I would recommend this Coursera course - Learning How to Learn: Powerful mental tools to help you master tough subjects. Its useful even if you are not a student. I did this course back in August and it has helped with my productivity tremendously. The next version starts on Jan 2nd. You can read some reviews here.
You can also checkout this list of Ten Most Popular Courses of 2014.
Yale has some online courses. Some you can get certificates from, most are just very interesting and helpful to better your knowledge on an array of subjects:
They also have a youtube page here:
Edit: not even just Yale (thats just the one I went through) but this website also has tons of online courses:
I will call complete bullshit on this one. I took a class on Coursera called <strong>Learning how to learn</strong> (which I highly recommend btw), and they rip this opinion to shreds. You have two types of tasks. One where you use your focus mode, and one where you use diffuse mode. Focus mode is intensive and intended. Diffuse mode is what you do with intuition.
So if you are doing something where a lot of focus is required, especially learning a new task, adding ANY other kind of stimulus is the worst thing you can do. If you are doing something that you've been doing for years, and don't require that much focused attention, music will probably do you no harm.
The best thing that you can do for a focus intensive task is to really try hard for short bursts of time (30 min), followed by an activity you find relaxing. It is called the pomodoro technique.
Here're a few common paths:
1. Many people are applying ML to projects by themselves at home, or in their companies. This helps both with your learning, as well as helps build up a portfolio of ML projects in your resume (if that is your goal). If you're not sure what projects to work on, Kaggle competitions can be a great way to start. Though if you have your own ideas I'd encourage you to pursue those as well. If you're looking for ideas, check out also the machine learning projects my Stanford class did last year: [link] I'm always blown away by the creativity and diversity of the students' ideas. I hope this also helps inspire ideas in others!
2. If you're interested in a career in data science, many people go on from the machine learning MOOC to take the Data Science specialization. Many students are successfully using this combination to start off data science careers. [link]
This is great, thanks. A couple resources I wanted people to see as well:
/r/philosophy has a fairly extensive recommended reading list, available here. If you're looking for an introduction to a topic, try that!
Coursera has a few philosophy classes, and at least some of these are offered by professional philosophers at top universities, and are quite good. For example, this Intro to Philosophy is a great start.
Wi-Phi is a partnership between professional philosophers and Khan Academy, and includes many great and easy to digest videos on a variety of philosophical topics.
I took a pretty interesting short MOOC from Coursera called Learning How To Learn. I highly recommend it.
It's very easy to follow, and it goes into a lot of good practices for learning, but also a lot of easy to follow science behind it, that really enlightens and encourages you to learn.
It goes into breaking down learning something, how much to study, how to study, when to study, etc. It goes into what your brain does when you sleep to "save" the things you're learning (neural connections, like the guy in this video said)
I would definitely recommend taking this free short course. It sounds like something you're interested in, and it really helped me.
Were you given a syllabus or curriculum? Are you teaching for the AP CS test? What do you want the kids to get out of this class?
Personally, I think a CS class would do really well to have logic puzzles, brain teasers, and critical thinking, especially early on when no one knows how to program. It's a good way to start thinking logically and algorithmically.
But it'd be a silly intro to CS course if the students never learned programming (after all, prog languages are how we convey our ideas and incidentally happen to also run on computers :P). You'll want an easy high-level languages that the students can jump right into. I recommend Python - it's very easy to understand and there are lots of terrific resources for it.
I found this coursera course to be one of the best MOOCs I've been a seen. It's introductory programming in Python. In fact, If I were you (and if its offered again in the Fall), I'd consider having the students enroll in this. The video lectures are great, it has quizzes to test understanding, and the assignments / mini-projects are amazing. Plus, their codeskulptor site is a great thing - you can get a Python program up and running in no time (no installation hassle required)!
The mini projects build interactive games (such as pong and asteroids) with appropriate starter code. It uses its own simplified GUI library designed for first year CS students that are new to programming. The fun interactive games are a great way to keep studets engaged (rather than writing a program to compute a Knight's Tour or whether a number is prime, etc).
I, myself, learned Python from Google's Python class taught by Nick Parlante. Very good lecturer, and goofy too (the good thing). The assignments were fun little exercises for me.
Good luck! This sounds very exciting! I hope you and your students have a great time :)
"It is the mark of an educated mind to be able to entertain a thought without accepting it" - Aristotle
Cognitive decoupling is a prerequisite to algorithmic intelligence.
Have you seen this? [link]
Check out [link] Its an "education platform that partners with top universities and organizations worldwide, to offer courses online for anyone to take, for free" They literally have thousands of college courses covering all topics/interests. A few upcoming farm/agricultural/homesteading type courses included Chicken Welfare and Behavior, Sustainable Agricultural Land Management, Livestock Health Management, and Introduction to Household Water Treatment and Safe Storage. The first time I discovered the website I spent almost 3 hours browsing through courses. Ive been hooked ever since!
My daughter and I just took their "Beginning Programming" course using Python. It was PHENOMENAL.
I wholeheartedly recommend it.
The profs were great, the exercises built on the lectures and actually taught us more than the lectures. They weren't just rehashing the classes, they made you think and move farther.
EDIT: This one: [link]
You start with just the NAND gate and build logic and arithmetic with that.
Then you build a complete computer ~~with blackjack and hookers~~ and design an assembler and high-level language (including the corresponding compiler) for it.
You also write a VM and an OS. In the end, you finish the course by writing Tetris for your own computer.
The materials are free, open source and always available. You can also take the free course on Coursera, starting April 11.
They are the backbones of programs.
One of the best books (sort of THE standard book) is Algorithms 4^th Edition by Robert Sedgewick and Kevin Wayne (link to online version - free - Java oriented but available for many languages or language agnostic).
There are Coursera Courses covering exactly that subject:
Hi all, this is Professor Martinez-Davila from the University of Colorado! The question you pose on online courses is a really interesting one because some academic scholars like myself think that engaging "citizen scholars" in online courses is a huge opportunity for advancing discoveries. On June 15th, my "Deciphering Secrets: Unlocking the Manuscripts of Medieval Spain" free, online course (a MOOC) is opening at [link] .
I think we are at a really interesting point in historical studies because scholars like myself are not only interested in sharing their passion for history (in my case, medieval Spanish Jewish, Catholic, and Muslim coexistence) but also want to harness the power of crowdsourcing to rebuild medieval worlds -- even digital 3d worlds!
And, I've often found that professionals in the work world do some pretty amazing historical research themselves! Equal to or better than traditional university scholars. One of my Spanish friends, a computer scientist by training, is one of the best paleographers (a reader of old handwriting scripts) I know.
Thanks for sharing this post, Roger :-)
Coursera gives you online access to real college courses. They don't provide college credit, but at least some of them will provide a certificate of completion is you pass the course.
If you feel like taking a free online class, Berklee College of Music is offering this one, starting Oct 13:
Introduction to Music Production
You mentionned you don't know what the reverb is. They will teach you that, among other things.
> Some punters had paid as much as $200 to attend.
I wonder if they know that Coursera has a four-week course on Epidemics, Pandemics and Outbreaks by the university of Pittsburgh for free.
> 68 seems like a very small sample size
a) It isn't.
b) For a set that is small to begin with it definitely isn't.
That said, that alone is not sufficient to say whether or not their conclusions are any good. There is much more to know about a sample than its size. For example, why a sample? They must have the complete set of data to begin with...
Seriously: Great statistics courses (well, some of them anyway) are available for free on coursera.org and on edx.org. I recommend this guy and his two courses (coursera), he is a very good teacher of this subject.
You may find what is relevant to this subject in the videos of course 1, "Video Lectures", Module 3.
what is the cost? Not going to pay for an unaccredited program when I can take a courses taught by university professors for free on the same topic through coursera. Data Analysis and Statistical Inference, Data Science, The Data Scientist's Toolbox, and so on.
Not to be a jerk, just wanting to know what you offer that is superior to what is out there... and frankly not going to pay for a service I can get for free.
> Cele mai dificile subiecte pot fi explicate celui mai lent la minte om daca acesta nu si-a format nici o idee despre ele; dar cel mai simplu lucru nu poate fi clarificat celui mai inteligent om daca el este ferm convins ca stie deja, fara nici o umbra de indoiala, ce este in fata lui. - Lev Tolstoi
Sunt persoane care datorita unui context si a unui tip de argumentare, ajung sa creada tot felul de bazaconii. Acum, trebuie sa intelegi ca astfel de persoane nu sunt incapabile sa gandeasca. Este pur si simplu un context.
O discutie cu o astfel de persoana, poate fi teribil de frustranta. Este un curs pe Coursera despre argumentare: Think Again. Daca ai timp si te intereseaza ideea de a dialoga spre descoperirea adevarului, ti-l recomand cu caldura. Nu a inceput decat de 3 sapt... si cred ca-l poti prinde din urma.
I think at this stage, the most important thing is to validate your idea, whether are there customers who are willing to pay. Use platforms like WordPress, LaunchRock, Unbounce to put up a simple site, telling your prospective customers what you are offering, and how are they different from competitors, and see whether anyone signed up, or best, pay you for it.
This is then you will start to do a prototype. I would recommend you to at least learn a little bit about programming. There are tons of resources out there (treehouse, Udemy, Coursera) that you can pick up what you need to build a prototype. If not, at least you can divide your project into parts, and outsource it to different programmers, and you will be the one putting it together. In that case, you will be assure that no one programmer can just take your idea, and yet you can still get the prototype out. But the most important thing is you need to know a little about programming.
Actually, if I am you, I wouldn't be too concern about the idea being copied. You somehow have to market the idea to your prospective customers or users. The moment you market it, and one of the user/customer is a programmer, they can easily do up a web application that is similar to your idea.
Anyway on a sidenote, there are some free resources that can help you can started:
Both courses started in four days time. I took the first course before, and I would say it gives a pretty good introduction to programming in Python.
You forgot to mention Developing your musicianship which I found to be incredibly fascinating!
The Jazz Improv class went way over my head and I couldn't keep up, but that was a while ago, I might try it again one of these days.
There is also a brilliant course on Songwriting that gives you a great deal of information about the structure of a song and writing lyrics.
Evolution, as a scientific theory, does not address the origins of life at all.
And it really sounds like you don't understand the process of evolution at all. Random mutations are just one, and not ~~even the most important~~, evolutionary process of change.
I would suggest you read up on this as it sounds like you don't really understand the basic scientific premises of the theory well enough to properly object to it.
Give this a look:
EDIT I stand corrected - apparently random mutations are one of the most important determinants of genetic variation.
Try these courses:
The first one is a really good intro. Unfortunately, there are not much practical exercises - just a few tests after each lecture, but theory is nice.
Can't tell anything about the second course - haven't tried it yet...
Hope this will help. Good luck.
Wow, just wow. If you want an intro of games in python try one of the best e-courses (Free) I've seen so far:
An Introduction to Interactive Programming in Python
reviews (overwhelmingly positive):
Before you pay anything. Make sure it's what you want. That page barely tells you anything about the competence of the instructors, the way and range in which materials are covered, etc.
> As game developers, we often see students attending overpriced and under-performing schools, learning outdated, inferior, or incorrect information about video game development. We think you deserve better. Electronic Gaming Education was founded by industry professionals in the hopes of training people who really want to be in the industry by people already in the industry, at a price that is reasonable. Our training is more focused, concise, and applicable to working today!
Well, you guys need to show proof that you're worth it. Specially in the dawn of free e-learning from Coursera, Udacity, Edx, etc.
Well written: pulls together a couple different well known ideas in statistics and psychology and examples.
Will we stop interviewing people? Of course not, as the author undoubtedly knows, people think they will make the right decision and will refuse to give up control. Aside from regression towards the mean, in general, we almost always overestimate our ability to regress and compare / rank complex models.
As I am sure he will allude to in his next post, using models is a great way to improve the decisions we make. By explicitly stating the factors that matter to us, we will be much better at making a decision as we won't be jumbling around a bunch of random unorganized information in our mind. The example in the post was a little extreme, though.
However, should we stop interviewing people? No. When using a model as I described, including subjective information (shyness, assertiveness, tidiness, timeliness, etc.) is perfectly fine. By explicitly stating it and giving it a weighting, it will simplify your thought significantly. By not interviewing people, you lose the ability to ascertain one of the most important parts: how well you enjoy working with them (and potentially how well everyone else does and how well they work with the team).
Of course, one could potentially just put that as the most important thing in the model, but generally that never happens. Our gut feeling is mostly defined by the subjective observations, but when we are forced to rank them, usually we get more level headed.
I haven't done any of those online courses personally. All I can say is that the best piece of self study I ever underwent was:
It really is excellent.
Oh I do like Scala-chan. Scala tries to marry object-oriented programming and functional programming; hence the heterochromia. She is the beloved imouto of Java Onee-san as Scala also runs on the JVM, so you can use Java libraries. I don't know why she has those spikes on her boots though, maybe I should learn more about her.
the way he talked was straight out of a negotiation textbook, the dude was a master negotiator for sure.
I highly suggest everyone here takes as many negotiation courses as possible; here's a free go-at-your-own pace version I'm going through: [link]
edit: an word
Check out the reading lists for classes by Andrew Ng at Stanford. He co-founded Coursera and offers most of his courses online for free. His machine learning course is a classic over in the ML subs and his CS229 course was probably one of the most popular in the CS program when he was professor because it had many practical exercises and examples, including stock trading.
Free online course (you can go through it any time, but have to register to get a certificate at the end and chatroom help from TAs): [link]
A more specific course I would take after the general ML class above: [link]
We are always negotiating, everyday whether you like it or not.
If you had two identical situations and all merits are the same, and you could achieve your goals safely and ethically by just adjusting your argument and presentation, would you do it?
Take this course and change your life.
Also, I recommend reading the book "How to Win Friends and Influence People". I hate the title of the book, because it comes off as pretty underhanded and manipulative, but it's just the opposite. I like to describe this book as giving basic social skills for getting along with people.
This book teaches you basic social skills that you may have most of, but perhaps not all of it. For example, call people by their name and remember it. Don't talk about how great you are, but get the other person talking about how great they are. Get to know the other person and appeal to their needs and wants. Remember details about a person and bring it up occasionally. Never bad mouth a person or anyone for that matter. Smile! Etc.
Negotiations and that book combined have changed my life and have gotten me further toward my goals ethically and honestly in a way that pure academic knowledge and full resume couldn't do alone.
Here, this one is free. I checked it out and it has everything you need.
Actually, coursera has A history of the world since 1300 starting in two weeks, and The Modern World: Global History since 1760 sometime in the future.
shameless promotion: /r/OnlineEducation
edit: And I forgot Greek and Roman Mythology, starting in a couple of weeks.
I'd suggest taking a look at the free Data Science Specialization offered in Coursera. I've only tried the first two courses, but found them challenging enough with the added benefit of encouraging individual research to complete some of the assignments (they don't hold your hand like many MOOC do).
If I'm not mistaken, the courses are running throughout the whole year, so you can just sign up for whatever level you're comfortable with.
Sounds like its a culimination of boredom impacting self-worth.
Why not show her a free online course on [link] that matches one of her interests? I'm enrolled in a couple and its really nice to have that sort of extra information to keep me busy during slow times.
If a part time job isn't in the cards due to the time schedule, why not look into volunteering opportunities. Surely there's a library, sports league or church nearby that she's expressed interest in before.
Also, there's meetup.com, which is pretty good for expanding social circles.
Don't forget about date nights. You don't have to spend a ton of money going out. How did you guys date when in college? I bet you didn't spend alot. Or stay at home and have friends over for game night with some cards, board games, some wine and good music.
Free education for anyone smart enough to enroll
Protip: Before you start making claims that macroevolution isn't supported by science, do some research so you don't sound like a cunt.
Edit 2: 4 points 20 minutes ago... awwww yeah
Coursera An introduction to interactive programming.
Programming course in Python with heavy game focus. Oregon trail type programming included.
Looks like it started last week - perfect timing :)
Change means getting cooler or warmer than it has been previously. This will depend upon your location and the features in your area. If you have an hour or two a week to spare for the next month, take this Coursera course on the climate in the great lakes region offered by UW - Madison. It is a very reputable university, I studied math there. I think it will give you some insight.
I really liked this course: [link]
I recommend reading the course book (written by the lecturer) as you watch the videos.
You can find the part I and part II lectures on thepiratebay.
Just a PSA to all interested in how food is grown here in the USA.
I recently signed up to a free course about the US food system in relation to public health. I would recommend everybody, from skeptics to food system activists to take it. the course goes into the history and politics of the way we eat, how we grow our food, and the environmental and climate changes as a result of those choices.
the course will help those make informed decisions in the way they grow/shop/eat food, as well as filter fact/fiction while engaging in a food discussions anywhere (and such as here on reddit).
e.g., Did you know the reason why china (and possibly other countries) grow soybeans in the amazon basin? they do it in order to tap into the largest source of water on the planet, water is a scarce resource for many countries, and in order to grow enough farm animals to feed a growing economy, you need a lot of corn and soybeans, and that requires a lot of water that china doesn't have. the externalized cost of farm waste, pesticides, etc is also passed onto the amazon basin.
Science is my life. Currently doing Coursera's Galaxy and Cosmology course which began today, Computational Neuroscience and a few others.
PSA to anyone thinking of taking Computer Science 101: roughly 50% of the exercises for the class are not functional. All of the lectures work to my knowledge, and some of the exercises do work properly, but I was disappointed when I found out that not all of the content is available at the moment.
This is Jeff here, from Simply Statistics. Roger's course was designed to teach the mechanics of R. I know he made a pretty strong effort to help folks who didn't have much background, but obviously there is variation in backgrounds. He would definitely love feedback on the course.
If you want to learn the statistical component, my course in Data Analysis: [link] is the natural continuation of Roger's course. Hope to see you in that one!
Andrew Ng is rather famous for being one of the forerunners of free online education. As such, you could check out his machine learning courses, with lectures online. [link] ... [link]
Andrew Ng also works at Google X labs. Likely in conjunction with the self driving car along with Sebastian Thrun. Almost certainly in part due to seeing Andrew's efforts Sebastian Thrun is starting a full online education system called Udacity. One of the courses is AI: Programming an artificial Car. This is not what I'd call introductory though... it depends on your level of CS knowledge.
Hope that helps :D
Can confirm. R has changed what I thought was possible. I'm never going back to Excel for data analysis. Well, Excel is still convenient for the basic stuff.
I recommend the MITx course to get started off: [link]
After that, consider taking the Stanford Online course: [link]
The first one starts off with the basics and hands you datasets for you to work on, which makes it a very fun and practical class. The second one goes more in-depth into the theory and more advanced methods. I took the Stanford course first and was utterly confused because I had no background in R... but the MITx course does not assume any prior knowledge of R or statistics. I find myself going back to the Stanford page for re-learning the complicated concepts even after doing the MITx course.
If you are very mathematically inclined, you can take Andrew Ng's course: [link] This one is insanely complicated and goes very in-depth with the mathematical aspect of data analytics. Personally this one is beyond me... I can't understand what's going on.
Rice University has a Principles of Computing MOOC series in Coursera, and it's fantastic and free. It's divided in 3 parts:
An Introduction to Interactive Programming in Python. It's an introduction to programming course, but focused on programming video games. The final project is an Asteroids clone.
Principles of Computing. Covers the background you need to go full-on into algorithms, and covers more advanced Python parts like lambdas. It's basically an overview of computer science. In the projects (graded) you'll code stuff like Monte Carlo and minimax machine players, and a Fifteen Puzzle solver.
Algorithmic Thinking. Full-on algorithms course. Big O notation, complexity, all that stuff.
Here's the page of the specialization: [link]
Right now there are countless MOOCs in computer science. Check edx.org, coursera.org, and udacity.com to see what's available. You'll find iOS and Android development, cryptography, cloud computing, specific programming language courses, paradigms, etc, etc, etc.
I've been working my way through Learning How To Learn at coursera and in a short time I've noticed a marked improvement in my ability to retain information.
Take that Coursera course:
From Andrew Ng it is free.
This is a pretty good starting Point:
and have fun ;-)
There's a course of Coursera about that: [link] It explains the main problems the election system has to solve and the problems that have actually transpired during various electronic voting elections.
I took it and it made me very cautious about the whole issue.
Not really, you could get into making 3D models in blender and animating and such. However you can't do anything with that without learning how to program. If you want to mod you're stuck learning how to program. This is where I learned, this is where most people on reddit suggest to learn from.
Check out /r/learnprogramming or /r/learnpython
For R, there is recently started course which you can take for free
Andrew Ng class is a really good introduction to machine learning
Jason has also written excelent guide for starting
You sound like someone that would love to check out Coursera's An Introduction to Interactive Programming in Python which is a free seminar offered by the fantastic Coursera project.
I'm having a hard time making sure I don't enroll in tens of dozens of those myself, found this Python one today and it puts me up at 10 courses for this semester. I wish I had been this active when I was at University myself...
Coursera also offers free courses online from other university such as Stanford, Rice, and Princeton. Make sure to check pre requisites (because some do require subject knowledge). Not all courses will send you certificates of completion, and like Edx these are not accredited courses.
But they are there for those who want to learn. Which is awesome.
You might also like the free cryptography online lessons by Stanford: [link]
too bad it started already like a month ago but I'm sure they'll do this again. These courses come with practical assignments and exams (ofcourse multiple choice), all for free! Some of the top guys of the field are at Stanford, and they give many more courses.
Check out this free course from Stanford, Software Engineering for Saas. Starts on the 18th and is quite promising.
I'd agree with looking at Sinatra. Tons of sties and apps have used it, many of them integrating existing APIs.
Forget about the E-PREP nonsense, learn programming online, Coursera and edx are both very solid places to learn. Some of the courses are the exact same courses that undergrads at places like MIT and Berkeley attend.
I can't recommend this Coursera online course enough: [link]
I'm not sure if you're looking for something that extends through 6 weeks, but it's definitely an amazing material that covers all the basics. The course already started on April, so you will have a month of catching up. But it's definitely doable if you have time.
The second recomendation is The Recording Revolution blog: [link]
Graham is very passionate about his teaching. It's not so much about the techniques, but more about the right mentality when recording/mixing and what to aim for. He focus on home recording, so it's perfect for you who is just starting with minimal gear.
I strongly recommend going through either Dan Jurafsky and Christopher Manning's Coursera or Michael Collins'. They both treat the field from a different choice of emphasis on theory, and the material in both ought to be mastered by anyone looking into doing research in NLP. It'll give you the general idea and common frameworks used, and a surface level understanding of the many different problems encountered.
Ongoing research is often more about applying the most recent algorithms developed in (statistical) machine learning, so you'll also need to dig into more general machine learning courses if you aren't familiar with them.
Sedwick teaching algorithms in java, intermediate level, free, next session starts mid june. The problem sets are difficult, but regardless just watching the lectures would be informative.
First of, recording a drum kit with one mic, especially the kick with a sm57, can turn out to be quite the challenge. I'm not saying it's impossible, but depending on the sound you're going for it might take a while to get enough low end and punch through such a general purpose dynamic mic. Just so you know.
Try to imagine as closely as possible the sound you're going for. Look for reference tracks not just as a whole track but with a specific instrument in mind. As in "I like the bass sound in this track" or "the snare has so much punch". After you've done that for all your tracks, start recording. After a while, you will hear just what the mic and the room you're recording in sound like and how close you can come to your imagined sound. Adjust your expectations accordingly.
As for the recording order, I recommend this (assuming the songs are written on guitar): guitar guide track -> drums -> bass -> guitar -> guitar overdubs -> vocals -> mixing. When you separate the parts of your projects, you won't get lost as easy. In Ableton, decide early if you want to work in Session or Arrangement view. I recommend arrangement view.
If you're feeling stuck, Ableton have just released a book on production strategies which I have heard very good things about.
Check out /r/ableton and /r/abletonlive, the free courses on Ableton and music production on coursera.org and start experimenting!
Check out Algorithms 4^th Edition by Robert Sedgewick and Kevin Wayne (link to online version).
It is basically the standard book on algorihms and data structures.
There also is a two part Coursera course by Sedgewick and Wayne.
Just a PSA for anyone who didn't go to uni or went and didn't do an argumentation/reasoning skills/informal logic course. Coursera's offering Think again: How to Reason and Argue, the next one starts on the 5th and lasts 12 weeks, it's free of course.
If you ever wanted to know how to refute a straw man or wondered what an ad hominem attack was, you'll get that and more. Challenge: I scored 87%, probably could have scored higher with a bit more effort.
If you're interested in this era of history, the things that fed into it and the outcomes, there is an excellent Coursera course that I can recommend:
It's a truly fascinating 14 week dive into what has shaped the world since 1760.
Take a look at the upcoming Bioinformatics MOOC; they run "MOORs" along with the MOOC, where a MOOR is a "massive, open, online research project". Not only are they projects you can work on at home but they are genuine research problems with scope for new work.
If you wanted to you could of course do the MOOC, but there's no obligation, and you can access the research project without completing the MOOC. You do not have to wait for the next MOOC to start (September 15th), you can access the archive for the previous course and look at the research projects there, but it might be more fun to take part in the new ones so you can work with others.
You might also want to check out Rosalind, some of the problems there are quite good fun and will allow you to build up a portfolio of code solutions.
I did that course, but I really don't know why they designed the course so bad. The prof seems to be a nice guy but he can't teach. Basically he is just reading over a text file. I find that course really boring.
I really liked this course on Coursera. Although OP is not looking for video tutorials, but I really recommend this course. You get to build easy but functional apps really fast. I followed this course and another youtube channel and in 2 months I built an app which connected(via BT) to several sensors, uploaded the sensors data to a dropbox account synced with another device and a web application. and alot of other features on the android app. My major is mechanical engineering and this was my first real app. But that course on coursera really helped me to understand basics of android in less than 10 days.
Here's the ones I'm interested in doing:
This course on coursera is really good too. It takes a lot of works though and has been started for about 3 months, but I think you still have access to lectures and exercises
The state of being tired is a state of hormones in your brain, if you resist their insistence on getting some sleep your body will react with adrenaline and cortisol causing a 'second wind' effect of alertness and activity until the sleep hormones can cycle back to forcing you down.
No guarantee it will 'balance' out and you'll just rush home to bed earlier at the end of the day.
Apparently there is no making up for sleep deprivation, if you didn't get sleep, your brain didn't get to heal and grow, and you just got another day older and lost another chance to heal and grow your brain.
Early chapters of This free class on learning how to learn illustrates about how important sleep is in clearing our brains of stress hormones and creating neural networks for new information. Check it out.
There is a free online course from Berklee College of Music that helped me progress a lot. It's called Developing your Musicianship and it starts again this week:
I think you are describing confidence and intellectual curiosity. To me laziness would describe following a procedure without bothering to understand it and being done when you get an answer.
Anyway, I know it is common to talk about being lazy, but I think a lot of time people are talking about a strong aversion to menial and boring work. Consider, for example, some boring task that takes one hour, that you'll have to perform once week for a couple months, and the alternative of spending two or three days automating the task. Even if it takes more time to automate, at least that wouldn't be painfully boring work! You might even learn something new. On the other hand, there are people who really wouldn't be bothered by doing repetitive work, and who would not see the process of automating something as an opportunity to be creative.
To the OP's question, I highly recommend the Stanford/Coursera course on Mathematical thinking. It places a strong emphasis on the distinction between matching patterns in order to get the answer so that you're done with a problem, and really engaging with the material.
In many other disciplines I think there is a focus on being results-oriented, and where having high standards and being mindful or rigorous is discounted as being frivolous or playing around. A lot of the joy of mathematics is that doing things well is the result you are ultimately looking for, and that you don't have to settle for shortcuts that are sloppy for pragmatic reasons.
Khan Academy is a fantastic place to start. Especially with math. Last I looked he had some good intro to chemistry and physics stuff too. (As well as a smattering of other subjects.)
Coursera is another excellent resource for auditing some college level courses for free from some of the best colleges. But don't be intimidated, most 101 classes are just high school senior level.
Then there are probably dozens of YouTube channels for just such a purpose. Try searching for specific topics you're interested in.
This is what I got from a quick search for "youtube intro physics"
Free online courses, some with certificates. Some from top universities. You can take them in your spare time, for the most part. I'm not soliciting or anything -- I found them through someones post on reddit and I've taken and enjoyed a few courses.
I pretty much don't have anything else to say, but I hope this helps?
Here's a awesome roadmap by Swami Chandrasekaran on becoming one.
>Are there any books/textbooks out there that help me learn?
How about the Intro to Data Science on coursera?.
>Also, are there any data sets out there that someone like me can practice on?
Perhaps you will have some fun on kaggle. Interesting problems + helpful community for learning.
bitcoind/RPC is pretty simple. The documentation is here:
I would highly recommend you learn how to construct a transaction manually:
Lastly, everyone working on bitcoin tech should really understand some basics of cryptography:
There's a whole lot more you'll need to learn, but these three and what others are recommending here should get you off to a pretty good start.
As a side note, I also recommend Coursera's course on Learning How to Learn or just the professor's book. It's based off of scientific research which motivated me to follow it and has worked for me really well so far.
"A permanent bailout state" is what Basic income should be. The Fed (which created some $3 trillion on balance sheet, and many more - at least $16 trillion more, according to Bernie Sanders's Fed Audit - off balance sheet) proved it can backstop any amount, with no taxpayer funding needed. The "Austrian school" predictions of Zimbabwe-style hyperinflation proved to be hyperbolic paranoia.
The Fed should have bailed out individuals instead of banks. Even Republican darling Kenneth Rogoff says as much:
"Without question the best and most effective approach to the problem would have been to bail
out the subprime homeowners directly, forcing banks to take losses but keeping them manageable.
For an investment of perhaps a few hundred billion dollars, the US Treasury could have saved
itself from a financial crisis whose cumulative cost, counting lost output, already runs into many,
many trillions of dollars. Instead of “saving Wall Street,” a subprime bailout would have been
targeted, almost by definition, at lower-income households. But unfortunately, this approach too
would have been politically impossible prior to the crisis."
The question of political feasibility is crucial. We can fund a Basic income without taxes, but everyone, including the Fed, is afraid to say it. I suggest that we, the People, educate ourselves about how money is created all the time by banks, and how we can use that creation tool that the private sector exploits so well, to benefit us.
Coursera's Economics of Money and Banking MOOC, Parts One and Two, is a great place to start.
/u/cyrusol said it, go for both.
[link] is starting up at the end of the month.
Looks like a great into to C#
From a non-technical perspective... With any job, your heart has to be in it, or else you will lack the drive.
My background is the other side of IT, on the networking side. I can't speak for programming but the 'dumbasses' on this side of the fence are usually the ones who know a little about computers, find a way to pound through cert exams and end up as
I've worked with tonnes of people that don't have a degree in their field. One of my old IBM Team Leads had a Masters in BioChem. Didn't mean he didn't do a great job at IT.
I've trained legal secretaries to run helpdesk, mixed results, but did make some successes.
Its not a matter of having a degree in a related field (Although it helps plenty) its being able to sit down, know what the departments goals are, and understanding the work you and your colleagues need to execute.
With the right motivation, any one of us could go from day 1 'dumbass' to being able to conduct--at very least, debugging by a couple months in.
For those interested, Coursera is hosting an Algorithms course taught by Kevin Wayne and Robert Sedgewick, authors of a fairly well received Algorithms book.
I only graduated in 2011 and I've done far too many different things to pigeon hole into a single job title: embedded software, ASIC design and verification, Java web apps, training and migrating an organization from SVN to Git, kernel development, physics simulations, reverse engineering.
Right now, I'm in a position where I'm ostensibly adding features to a C++ application, but instead I'm working on Linux driver development because that's what needs to be done right now.
I think having a penchant for learning and a wide variety of experience has been key to my success. To that end, of course, the whole of my undergraduate experience was important, but if I had to pick a single course, it would be Programming Language Paradigms (PLP).
PLP was a course for upperclassman (due to prerequisites) in which we studied many different programming languages and their different paradigms. We studied Python, Haskell, and Go in my particular section followed by group projects in yet more languages (my group chose Erlang). The course was as much about learning effectively and being able to quickly change your frame of mind as it was about the actual languages I mentioned. That experience in quickly learning new skills has been invaluable to me.
Coursera has a similar class called Programming Languages that's aimed more towards freshmen or sophomores. I enjoyed it as well.
MIT has a lot of course lectures online.
In addition, you have Coursera. They run courses over 6-12 weeks where you follow lectures and do hand-ins. At least some courses runs again in intervals
I highly recommend the course "Functional Programming Principles in Scala". :)
If anyone is interested in learning a bit more about music production for free you can do as I did and sign up for the Introduction to Music Production at Coursera.org. Here's the link:
And here's another course that seemed worthwhile:
Introduction to Digital Sound Design
Book: Introduction to Linear Algebra
Video: Gilbert Strang's MIT Linear Algebra Opencourseware
Free Online Course: Coursera: Statistics One
Everyone learns something on the job. Learn on the job while you can.
Take notes, by hand, to form more solid memories of what you are doing and learning.
There will be a time eventually when you have to tell other people how to go about doing their jobs because you spent the time reading all those docs and learning the things you learned.
Give yourself a long term project that challenges your programming skills, focus on the processes not the final product. Maybe write your own tutorial for something.
Get a hobby outside of coding so you have things to look forward to besides your coding school/career.
> This makes me very anxious about getting a job as a software engineer in the industry
Focus on the process not the product.
Maybe relax with this great class on learning how to learn
I'm going to make an ideological suggestion. There are a lot of tools available in R, and many of them are really amazing (ggplot for example). However, as a programming language, R is terrible. The things it does well it does really really well, the things it doesn't do well it does really really poorly (e.g. error messages, Rs are the worst).
So, if you haven't already, learn the following things:
0. How to use the *nix command line
0a. Some basic shell scripting
1. Pick a workflow engine and go with it. I like bpipe, but there are plenty of others to pick from
2. And then R.
Keep your R scripts short and single-purposed, rather than trying to do everything in one script. Then chain them together using either basic scripting or a workflow engine.
A typical RNA-seq workflow involves several steps, most of which will not be done in R. You'll be using other tools (like Tophat or cufflinks) which are designed to be called from the command line. So that's a good place to start.
Just because I don't want to be a total douche: R coursera course, R edx course
I went from data analysis in financial services to data analysis in marketing at a different organization. While the comment about Excel has merit for some tasks, I still use SQL for the vast bulk of my work.
In particular, I'm using it to do response rate and lift calculations, analysis in identifying target audiences, and various forms of exploratory data analysis.
When you get into performing tasks like basket analysis and affinity groupings, Excel will simply not be able to handle the million+ (100Million+) rows. However, in many situations, using SQL to generate some high-level aggregates that you then put in Excel for nimble ad-hoc analysis can be a great combination.
If you want a good starting point for what you can do with SQL for marketing, I would recommend Data Analysis Using SQL and Excel. It won't teach you the language, but it will show you the basics of what's possible, and how to implement various solutions once you have a solid, foundational understanding.
As for another language, I would recommend picking up R. It's very useful, free, and has great learning resources available. Johns Hopkins offers a free 4 week intro on Coursera.
Depends on you, mostly.
It's definitely one of the hot areas right now and is likely going to stay that way for at least a decent bit. A lot of the skills are also transferable into areas if you want.
Because of that, there are a lot of people getting into the field that aren't very well suited for it. They've got minimal training, no experience and just don't have the good instincts for it. We get a ton of CVs like that for our open positions at my company and we just put them aside. We just don't have the time to try to train someone who might not work out.
So you need to show that you're a step ahead of the others. Finishing an online course doesn't mean that much. Show that you finished the full Coursera Data Science specialization ([link]), including the capstone and I'll definitely be taking a second look. Building a presence on online forums (biostars, bioconductor help, etc.) of the tools you use is another way of building a name for yourself.
Recurrent and recursive neural networks are very promising models for processing text, there have also been recent papers popping up using convolutional networks for text too. To learn more about them, Hinton's coursera is an excellent resource and Yoshua Bengio's deep learning book is starting to fill out with great info as well!
On this specific dataset, unfortunately, it's small (25K labeled examples) for these much more complex models to really shine. They overfit a good deal and the results you get are just competitive/comparative with the code above.
When you initialize the supervised rnn with embeddings learned by a language model rnn leveraging the additional 50K unlabeled examples (similar to what was done with word2vec in this tutorial), it's getting about 90% accuracy which makes it better than any single linear model that I'm aware of in the literature, but still a bit worse than an ensemble of NB and SVM at 91%.
Do you feel like learning how to write another type of music could help you? If so, there is a free online course starting on January 13th, on Coursera. It's an introduction to classical music composition.
Prerequisite are: Some knowledge of music theory, which you seem to have.
More information here: [link]
I don't know if the course is actually good, but I've taken some excellent ones on Coursera in the past, so I'm going to try this one out too.
Are you only interested in books?
There are some great videos/online courses out there (I'm thinking of thenewboston's videos or this course on Coursera).
Btw, you should look for Java books first if you don't know it yet.
Berklee is offering a few free online music courses, starting Oct 13, including one called Introduction to Guitar: [link] (there are also 4 others that sound good too).
I did the one called "Developing your Musicianship" and it helped me a lot.
Also, to improve your practice routine, I suggest you go to justinguitar.com and look at his recommendations for practice at various level, until you find what fits your current needs:
the free algorithms course on coursera. Even if you just read the book, which is free on the book site + extra materials, you will have learned a lot of important concepts applicable to all sorts of programming problems
A Computer Science degree doesn't really teach you how to program. It teaches you the science behind computing and a fundamental way of thinking. It exposes you to a wealth of information, then leaves it up to you to take in that information and make yourself become a competent real-world developer.
You will never stop learning as a programmer. Get used to it. There will always be something you don't know. Teaching yourself new things will, more than anything, be the most important skill in your career.
That said, we live in an amazing time. Especially for aspiring computer scientists. An entire undergraduate CS course load can be found online for free at various places if you look around hard enough. Check out Coursera, Udacity, Kahn Acadamy, Youtube. Information is everywhere. But not on reddit. Find video lectures from professors that you find easy to listen to. Make learning it a domino effect.
Try these. The two algorithm courses are top notch if you're at that point.
Edit*: And ask your SO for explanations when something doesn't make sense. He/she should love teaching about it. I know I would if my SO was interested in computer science. Also take what other developers say with a grain of salt. Even me. Most act like they know what they are talking about, and many can be incredibly convincing. But very very few really do. If your SO can't explain something to a point where it makes sense, then he/she is probably faking their own knowledge. Don't put yourself down for other people possibly pretending to know more than they actually do. If someone thinks using a text editor over an IDE somehow makes someone inferior, I'm forced to lean towards that someone not knowing anything at all.
Take an online class at Coursera
Two courses that started yesterday:
One that started today:
I'm taking one that starts in January on "Fundamentals of Personal Financial Planning"
Don't forget: Go Learn Yourself Some Science.
There are great science-explaining channels on youtube, or dedicated pages such as the Khan Academy or Coursera. Some Universities also started offering free-for-everyone online courses, some even including exams and certificates in case you pass. There is little more fulfilling than working on your understanding of how the universe works.
I took a previous incarnation of that course. It's pretty intense but fun. It does not touch on much nootropic-related material though. More internal cell electrochem and mechanics than external neurotransmitters, but still relevant. Some statistical math is expected (Bayesian stuff).
There was a Coursera course called "Visual Perception" from Dale Purves (the neuroscience textbook author) that was more perception-related and less math intensive. You can view the older course videos and sign up to be notified of reruns here:
And there was a neuro-chem related course called "Drugs and the Brain" from Dr. Lester at Caltech. Closer to the subject at hand but that was a terse and difficult course. Parallel study of a good neurochem text would be helpful. Older course videos probably still available:
Last but not least: The one that may be the most fun for non-scientists is "Synapses, Neurons and Brains", which was sponsored by Idan Segev at Hebrew University. Very well presented. Everyone loved that course. It has not been scheduled to run again this year, but you should be able to get to the videos here:
Maybe his problem is with the term "wage", which implies working to get the wage? Maybe Ng isn't yet familiar enough with the idea of a Basic income to get the terminology right?
I think Ng, whose Machine Learning MOOC was one of the first and one of the best, means by "giving people the skill sets" more free MOOCs. The work comes in making them more customizable to each individual's customized needs, I think.
Then (my own idea, not Ng's as far as I know) hold lots of challenges to stimulate individuals (on a Basic income, or not) to work on great unsolved problems, with disruptive ideas. Take the best ideas and let the private sector do what it does best: incrementally innovate.
Fund the basic income at zero cost, through the Fed. The resulting innovation from the people freed from having to work for a boss, to work on challenges (of their own or others' devising), will increase standards of living faster than ever before.
Why would inflation occur, when knowledge is being advanced faster?
edit: Don't believe me? Check out the CGP Grey video, Humans Need Not Apply. If you're feeling suddenly replaceable, I definitely recommend taking (free) classes on Machine Learning (Stanford/Coursera) or Artificial Intelligence (Berkeley/edX).
Coursera has some awesome classes for this type of stuff. There's often classes for both the technical side of things(basic SQL up to query optimization and database building concepts) and the mathematical side of more advanced analysis.
General warning: The "math" side of analysis(building models etc) is much more academic-gated. If you want to pursue that role, a master's degree might be in your future. The technical side of things, especially at the low level of just writing queries, is much more lax about qualifications. I got my first SQL-related job by passing a skills test with a recruiter. Had a degree in Biology, some general math skills, Excel, and not much else at the time.
Read the NLTK book. It is very hands-on and will give you a sense of what kind of stuff is out there. It isn't state-of-the-art though. (Stanford's tools come closer.) Manning & Schütze or Jurafsky & Martin are also good choices, and provide a really solid intro. Your library likely carries at least one of those.
Alternatively, check out this course on machine learning on Coursera.
Since you are still in the inspiration stage, perhaps consider exploring your interests without taking time out of your working life or forking over large amounts of tuition dollars.
For example, go onto Coursera and take an astronomy course, a physics course, and if you are considering sciences and astronomy, especially the calculus course.
If your interest is still there, then maybe its time to to take on university study. If you got no degree, consider a Bachelor of Science. If you got one, just take the courses that interest you (or a university may allow you to do the degree in two years).
If you want to be an astrophyicist, you will likely be looking at a Ph.D. after (usually 4 - 6 years), as well as post-docs (another 4 - 6 years in common in the sciences).
Some jobs a B.Sc. in astronomy would qualify you for though are:
Some engineering positions
As for scholarships, there are ones for everything under the sun - female, mature student, financial need, etc. You'd have to talk to whoever is in charge of student finances at your university of interest for more details.
check coursera introduction to python
week 1 is about to finish but you can still join and learn everything they teach, it's completely free with an optional paid verified certificate
Be brave and intentional in my actions.
Complete this set of data science courses as a first step toward a more data-focused career. I'm in an entry-level marketing position right now, but my passion is for analytics and metrics.
Maintain a fitness schedule that balances rock climbing and strength training. Right now I'm lifting 3x/wk while taking time off of climbing due to an overuse injury, and next year I will maintain lifting at 2x/wk while reintroducing climbing-specific training 3x/wk.
Keep in better touch with friends who have moved away by setting aside time to make phone calls and write letters.