AI News, Data science intro for math/phys background

Data science intro for math/phys background

After posting What I do or: science to data science I got a lot of emails on how to make this transition.

In short: All projects required me to learn something new - be it a library, a machine learning model or a software tool.

From my perspective the whole process looks that way: And everything needs to be done in a reproducible way - so others can interact with your code, or even run it on a server.

even look at this tweet - while humorous2, it shows a balanced list of typical skills and activities of a data scientist:

If you want to learn more about what is data science, look at the following links: When you have some academic title, no-one will question your intelligence.

From my experience, you need to fulfill two requirements: Most data science things are simple and at the point that you are able to use R or Python you can start working, gradually increasing your knowledge and experience.

So (from Academia to Industry linked below): While having a strong coding ability is important, data science isn’t all about software engineering (in fact, have a good familiarity with Python and you’re good to go).

Things need to work, and there is little difference if it is based on an academic paper, usage of an existing library, your own code or an impromptu hack.

That is, in data science the emphasis is on practical results (like in engineering) - not proofs, mathematical purity or rigor characteristic to academic science.

Rather than being a complete record or all positions, awards and publication, it is a short (typically 1 page) summary of the main skills and the most important positions/accomplishments.

In any case, take a look at: If you need learn basic algorithms and data structures, I recommend: If you get no technical questions, it may be a red flag. If

you get only software engineering questions, it may be a sign that they want to hire a programmer, not - a data scientist (no matter what their job calling says); and

won’t point to a general tutorials - there are tons of it and personal preferences vary (MOOCs, interactive courses, websites, textbooks, …) and I tried to link only to things I recommend myself. When

Learn to be comfortable with Python (installing packages, loading, saving and transforming data, etc) - links below may help: You need some basic linear algebra (vectors, matrices, SVD, …), calculus (exp, log, differentiation, integration, …) probability (independence, conditional probability, …), but if you are from natural science background, you already know that. It

does not mean that you know all - it just means that right now you have mathematical skills sufficient to be an employable data scientists and you are able to read about other methods, algorithms, etc.

If you need to get a real dataset suitable for working with a given machine learning algorithm, there is a wonderful collection: For statistics, screw learning by heart various statistical distributions and tests - you can easily look them up later.

For the latter I recommend: It’s a fast changing field - I am constantly tracking new libraries and updates to ones I am using.

get me wrong - there are great resources, it provides feedback (otherwise it is hard to tell if your solution is good) and some people find it really engaging. But

Also, this way it is a complete data science - from asking questions and getting data to presenting the results in a meaningful form.

So don’t be afraid to asking about or for anything, starting talking to people etc - on the average it will be much better than taking a passive posture.

Here is a random list of starting points I consider interesting: EDIT (Feb 2018) - some of my new introductions: This blog post started as emails, and went through a stage of an extract of emails (shared on Google Docs).

How to start learning data science

You’ve probably read numerous articles telling you how to start learning data science.

They tell you all the skills you need: learn machine learning, visualization, data wrangling.

It’s like standing at the base of Everest, saying to yourself, “how the hell will I climb that?”

Look, there are lots of people out there that aren’t actually skilled in data science, telling you how to start learning data science (I’m looking at you, HR professionals).

skills, used either by a select few specialists or used only occasionally by the average analyst.

Focus on the skills that are easy to learn, easy to implement, that yield the greatest results.

There are a few reasons I recommend R: To be fair, I think you could also make a strong case for learning Python (it came in just behind R in O’Reilly’s list of data science tools).

And as I mentioned in the introductory dplyr tutorial, dplyr’s syntax is easy to learn, easy to use, and operates in a way that streamlines your workflow.

(For an example, see this section on dplyr + ggplot2.) Data visualization is the fastest, easiest means of finding insight when you’re first starting out.

It’s also one of the most versatile tools: you can use visualization for data exploration (finding insight) and communication (communicating what you find to business partners and executives).

When you’re starting to learn data manipulation, I recommend mastering the 5 basic dplyr verbs, as well “chaining”

And then there’s one guy who is highly specialized in the most arcane of skills: pitching (specialization in throwing).

Strategically, you’re much better served learning visualization and data manipulation: they are easier to learn, easier to implement, and the jobs are more plentiful.

It’s the hardest to learn, the hardest to implement (both because it’s difficult and because it requires a foundation in data manipulation and visualization), and there are fewer jobs requiring machine learning.

I’m just saying that you should learn it after you’ve built solid foundation of data visualization and data manipulation.

Stacking (RapidMiner Studio Core)

Stacked generalization (or stacking) is a way of combining multiple models, that introduces the concept of a meta learner.

It does this by using performance on the testing data to combine the models rather than choose among them, thereby typically getting a better performance than any single one of the trained models.

Evaluating the prediction of an ensemble typically requires more computation than evaluating the prediction of a single model, so ensembles may be thought of as a way to compensate for poor learning algorithms by performing a lot of extra computation.

This flexibility can, in theory, enable them to over-fit the training data more than a single model would, but in practice, some ensemble techniques (especially bagging) tend to reduce problems related to over-fitting of the training data.

Although perhaps non-intuitive, more random algorithms (like random decision trees) can be used to produce a stronger ensemble than very deliberate algorithms (like entropy-reducing decision trees).

Using a variety of strong learning algorithms, however, has been shown to be more effective than using techniques that attempt to dumb-down the models in order to promote diversity.

8 Essential Tips for People starting a Career in Data Science

The idea was to create a simple, not very long guide which can set your path to learn data science.

This guide would set a framework which can help you learn data science through this difficult and intimidating period.

A data visualization expert, a machine learning expert, a data scientist, data engineer etc are a few of the many roles that you could go into.

point to keep in mind when choosing a role: don’t just hastily jump on to a role.

The demand for data scientists is big so thousands of courses and studies are out there to hold your hand, you can learn whatever you want to.

Finding material to learn from isn’t a hard call but learning it may become if you don’t put efforts.

What you can do is take up a MOOC which is freely available, or join an accreditation program which should take you through all the twists and turns the role entails.

The choice of free vs paid is not the issue, the main objective should be whether the course clears your basics and brings you to a suitable level, from which you can push on further.

The most straight-forward answer would be to choose any of the mainstream tool/languages there is and start your data science journey.

Now that you know that which role you want to opt for and are getting prepared for it, the next important thing for you to do would be to join a peer group.

Taking up a new field may seem a bit daunting when you do it alone, but when you have friends who are alongside you, the task seems a bit easier.

The most preferable way to be in a peer group is to have a group of people you can physically interact with.  Otherwise you can either have a bunch of people over the internet who share similar goals, such as joining a Massive online course and interacting with the batch mates.

These Data Scientists are really active and update the followers on their findings and frequently post about the recent advancement in this field.

But there may be many resources, influential data scientists to follow, and you have to be sure that you don’t follow the incorrect practices.

Gradually, once you have got a hang of the field, you can go on to attend industry events and conferences, popular meetups in your area, participate in hackathons in your area – even if you know only a little.

You get to meet people in your area who work actively in the field, which provides you networking opportunities along with establishing a relationship with them will in turn help you advance your career heavily.

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