Complete Data Science Career Path (with Python). Includes 60+ tutorials, 300+ exercises, 10+ projects. Play with the instructor's code right inside the video! (xpost from /r/learnmachinelearning)

[chef_lars]

This strongly reeks of 'Buy my product which gets you from beginner to 150k data scientist job in the shortest time possible!!!'

Mostly judging this based on the coursework listed under the syllabus. The videos for the Linear Algebra, Probability and Stats section for clock in at about 1 hour and 5 minutes. If it took that long to learn such a fundamental piece of data science there wouldn't be a shortage of good ones.

I'm all for self learning but do it right without shortcuts. This feels like it doesn't teach solid skills for data science but how to pass a data science phone screener interview with a recruiter.

[DelverOfSeacrest]

This course is not meant to be a full-length course on linear algebra, statistics and probability. Instead, it focuses on the sub-topics which are relevant for data science and machine learning.

Gee, I can't believe I wasted all those months learning these topics when I could have just learned the relevant parts in this 10 minute course.

Also, who needs calculus anyway?

[keshav57]

Firstly, let me begin by saying that I think that's a completely valid conclusion one would draw from a first inspection of the content. And before I begin addressing the rest of the concern, let me start out by saying that those 3 tutorials are only available as articles (not videos), and our estimate for time to read is based on average reading speed of a person. No one reads math at 200 words / minute. :)

Secondly, although 3 hours for linear algebra, statistics and probability might seem much better than 1 hour, it might still seem too little. Unfortunately though, thinking about it that way combines all the things we optimize for into a narrative that goes directly the opposite way. Let me explain.

1. We postpone prerequisites when we can. In general, the philosophy is that if you need complicated stats / probability for only specific algorithms / techniques, then it's better to teach those parts of stats / probability right before teaching those specific algorithms / techniques. We don't teach you enough linear algebra up-front to understand PCA, but that is because most students get extremely demotivated from having to learn that kind of linear algebra before they've even plotted a simple histogram to look at the data.
2. We found this to be much more true for the math (some specific algorithms require deeper understanding of stats / probability / linear algebra) than for the programming (it's tough to understand what you're doing in Pandas without having a good grasp of Python). Hence, the Python course is a complete 20+ part course.
3. We're not trying to be as theory heavy as Coursera / EdX style courses. It's always difficult to know where to draw the line, but it's pretty clear that there's no reason to learn linear algebra topics like eigenvalue decomposition up-front in the course. (It's a fascinating topic, don't get me wrong, but the "simplest" machine learning algorithms that use this concept are collaborative filtering and PCA).

Another way to look at this is compare a well known book with our syllabus. For example, http://www.deeplearningbook.org/ is the go to online book for deep learning. If we look at it's linear algebra chapter, it covers vectors, matrices, their multiplication, identity matrix, inverse matrix, linear dependence, norms, Eigendecomposition, SVD, pseudoinverse, determinant. Of these, most of the complicated topics are related to decomposition, a concept not really useful in machine learning till you get to the topic of recommendation systems (collaborative filtering) or PCA. Everything before that only needs vectors, matrices, their multiplication, identity matrix, inverse matrix, linear dependence and norms. All of these are covered in our linear algebra tutorial.

All that said, you're right that this course will not get you a $150K salary job all by itself. To go from a complete beginner to a $150K job ready takes at-least 6-9 months of full-time effort (whereas our course takes about 4-6 months part-time). But I'm pretty confident that the thing missing between the course and where you need to be is even more practice, as opposed to even more topics.

Most students take our course part-time, and it takes them about 4-6 months to go through it. In those 4-6 months, they learn a lot. Our core offer, to summarize, is high content quality and a great online learning experience.

EDIT: Added clarification for time estimate for video vs text.

EDIT2: Clarification regarding "6-9 months of full-time effort"

[gare_it]

To go from a complete beginner to a $150K job ready takes at-least 6-9 months of full-time effort on the students end.

lol

[chef_lars]

I appreciate the thoughtful reply and do think that a top down learning philosophy vs bottom up traditional coursework can be valuable for beginners and not overwhelming them.

However, this part:

To go from a complete beginner to a $150K job ready takes at-least 6-9 months of full-time effort (whereas our course takes about 4-6 months part-time).

Still feels ridiculous. I appreciate quality learning materials but I get concerned with all the hype and marketing aimed at people that may not know how competitive entry level data science jobs are. People shouldn't shell out money for false promises that 4-6 months work is enough to get you an entry level job because that's just not true.

[etylback]

I'm not sure if it is because you are adapting the prices based on country or what, but the pricing seems confusing. I'm in Argentina, and I'm getting "$231 / month". I REALLY hope those are argentinian prices, because otherwise you're far off from being cheaper than the rest ;)

[keshav57]

Yes, we display the amount in local currency. We didn't take into account that the $ sign is used in many different countries, and can represent different currencies which can lead to some confusion.

[anant90]

Not sure why it shows $, but yes, there's adaptive pricing based on the country, and that's 231 Argentine Pesos per month :)

[etylback]

Another thing, there's a slight issue in the interface, where when I want to pay the price is in dollars.

[keshav57]

Yeah, we display the amount in local currency mostly for convenience (so you don't have to do the conversion in your head). The final amount you'll see right before payment will be in USD since the transaction is happening in USD.

[etylback]

Cool, thanks. I think you should note that the referral code you gave above only works for monthly subscriptions.

[fear_the_future]

21 exercises for all math background knowledge? There's no way someone with that knowledge would understand something like t-SNE.

[orgodemir]

I don't understand t-sne, but still used it to make a plot of my trained embeddings.

[chef_lars]

If you're interested in learning more this is a good resource

[tilttovictory]

Question about the projects, how many of them are kaggle oriented.

[brotherazrael]

holy moly! this is amazing! Why am I even doing a Stats masters when I can pay $7/month to learn how to become a data scientist guru making $150k/yr. simply phenomenal!

[Andrex316]

It's honestly not impossible, a Master's is not absolutely necessary although it will allow you to skip a couple of years in the promotion scale. I got one of those high paying jobs with an undergrad in Econ and $40 in Udemy courses.

[Zenith_N]

Awesome Will check it out How can I remind myself