AI News, Fake data science

Fake data science

To add to the confusion, executives, decision makers building a new team of data scientists sometimes don't know exactly what they are looking for, ending up hiring pure tech geeks, computer scientists, or people lacking proper experience.

robustness, design of experiments, algorithm complexity, dashboards and data visualization) Fake Data Science Examples Here are two examples of mis-labeled data science products, and the reason why we are interested in creating a standard and best practices for data scientists.

Each chapter starts with a very short introduction in simple English (suitable for middle school students) about big data / data science, but these little data science excursions are out-of-context, and independent from the projects and technical presentations.

It has a bit of old statistics too and some nice statistics lessons on robustness and other stuff, but nothing about six sigma, approximate solutions, the Lorentz curve, the 80/20 rules and related stuff, cross-validation, design of experiments, modern pattern recognition, lift metrics, third party data, Monte Carlo simulations, life cycle of data science projects, and nothing found in a MBA curriculum.

Ironically, this online test is the same for everyone (I double checked), so technically, you could first take it using a fake name, save the questionnaire, then pay someone to answer the questions, then take the test again but this time with your real name - and complete it in just 30 seconds and get all the answers correct!

Aspiring Data Scientists! Start to learn Statistics with these 6 books!

David McRaney introduces one sad but true fact of life: that our brain constantly tricks us and we are not even smart enough to realize it.

It points out classic mistakes like the self-serving bias, the availability heuristic, the confirmation bias, and it also shows why people tend to be tricked by fake news, by scams or why people do not help when seeing someone having a heart attack on a busy street.

Being aware of these biases should be basic, but I see even the practicing data professionals are falling for them from time to time… (I wrote a detailed article about Statistical Bias Types.

Think Like a Freak shows us how critical and unconventional thinking can lead to huge success… and, hey, that’s something that as a data scientist, you should practice day by day.

The book lists a bunch of case studies from everyday life, goes into details and analyzes why a solution for a problem is good or bad.

A good teacher turns mathematical equations into mystical puzzles, probability theory into detective stories, and linear algebra into the ultimate solution for all the big question in life.

Reading it, you can easily understand basic concepts like mean, median, mode, standard deviation, variance, standard error or the more advanced things like the central limit theorem, normal distribution, correlation analysis or regression analysis.

On a topic this complex as data science, I think it’s worth to see different angles and have things explained by two different data professionals.

Oh, and I almost forgot to mention that Think Stats is available for free in PDF format, here: Or you can buy the book: here (affiliate link).

If you want to learn even faster, check out my new 6-week online data science course: The Junior Data Scientist’s First Month If you are missing something from this list, let me know in the comment section below!

Practical Statistics for Data Scientists: 50 Essential Concepts 1st Edition

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10 Free Must-Read Machine Learning E-Books For Data Scientists & AI Engineers

So you love reading but can’t afford to splurge too much money on books?

We begin the list by going from the basics of statistics, then machine learning foundations and finally advanced machine learning.

One of the stand-out features of this book is it covers the basics of Bayesian statistics as well, a very important branch for any aspiring data scientist.

Authors: Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani One of the most popular entries in this list, it’s an introduction to data science through machine learning. This book gives clear guidance on how to implement statistical and machine learning methods for newcomers to this field.

Authors: Shai Shalev-Shwartz and Shai Ben-David This book gives a structured introduction to machine learning. It looks at the fundamental theories of machine learning and the mathematical derivations that transform these concepts into practical algorithms.

Following that, it covers a list of ML algorithms, including (but not limited to), stochastic gradient descent, neural networks, and structured output learning.

It takes a fun and visually entertaining look at social filtering and item-based filtering methods and how to use machine learning to implement them.

Authors: Anand Rajaraman and Jeffrey David Ullman As the era of Big Data rages on, mining data to gain actionable insights is a highly sought after skill. This book focuses on algorithms that have been previously used to solve key problems in data mining and which can be used on even the most gigantic of datasets.

It starts off by covering the history of neural networks before deep diving into the mathematics and explanation behind different types of NNs.

If you want to learn Data Science, take a few of these statistics classes

A year ago, I was a numbers geek with no coding background.

started creating my own data science master’s degree using online courses shortly afterwards, after realizing it was a better fit for me than computer science.

For this guide, I spent 15+ hours trying to identify every online intro to statistics and probability course offered as of November 2016, extracting key bits of information from their syllabi and reviews, and compiling their ratings.

We made subjective syllabus judgment calls based on three factors: William Chen, a data scientist at Quora who has a master’s in Applied Mathematics from Harvard, wrote the following in this popular Quora answer to the question: “How do I learn statistics for data science?” Since a lot of a data scientist’s statistical work is carried out with code, getting familiar with the most popular tools is beneficial.

My favorite explanation of their differences is from Stony Brook University: They explain that “probability is primarily a theoretical branch of mathematics, which studies the consequences of mathematical definitions,” while “statistics is primarily an applied branch of mathematics, which tries to make sense of observations in the real world.” Statistics is generally regarded as one of the pillars of data science.

“Foundations of Data Analysis” includes two of the top reviewed statistics courses available with a weighted average rating of 4.48 out of 5 stars over 20 reviews.

Update (December 5, 2016): Our original second recommendation, UC Berkeley’s “Stat2x: Introduction to Statistics” series, closed their enrollment a few weeks after the release of this article.

…which contains the following five courses: This five-course specialization is based on Duke’s excellent Data Analysis and Statistical Inference course, which had a 4.82-star weighted average rating over 55 reviews.

The early reviews on the new individual courses, which have a 3.6-star weighted average rating over 5 reviews, should be taken with a grain of salt due to the small sample size.

Reviews suggest that the specialization is “well worth the money.” Each course has an estimated timeline of 4–5 weeks at 5–7 hours per week.

One prominent reviewer said the following about the original course that the specialization was based upon: Consider the above MIT course if you want a deeper dive into the world of probability.

We covered programming in the first article, and the remainder of the series will cover several other data science core competencies: the data science process, data visualization, and machine learning.

The best books on Computer Science for Data Scientists

First let’s talk about your journey to data and computer science.

It started off by going straight from high school to medical school in New Zealand, and realizing after a year that I didn’t want to be a doctor.

I spent some time in high school running databases for people, and also learned some PHP to create websites.

I was surprised by the gap between the very theoretical aspect of a computer science degree, and what I had actually experienced when I programmed.

For example, I took a class on algorithms, in which I learned that doubling the speed of a program wasn’t particularly interesting, which I thought was a ridiculous thing to say (ironically this is one of the few courses that is still useful to me today).

usually you’re assigned a position as a teaching assistant for a course in your department, but instead I got a consulting position where I helped students from other departments to do statistical analysis.

what was hard was getting data organised in a way that made sense, instead of constantly fighting to get it in the right form, and then visualising it to understand what was going on.

It became obvious that my repeated efforts to reshape and visualise data could be wrapped up in useful packages.

Your most famous contribution to data science, beyond the world of R, is what’s known as ‘tidy data,’

The idea of tidy data is to get people to store data in a consistent way, so that all of their tools can work with it efficiently, without having to wrangle and reshape it every time.

The basic concept is very simple: when you’re working with data, make sure that each column is a variable, so that each row is an observation.

It’s a rephrasing of the second and third normal forms in relational databases, which were my original programming background.

they sort of make sense if you’ve spent years working on databases, but most people simply won’t understand them.

Obviously your biggest contributions have been in R, but you’ve also worked on projects that try to bridge R and Python, and a lot of code you write behind the scenes uses C++.

If data scientists want to build solid computer science fundamentals for themselves, do you think that they should learn another general-purpose programming language beyond R?

As a programmer, I think it’s intellectually satisfying to learn about programming languages, and see how other languages think about instructions and data.

But many data science courses nowadays will try to teach Bash, SQL, Python and R all in the same course, which I think is bad idea;

“As a programmer, it’s intellectually satisfying to learn about programming languages, and how they think about instructions and data” “As a programmer, it’s intellectually satisfying to learn about programming languages, and how they think about instructions and data” Pragmatically, if you’re a data scientist, learning the basics of SQL is really important.

Then I think you’re better off specializing in one of these two and getting really good at it, rather than spreading yourself too thin and being mediocre at several languages.

it needs some, but, by and large, what it needs is engineers who know how to use programming languages and achieve a goal, rather than thinking about the atomic constituents of computer science.

They’re incredibly useful languages, but ones that computer scientists generally disdain, because they’re not theoretically pure or beautiful.

For example R does a lot of things that are very unusual among programming languages, and some of them could be considered mistakes, but a lot of them exist because R is trying to achieve a particular objective, and was thus designed following specific and sensible constraints.

You should not make that decision based on the technical merits of each language, but instead based on the community of people who use it and are trying to solve problems like yours.

The community of people using Scheme today is small, and somewhat esoteric, but there are interesting ideas to be learned in the language anyway.

When you identify a new problem, it helps you to come up with ideas, for example to use breadth-first search, or a binary tree, etc.

Similarly, is it important for data scientists to study those topics, not necessarily because they’ll need to use them often, but in order to acquire the intuition that something requiring n*log(n) computations is preferable to something requiring n² ones?

A lot of statistical theory is about measuring what happens to mathematical properties when some variable x goes to infinity, without thinking about what then happens to computational properties.

But if your algorithm needs n² computations, it doesn’t matter if x goes to infinity, because you’ll never be able to compute that.

It really helped me on my journey as a software engineer, to be able to write quality code day in and day out, and be confident that it’s going to work correctly.

It’s something that we never really talked about in my computer science education, and it’s certainly something that statisticians rarely think about.

The idea of unit tests is the same as double-entry bookkeeping: if you record everything in two places, the chances of you making a mistake in both places on the same item are very low.

So the easier it is to read your code and understand what’s going on, the easier it will be to add new features in the future.

and past-you doesn’t respond to emails” The third part would be to make sure that it’s fast enough, so that it doesn’t become a bottleneck.

It can be easy (and fun!) to get carried away with this, and obsess with writing code that’s exponentially faster.

The important thing is to make sure that nothing is overly slowing down execution, to the point of interrupting the flow of your analysis, or meaning that your program has to run overnight.

This book gave me the tools to analyze a text and identify the reasons why it doesn’t work, for example stating the topic of a paragraph only in the middle of it.

“Writing well and describing things well is very valuable to a good programmer, and even more to a data scientist” “Writing well and describing things well is very valuable to a good programmer, and even more to a data scientist” Knowing how to write clearly helps you to write code clearly, and also helps you writing good documentation and explain the intent of what you’re doing.

It doesn’t matter how wonderful your data analysis is, if you can’t explain to somebody else what you’ve done, why it makes sense, and what to take away from it.

You make this distinction between writing for computers and writing for humans, but one of the characteristics of your work has been to use elements of style and clarity to enhance the R language.

You often talk about the importance of semantics and grammar in code, for example in ggplot2, your data visualization package that’s based on the theory of grammar of graphics.

It’s also visible in the way that the tidyverse has completely changed the way data scientists write code in R, including the iconic ‘pipe’.

It talks about the idea of writing a small language inside another language, to express ideas in a specific domain, and the idea of ‘fluent’ interfaces, that you can read and write as if they were human language.

There have actually been attempts, for example by Apple, to write programming languages that were exactly like human language, which I think is a mistake because human language is terribly inefficient, and relies on things like tone and body language to clarify ambiguity.

It can take simple forms, like thinking of functions as verbs, and objects as nouns, so you can draw on the grammatical intuition that comes from human language.

Of course it raises many problems, the biggest one being that 75% of the resources available on sites like StackOverflow are in English, so the answers wouldn’t be universal anymore.

I’m very interested to see where that goes, and how useful it can be to aspiring data scientists everywhere, especially when R is quickly democratizing access to the subject, well beyond the academic world.

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