# AI News, Latest book “Beginning Data Science with Python and Jupyter”

- On 1. august 2018
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## Latest book “Beginning Data Science with Python and Jupyter”

You'll learn about some of the most commonly used libraries that are part of the Anaconda distribution, and then explore machine learning models with real datasets to give you the skills and exposure you need for the real world.

Who this book is for: This book is ideal for professionals with a variety of job descriptions across large range of industries, given the rising popularity and accessibility of data science.

- On 1. august 2018
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## Teach Yourself Data Science: the learning path I used to get an analytics job at Jet.com

It’s easier to motivate yourself to learn Python and machine learning when you’re fascinated by the practical applications.

had about a month before the class began, so I took as many classes around data science and machine learning as possible.

it covers convolutional neural networks (what we use for image or facial recognition software) extensively, which I read would be incredibly helpful for the self-driving car Nanodegree.

If you’re interested at all in using machine learning with images or video, you won’t find much better than this course.

After diving intensely into machine learning for a few months, it was helpful to take a step back and reinforce my understanding of practical analytics and data science principles.

While touching upon machine learning, it completely covers principles in analytics, data science, and statistics, particularly around different data mining techniques and practical scenarios to deploy them.

Of course, if you’re interested in pursuing a career in analytics or data science, you should always be honing old skills or adding new skills into your toolkit.

Even if you don’t have access to high-quality data at your company, there are plenty of open source datasets that you can play around and practice with.

Many of these groups have free tutorial or study sessions, and you’ll meet plenty of insanely smart people who can provide tips and tricks to accelerate your learnings.

- On 1. august 2018
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## How to Learn Python for Data Science in 2018 (Updated)

In this guide, we’ll cover how to learn Python for data science, including our favorite curriculum for self-study.

You see, data science is about problem solving, exploration, and extracting valuable information from data.

To do so effectively, you’ll need to wrangle datasets, train machine learning models, visualize results, and much more.

In fact, Forbes named it a top 10 technical skill in terms of job demand growth.

Python is one of the most widespread languages in the world, and it has a passionate community of users: It has an even more loyal following within the data science profession.

In addition, Python's vibrant data science community means you'll be able to find plenty of tutorials, code snippets, and people to commiserate with fixes to common bugs.

packages) for data analysis and machine learning, which drastically reduce the time it takes to produce results.

If you are completely new to programming, we recommend the excellent Automate the Boring Stuff with Python book, which has been released for free online under a creative commons license.

If you only need to brush up on Python syntax, then we recommend the following video, aptly named 'Learn Python in One Video:' Again, the goal of this step is not to learn everything about Python and programming.

You should be able to answer questions such as: If you'd like more practice with the core programming concepts, check out the following resources.

These are the action steps we recommend for efficiently picking up a new library: We don't recommend diving much deeper into a library right now because you'll likely forget most of what you've learned by the time you jump into projects.

These are the essential libraries you'll need: NumPy allows easy and efficient numeric computation, and many other data science libraries are built on top of it.

If you were to take the slow and traditional bottom-up approach, you might feel less overwhelmed, but it would have taken you 10 times as long to get here.

Proper guided projects should combine the best of both words - they should be representative of real-world data science and allow you to solidify your skills through a carefully planned learning curve.

Bootcamps usually conclude with a 'capstone project' that allows you to see all the moving pieces together, from start to finish.

- On 1. august 2018
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## Boost your data science skills. Learn linear algebra.

I’d like to introduce a series of blog posts and their corresponding Python Notebooks gathering notes on the Deep Learning Book from Ian Goodfellow, Yoshua Bengio, and Aaron Courville (2016).

The aim of these notebooks is to help beginners/advanced beginners to grasp linear algebra concepts underlying deep learning and machine learning.

Acquiring these skills can boost your ability to understand and apply various data science algorithms.

I liked this chapter because it gives a sense of what is most used in the domain of machine learning and deep learning.

It is thus a great syllabus for anyone who want to dive in deep learning and acquire the concepts of linear algebra useful to better understand deep learning algorithms.

The goal of this series is to provide content for beginners who wants to understand enough linear algebra to be confortable with machine learning and deep learning.

So I decided to produce code, examples and drawings on each part of this chapter in order to add steps that may not be obvious for beginners.

Finally, I think that coding is a great tool to experiment concretely these abstract mathematical notions.

Along with pen and paper, it adds a layer of what you can try to push your understanding through new horizons.

The type of representation I liked most by doing this series is the fact that you can see any matrix as linear transformation of the space.

And since the final goal is to use linear algebra concepts for data science it seems natural to continuously go between theory and code.

I found hugely useful to play and experiment with these notebooks in order to build my understanding of somewhat complicated theoretical concepts or notations.

The syllabus follow exactly the Deep Learning Book so you can find more details if you can’t understand one specific point while you are reading it.

Scalars, Vectors, Matrices and Tensors Light introduction to vectors, matrices, transpose and basic operations (addition of vectors of matrices).

Then we will go back to the matrix form of the system and consider what Gilbert Strang call the *row figure* (we are looking at the rows, that is to say multiple equations) and the *column figure* (looking at the columns, that is to say the linear combination of the coefficients).

Norms The norm of a vector is a function that takes a vector in input and outputs a positive value.

We will see that a matrix can be seen as a linear transformation and that applying a matrix on its eigenvectors gives new vectors with same direction.

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## How to get your first job in Data Science?

As I’ve written about several times, in real projects these 4 data coding skills are needed: It really depends on the company, which 2 or 3 they use.

From books you can get very focused, very detailed knowledge about online data analysis, statistics, data coding, etc… I highlighted 7 books I recommend — in my previous article here.

Data science online courses are fairly cheap ($10-$50) and they cover various topics from data coding to business intelligence.

If you want to try out, what it is like being a junior data scientist at a true-to-life startup, check out my new 6-week online data science course: The Junior Data Scientist’s First Month!

You can think about it as a small startup, but make sure that you keep focusing on the data science part of the project and you can just ignore the business part.

If you hit the wall with a coding problem — that can happen easily, when you start to learn a new data language — just use google and/or stackoverflow.

If you are lucky enough, you will find someone, who works in a Data Scientist role at a nice company and who can spend 1 hour weekly or biweekly with you and discuss or teach things.

Taking your first data science job at a multinational company might not fit in this idea, because people there are usually too busy with their things, so they won’t have time or/and motivation help you improving (of course, there are always exceptions).

Starting at a tiny startup as a first data person on the team is not a good idea either in your case, because these companies don’t have senior data guys to learn from.

It could help the company to improve _____ and eventually push the _____ KPIs.”) Hopefully this would land you a job interview, where you can chat a little bit about your pet projects, your cover letter suggestions, but it will be mostly about personality fit-check and most probably some basic skill-test.

If you want to try out, what it is like being a junior data scientist at a true-to-life startup, check out my new 6-week online data science course: The Junior Data Scientist’s First Month!

- On 1. august 2018
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## The Open Source Data Science Masters

...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.

The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.

We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.

While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.

Course Data Science with Open Source Tools Book $27 This is an introduction geared toward those with at least a minimum understanding of programming, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing). Out

- On 11. april 2021

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