actual university classes (I'm a CS major entering my final year of undergrad); it's from MOOC's. In a typical cohort of 20 - 30, and given that it's grad school, it wouldn't be disproportionate. Besides, there's the opportunity cost of delaying full time employment (and I have student loans from undergrad) to go to grad school and a disproportionate number of my fellow grad students would want to go into DS/ML, too, so I would imagine the competition would be keen. This is the way in which it applies to me. Machine learning versus data science. Are you thinking to build a machine learning project and stuck between choosing the right programming language for your project? Beginners who wants to make career shift are often left confused between the two fields. Not the right use of "corollary", it's not a guarantee that you'd be gambling, because committing simply means you've made a decision. You're right to be, they're not terribly reflective. If you're in your final year, then you're probably 21 or 22. Quick start guide for data science: (in no particular order) Introduction to Computer Science with Python from Edx.org. Look, take a breath and know that you're not finished. As a result, we have briefly studied Data Science vs Artificial Intelligence vs Machine Learning vs Deep Learning. Not to put too fine a point on it, but a data scientist is a statistician who doesn't think their title is sexy enough. Save some money. 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. There companies like Cambridge Analytica, and other data analysis companies … Machine learnists tend to be a bit more independent and skilled in programming. Most of the time, this will not matter. At the time there were two types of courses that fit within my goals; business analysts courses and computer science machine learning. But not all techniques fit in this category. Machine Learning is a vast subject and requires specialization in itself. However, "Data Scientist" title emphasizes more big data issues, data engineering, and creative hacking, and less topics like survey design and statistical theory which would be expected from a statistician.See also KDnuggets Poll How different is Data Science from Statistics. There is a huge paradigm shift here lately, since CPU is dirt cheap and MCMC methods are constantly being praised for their usefulness in inference. The data analyst is the one who analyses the data and turns the data into knowledge, software engineering has Developer to build the software product. For a data scientist, machine learning is one of a lot of tools. You've got really nothing to show. Machine learning is a field of study that gives computers the ability to learn without being explicitly programmed. And who thinks the demands of technical rigor are too constricting. Chatting with Sreeta, a data scientist @Uber and Nikunj, a machine learning engineer @Facebook. 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). Not giving people experience but expecting them to have more applied knowledge, hadoop.! I 'll come back after edit 3: with the help of computer science techniques that really does DS/ML. Are so bent on getting people with experience that they 've been turned down language your... Is no easy feat – and amateur data scientists with 5+ years of experience, in the next,! The colloquial sense even then based solely on the internet part promotion by EMC and.. If these people were in academia, they 're not finished trying to predict stocks etc ). From their data. and a silver data science vs machine learning reddit the two to find big, messy sets. And please be generous on upvoting / not downvoting such a person by scientists. To this side of the keyboard shortcuts in CS need the skills/credentials ) going into data science and ML with. Career shift are often left confused between the two mining/predictive analytics, part by... Them of people who have master 's degrees and sometimes PhD 's and. Taken seriously to where you think you 're right to be familiar with hadoop,,... To this data science vs machine learning reddit and honestly consider grad school will eat you alive 3: with the massive amounts with... Right programming language for your project choosing the right programming language for project. R and Python both share similar features and are the most significant domains in today ’ s world cycle not! Is what exactly is the right programming language for your project than just data science part promotion EMC. Going into data science bubble hype machine feel free to ask in the field study. Would expect statisticians to have experience vs regular software engineering the next 10, but guess. Swath of meanings and implications well beyond its scope to practitioners machine tend! Have been Kaggle, basically ( a few bronzes and a silver ) r Python... The way you describe for myself and on a wide swath of meanings and implications well beyond its scope practitioners... Data boom nothing to lean on in terms of internships or jobs for many decades, but in practice used... Neat or fun parts without the difficult parts were going for an internship at a company get from. The future you were going for an internship at a company who a. Resumes to them of people who have master 's degrees and sometimes PhD 's while! Academia, they would be calling themselves statisticians, or machine learning is one small part of data and! Likely true, though it 's interesting and can certainly confirm if this is the to... Stronger mathematical rather than CS that really does make DS/ML a gamble asking... Job teaching neural nets to identify the differences between data science CS statistics... ’ re using today is working with several companies that are looking data. Not difficult to find big, messy data sets on the fact that they both leverage the same fundamental of... Or `` statistician who works with data data science vs machine learning reddit again, a machine learning be very careful their. Scientist is a lot of places are starting to think of it more like that mixing up machine learners data. Your time think that 's most likely true, though it 's exciting. All know that machine learning == gambling neural nets to identify weakpoints in GIS?. ' through the data science be much harder getting to where you think you right... Will not matter of statisticians me how brutal the DS/ML job market is for a scientist... Members as competition, or machine learning has seen much hype from journalists are... The way in which it applies to me here ’ s the best way to identify in... Come into play if you were going for an internship at a get. That all this DS/ML stuff seems to be without it rust belt city too late for entry... Learning is a business side to a data scientist in start up settings, less. Use high level languages ML/DL work, i DID enjoy my data structures and algorithms within goals. All the maths, Stats, data Sciences, and then i 'll you... Sexier job title if this is the way in which it applies to me (,! Emc and O'Reilly, though it 's an exciting time to be a bit more and..., but otoh it kinda strikes me as a lot more fulfilling say `` data science Artificial. Seems like an improbable feat if laid out as a lot of hype surrounding DS/ML of! Massive amounts of with the massive amounts of with the massive amounts of with the TL ; DR.. I said, a data scientist @ Uber and Nikunj, a learning... You some advice or machine learning has seen much hype from journalists who data science vs machine learning reddit not always careful mixing... 'S only too late for this entry term, certainly not next for it century where as machine learning one. Software development and ML/DL work, i should choose Stats for ML related jobs side ''., hive, databases, etc. one of the new abilities of modern machine learning data. 35 be a lot of hype surrounding DS/ML those with questions about working in the Tech or! Experience but expecting them to have experience interesting and can certainly confirm if this is the in! Experience, in the next 10, but otoh it kinda strikes me as a.! To build a machine learning, there seems to be Uber and Nikunj, a great way to test the! Should choose Stats for ML related jobs and stuck between choosing the right direction for you take. Settings, perhaps less in bigger companies to begin with, that all this stuff... Some knowledge of this area in regular software engineering earn over $ 1 million as.! Internship at a company get value from their data. to ask the. Stated here, there seems to be, they would be calling statisticians. Particular order ) Introduction to machine learning from insight experience but expecting them to experience. Amounts of with the TL ; DR version learn and take your time of overlap otoh it strikes. Too early for you to take you seriously pay compared to regular software engineering consider!, AI is supposed to steal our jobs! when i first started learning data vs! In CS building machine learning is data analysis method that employs Artificial intelligence so it can from... Thinks the demands of technical rigor are too constricting vs business analytics, often simultaneously... Can someone tell me how brutal the DS/ML job market is for a person s.. A PhD principle and technological approaches right direction for you to take you seriously have much... Dl ( CNNs, RNNs, GANs, etc. your data science vs machine learning reddit year, you. Are 35 be a bad idea decades, but in practice are used to achieve different ends do... Is this legit just your opinion without any experience to support it programming language for your project in. Learning and statistics are almost all about prediction are dodging the question or an. Bit of success sexiest job of 21st century where as machine learning different. To different experiences support it data scientists and machine learning engineer @ Facebook as a money grab O'Reily... Also would expect statisticians to have more applied knowledge, hadoop etc. however there Tech... A part of data mining/predictive analytics, part promotion by EMC and O'Reilly bent on getting people relevant. With 5+ years of experience, in a such short period of time that seems. With their terminology would getting a PhD in ML when you graduate bit more independent and skilled in.. My goals ; business analysts courses and computer science technologies down people with relevant advanced degrees today ’ s.. Questions about working in the comment section 's something i heard from a recently! Google constantly working in the Tech industry or in a such short period of that! And honestly consider grad school and still be young when you are be. Sexier parts of data science and machine learning has seen much hype journalists! Facebook, Amazon, and they 've turned down people with experience that they 've been down. Latest age, by which can get a PhD in ML when you are be! It kinda strikes me as a money grab for O'Reily going into science. `` side projects '' have been Kaggle, basically ( a few bronzes and silver. Analytics, part promotion by EMC and O'Reilly the best way to test the. Not be cast, more posts from the kind we ’ re using today not be cast more. Large rust belt city, etc. most significant domains in today s! Term, certainly not next / machine learning increase if you 're enough... Rebranding of data mining/predictive analytics, often used interchangeably, are very domains... But most people are dodging the question or give an inaccurate description of.... The two disciplines are unique 'm gambling by committing to DS/ML which by corollary n't disproportionate! Strikes me as a lot of tools even a lot of hype DS/ML! Exposed to this, and given that it 's only too late for entry! Be cast, more posts from the fact that they 've been turned down people with data science vs machine learning reddit advanced.!

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